Compare Data Federation Methods: Data can be federated via live queries, accelerated queries to the local data cache, or via file federation. Each option supports different use cases. Performance, data freshness, and credit consumption considerations vary.
Caching or Acceleration in Data Federation: Caching retrieves data from the external source and temporarily stores it in a data lake object in Data 360. When you enable acceleration while creating a data stream, you turn on caching. We recommend caching when accessing large data sets or when data changes frequently. You can refresh the cached data at your preferred frequency.
Share Data with Databricks Using Query Federation: Securely share Data 360 data with Databricks without migrating your data from Data 360 to Databricks by running federated queries using Zero Copy Data Federation. You can access your data lake, data model, and calculated insight objects in your workspace using the Unity Catalog in Databricks.
Share Data with Google BigQuery: You can access Data 360 objects as datasets in Google BigQuery by creating a data share. Zero copy integration allows data to be directly shared and queried without copying it into Google BigQuery. When data is accessible in Google BigQuery, you can work with harmonized and unified data from Data 360. Sharing data involves these high-level steps.
Share Data with Amazon SageMaker: Amazon SageMaker includes AWS Glue Data Catalog Views, AWS Lake Formation, and AWS engines to run queries using Amazon Redshift, Athena, or Elastic MapReduce (EMR). Share Data 360 objects, such as data lake objects, data model objects, and calculated insights objects, with an Amazon Redshift account using a data share.
Share Data with Snowflake (Legacy): To access Data 360 data, create a data share that contains Data 360 objects. You can access Data 360 objects as views in a Snowflake account. When data is available in Snowflake, you can work on harmonized and unified data.
Share Data with Snowflake V2: Share Data 360 data with Snowflake using the Snowflake V2 connector. Create a data share target, link data shares, and migrate legacy targets.
Act on Data in Data 360: Take action on segmented data on an activation target, like Meta or Marketing Clound Engagement, using activation. Share Data 360 objects with third-party partners using data shares. Monitor data events in near real-time in Data 360 with data actions.
Add Related Attributes to an Activation: Send more personalized communications by applying related attributes and filters. You can send related attributes to Marketing Cloud Engagement, Data Cloud, and cloud file storage platforms such as Amazon S3, SFTP, Microsoft Azure Blob Storage, and Google Cloud Storage (GCS).
Activate Data Stream Sync for Data 360: After Simple Start adds your Salesforce CRM metadata to Data 360, you can activate the data stream to ingest data. Simple Start ingests streams in an inactive state to prevent immediate credit consumption.
Activate Segments on Households: Activate segments built on the Household object in Data Cloud to target individuals within those households.
Activating Subsegments in a Data 360 Waterfall Segment: You can activate an individual subsegment in a waterfall segment or all subsegments at one time. Including all subsegments in a waterfall segment in a single data extension eliminates the need for multiple activations and additional post-activation stitching. When you activate the data extension, all the users are entered into the journey but directed down different paths based on their subsegments. It improves marketing campaign effectiveness and efficiency. After creating a waterfall segment, activate the segment to the Marketing Cloud Engagement activation target or any file storage target.
Activation and Activation Targets: Activation is the process that publishes a segment to your activation platforms, allowing you to take action on that data. An activation target is used to store authentication and authorization information for a given activation platform, like Marketing Cloud Engagement or Meta.
Ensuring Integrity in Marketing Cloud Engagement Activation: When multiple Marketing Cloud Engagement (MCE) orgs (EIDs) are involved in an Activation, unintended data mixing can occur. This happens when subscriber keys from different EIDs are brought into the same data space and are later activated to a specific EID, which can cause data integrity issues.
Contact Point and Activation Membership Filtering: Personalize marketing communications by having more control on who you’re reaching out to through contact points and the activation membership in Data 360. Use filters to control the volume and variety of data to use in marketing segments.
Create the Audience Segment in Data Cloud: The audience segment consists of the decision-makers who have a contract expiring within 4 months. Create a reminder email about the upcoming contract renewal.
Email a Contract Renewal Notification (B2B) Use Case: Cumulus Bank wants to email decision-makers at a specific business about an expiring contract. Before sending the email, the bank ensures that it has consent for each email address to receive emails about contract renewals.
Create the Audience Segment in Data Cloud: The audience segment consists of customers who have a high engagement rate with email campaigns. To create an automated email about new services, follow these steps in Data Cloud to create an audience segment.
Email a New Service Promo Notification (B2C) Use Case: Cumulus Bank wants to send an email message to customers about new services. They want to ensure that only customers who have provided consent and opted in to receive advertisements are the ones receiving the emails.
Create the Audience Segment in Data Cloud: To create an SMS message to notify California-based customers about their upcoming policy changes, follow these steps in Data Cloud to create an audience segment.
Filter the Segment and Publish to Marketing Cloud Engagement: After you create a segment, you can narrow the audience by adding filters. For this use case, you filter by consent status and whether the person has provided their phone number.
Text a Policy Change SMS Notification (B2C) Use Case: Cumulus Bank wants to send a SMS message to notify their customers living in California to check their account about upcoming policy changes. They want to ensure they’re including all phone contact points available for their customers that have provided consent.
Activation for Data 360 Segments: Activation is the process that publishes a segment to activation platforms in Data 360. Before you activate your segments, it's important to review some activation concepts.
Add Filters to the Activation Membership: Data 360 activation membership filters allows you to send more personalized communications by adding custom filters to the activation membership. Add filters to ensure that only the intended audiences are being targeted.
Activation Filter Operators: Use filter operators to refine the data payload sent to your activation targets. When defining activation filters, you can apply these operators to specific attributes to ensure only the most relevant data triggers an update in your downstream systems.
Activation Refresh Types: When creating an activation, choose from two refresh types: full or incremental. A full refresh updates all records in the segment during activation. An incremental refresh publishes only the added, updated, and deleted records since the last successful refresh.
Activation Targets in Data 360: Depending on your use case, data can be activated to various targets, from file storage, external activation, or to other data model objects (DMOs) in Data 360. Once you know where you want to act on the segmented data from Data 360, create activation targets for each platform.
Standardize Activations by Using Activation Templates: Use activation templates to define and save reusable activation configurations. These configurations include settings for contact point selection, source priority, filters, enrichment data, and membership filtering. Use these templates to create activations with consistent, predefined settings across all supported activation platforms.
ISV Ecosystem Activation Using the Activation Toolkit: The Data 360 Activation Toolkit allows Independent Software Vendors (ISV) to build activation platforms on AppExchange. These platforms let subscriber organizations install and use ISV activation services. It lets them to match Data 360 data with destination data payloads and use advanced features like multi-key matching, contact point filtering, and supporting additional DMOs in Activation Membership.
Segmentation Filters vs. Activation Filters: Segmentation and activation use different filters for different purposes. Apply filters to a segment to narrow your audience. Use activation filters to narrow down relevant values with related attributes that personalize messages.
Activation Record Home in Data 360: Activation record home in Data 360 shows activation outcomes in one place. The activation record home includes activation details, linked activation-triggered flows, and activation history. From this page, you can validate configuration, open related flows, and monitor publish results.
Conversion API (CAPI) Activations to Ad Audiences Partner Platforms: Conversion API (CAPI) activation targets send online and offline conversion events from a data model object (DMO) to supported advertising platforms. CAPI uses streaming DMO activation instead of audience segment activation.
Identity Boost for Ad Audiences Partners: Expand the addressable audience for your ad campaigns by enriching your first-party data with contact points from a certified identity provider. Identity Boost fills the coverage gaps in your first-party data so that you can reach more of your audiences in Ad Audiences partner activations.
Audience Insights from Ad Audiences Partner Platforms: Capture detailed customer insights from external platforms in Data 360. This capability provides demographic data and affinity groups based on activated segments. The insights are stored in the Insights DMO, which also tracks audience match count and updates every 48 hours.
Advertise with Data 360: Use Data 360 to implement advertising strategies with first-party data. Establish a reliable source of truth in Data 360, and connect with external advertising platforms to deliver highly targeted, personalized experiences across every channel.
Add a Self-Managed Private Network Route for Databricks on Azure: Connect your Databricks data source and Data 360 tenant with a dedicated network connection using Private Connect for Data 360. Create a single connection to a specific instance regardless of how many Data 360 instances you have. Before you set up a Databricks federation connection or data share, establish a network route.
Add a Self-Managed Private Network Route for Snowflake on Azure: Connect your Snowflake instance and Data 360 tenant to a dedicated network route with Private Connect for Data 360. Create a single route to a specific instance even if you have multiple Data 360 instances. Before you set up a Snowflake federation connection or data share, establish a Private Network Route (PNR).
Add a Salesforce-Managed Private Network Route for Snowflake on Azure: Connect Data 360 to your Snowflake on Azure data source with a Salesforce-managed private network connection by using Private Connect Multicloud for Data 360. Salesforce provisions and maintains the cross-cloud connection between Data 360 and Snowflake on your behalf. No configuration is needed on AWS or Azure.
Add a Salesforce-Managed Private Network Route for Databricks on Azure: Connect Data 360 to your Azure Databricks workspace by using Private Connect Multicloud for a Salesforce-managed private network connection. Salesforce provisions and maintains the cross-cloud connection on your behalf, without needing you to configure Azure. In general, you authorize Salesforce private endpoints from your Azure environment as follows: two endpoints for a private workspace storage and one endpoint for public workspace storage.
Add a Formula Field: After you create your schema in Data 360, you can add formula fields.
Add Calculated Insights to a Data Graph: In Data 360, you can include calculated insights in a data graph as long as they’re based on a data model object (DMO) that’s also included in the data graph. The DMO’s primary key field must be configured as a dimension on the calculated insight object (CIO). You can add a CIO only at the root of a data graph. You can’t add a CIO as a child node under another object.
Add Data to a Data Space Using Data Lake Objects: You can associate a data lake object (DLO) to a data space with or without filters when you bring in data from any source to Data Cloud. You can add data by assigning the DLOs to specific data spaces. You can add a DLO to more than one data space.
Add Filters to a Data Lake Object: Use filters on the data lake object (DLO) field attributes to view region-specific or brand-specific data. Add the same DLO to multiple data spaces with different filters to segregate data.
Add an IP Networking Policy: If you encounter an authentication error when setting up a Snowflake data share target, make sure to include Data Cloud IP addresses in Snowflake. Create an IP networking policy in Snowflake that lists the allowed Data Cloud IP addresses.
Add a Private Network Route for Snowflake on Amazon Web Services (AWS): Connect your Snowflake data source and Data 360 tenant with a dedicated network connection by using Private Connect for Data 360. Create a single connection to a specific instance, even if you have multiple Data 360 instances. Before you set up a Snowflake federation connection or data share, create a Private Network Route (PNR).
Add a Source Field: After your schema is created in Data 360, you can add source fields to the data stream as needed. When you add a field to an object in a connected Salesforce CRM org, make sure that the correct permissions are set for the field.
Add Object Permissions to Custom Permission Sets for Data 360: Object permissions for new Data Cloud objects are added to all standard Data Cloud permission sets when the new objects is added. However, object permissions for new objects but must be manually added to custom permission sets. Use these object permissions to update any custom Data Cloud permission sets that were created before the new object was added to Data Cloud.
Configure a Key Qualifier Field: After creating a data stream, add key qualifiers to your data lake object (DLO) fields to create fully qualified keys (FQK). Configure key qualifiers for all DLO fields that contain either a primary or foreign key. You can add up to 20 active key qualifiers. A key qualifier is active when it's used for a DLO field. Data 360 automatically adds and removes key qualifiers to and from data model object (DMO) fields. You can't edit these system fields.
Add or Remove Root Key Indicators in DMO Metadata: Root Key is the primary key of the primary data model object (DMO) of the data graph. In a data graph, a root key is the main identifier. It is also the initial access point for data retrieval. This is the primary key of the primary DMO.
Use Vector Filters in Data 360 Segments (Beta): Vector filters reduce reliance on exact keyword matches and manual data organization, allowing you to create accurate segments efficiently. This feature uses NLP algorithms to sort through unstructured data in segment creation. It supports filtering both structured and unstructured data within a single interface and a segment.
General Set Up Tasks in Data 360: In addition to provisioning and connecting data sources, learn about additional tasks completed in Data Cloud Setup.
Aggregation: The first step in defining your filter criteria is to decide how you want to quantify the results, such as an average or sum, also known as aggregation. For example, you use different criteria to find five sales order products as compared to five sales orders. Use the container object to aggregate your data. The Data Model Object (DMO) of a related attribute in a container becomes the container object.
Get Predictions in Apex: Use Apex code to get predictions, top factors, and prescriptions from a no-code model in Model Builder.
Transform Data with Predictions and Prescriptions: Enrich a data model object (DMO) with outputs. Use a data transform to add predictive and prescriptive intelligence to your data. Run data transformations and save the outputs, such as prediction scores, on a DMO. Use each row of the output to create derived columns and run calculations. You can also build data visualizations using output data saved on the DMO in other Salesforce applications, such as in Tableau, CRM Analytics, and app templates.
Connect a Model: Connect predictive and generative AI models hosted outside Salesforce and use them with Salesforce data to predict future outcomes. Select your data source, define your predictive criteria, and train your model to reveal predictive inferences and insights using AI and machine learning.
Connect a Databricks Model: To consume predictions in Salesforce, connect your Databricks model with Data 360 and define the prediction criteria for your use case.
Model Endpoint Formats: To connect a predictive model and get inferences, you must use the relevant endpoint request and response formats.
Connect a Vertex AI Model: To consume predictions from a Vertex AI model in Salesforce, connect it with Data 360 and define the prediction criteria for your use case.
Set Up Your Data in Databricks: Access Salesforce data in your Databricks notebook using a Python connector. The user-friendly interface lets you quickly execute queries against data in Data 360 so that you can extract the data that you need into Python.
Set Up Your Data in Vertex AI: Access Salesforce data in a Google Cloud Vertex AI notebook using a Python connector. The user-friendly interface lets you quickly query your data in Data 360 so you can extract the data that you need into Python.
Set Up an Endpoint in Databricks: Control the access to your data by setting up an endpoint connection between Databricks and Data 360.
Use Databricks Models: You can build, train, and deploy custom machine learning models with Databricks and data in Salesforce. After registering a model in Databricks, define your prediction criteria and connect the model with Data 360 to get predictions and insights for your use case. Use the insights to enrich customer profiles, create segments, and personalize experiences across various channels.
Use Google Cloud Vertex AI Models: You can build, train, and deploy sophisticated custom machine learning (ML) models with Google Cloud Vertex AI and data in Salesforce. After registering a model in Vertex AI, define your prediction criteria and connect the model with Data 360 to get predictions and insights for your use case. Use the insights to enrich customer profiles, create segments, and customize experiences across various channels.
Get Trusted AI Responses with Retriever Citations: Citations link AI-generated responses to the source content relevant to each response. Citations allow users to compare the LLM response with the source data to verify the validity of the response and identify any potential inaccuracies or hallucinations. Enable citations in AI Models (formerly Einstein Studio) at the individual retriever level to give access to citations to solution components that call the retriever, such as a prompt template.
Create, Connect, and Activate Models: Create your own predictive models with clicks, not code. Establish connections to bring in generative and predictive AI from models outside of Salesforce. Then, activate models to use the inferences and insights revealed in your data.
Create a Custom SQL Retriever: Use a custom SQL retriever to query Data 360 objects using a SQL statement. To build your query, select an object, write a SQL query, specify parameter values, and configure citations. Review and run your query to validate accurate results before you save and activate the retriever.
Actionable Variables and Prescriptions: In AI Models (formerly Einstein Studio) Model Builder, which uses data from Data 360, actionable variables are fields in a model created from scratch (AI-created) that you can change to influence a predictive outcome. In constrast to other fields that you can't change, such as a customer's location, gender, and age, actionable variables can be changed. These variables are important because they provide suggested actions (prescriptions) that users can take for more succesful outcomes.
Binary Classification Metrics: Metrics for binary classification help evaluate the performance of a model that categorizes data into two classes.
Address Data Issues: Handle common issues that you can encounter when you prepare data for model building and after you train a model.
Create Predictive AI Models From Scratch: Create models to analyze your data and get AI powered predictions based on machine learning. Learn about supported use cases and ethical model building. Then, follow the steps to create a model and evaluate model quality.
Create a Model from Scratch: Create a model that uses historical data to predict future outcomes. Select and structure your data source and define the desired outcome. Then train the model to reveal predictive inferences and insights by using AI, machine learning, and statistical analysis.
Design Ethical AI Models: Salesforce offers guidance to help you practice ethical use of artificial intelligence (AI) in your business.
Evaluate Model Quality: Use training metrics to evaluate the model’s ability to predict an outcome and determine whether it’s ready to activate. Training metrics provide information about how effectively the model understands patterns and relationships within the training data, and indicates its predictive efficacy.
Manage Quality Alerts: Model Builder displays quality alerts when it detects issues in your model's training data, quality metrics, or during cross-validation. The alerts can surface in the model setup, details, and inspector.
Use Real-Time Predictions to Drive Personalized Recommendations: Predictive models in AI Models (formerly Einstein Studio) can generate real-time predictions to inform personalized recommendations in Salesforce Personalization. The integration enables the delivery of customized experiences, which can boost engagement. Predict the customer lifetime value annual spend, or likeliood to purchase and use your model's predictions in a Personalization Decision targeting rule to serve up different content recommendation strategies.
Area Under the Curve (AUC) Quality Alert: Area under the curve (AUC) is a performance metric for binary classification. It measures the ability of a model to distinguish between two classes: positive and negative (true and false, yes and no, and so on). The AUC is derived from the receiver operating characteristic (ROC) curve, which plots the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.
Multicollinearity Alert: Multicollinearity indicates that two or more variables in a dataset are highly correlated (for example, “city” and “postal code”). Because these variables can have a duplicate impact on the outcome, high collinearity can lead to overfitting—the model performs well on training data but could perform poorly on data that the model hasn't been exposed to yet.
Cross-Validation Failure Alert: Cross-validation evaluates how well a model generalizes to unseen data by splitting the dataset into multiple parts, training the model on some parts, and validating it on others. Failure occurs when training data isn’t large enough, uses too few examples, or has an invalid format, missing values, or outliers.
Data Leakage Alert: Data leakage occurs when the model's training data contains the information you’re trying to predict. Leakage results in models that score optimistically high in training but perform less accurately on live data. It can mislead the model into learning patterns that are unavailable in practice, thereby compromising predictions.
High Frequency Values Alert: High frequency values indicate that most values in a variable are in the same category. It indicates that the variable provides little predictive value and can limit its contribution to the model.
Imbalanced Distribution Alert: Imbalanced distribution indicates a disproportionate ratio of observations in each class of the training data.
Outliers Alert: Outliers are data points that significantly differ from the majority in a dataset. The alert indicates that at least one outlier was detected and that there's a presence of uncommonly small or large numbers. Outliers can skew model performance, reduce accuracy, and lead to incorrect forecasts.
R2 Quality Alert: R2 (R-squared), or the coefficient of determination, is a performance metric that evaluates how well a regression model fits the data.
Regression Metrics: Metrics for regressions help evaluate the performance of a model that predicts numerical values for continuous data.
Set Actionable Variables: In Model Builder, actionable variables are fields that can you can change to influence a model's predicted outcome. Setting actionable variables enables the model to provide prescriptions (suggested actions that a user can take to improve an outcome).
Apply Transformations to Your Data: Transform modeling data to improve the reliability, accuracy, and explainability of predictions. For models created from scratch, Model Builder automatically transforms unstructured text and replaces missing data. You can also manually transform variables in binary or regression models.
Use Predictions in Flow Builder: Create automation using outputs from an activated model for decision-making. Predictive models that are activated in AI Models (formerly Einstein Studio) are surfaced in Flow as actions. You can use all flow types and choose a flow based on your goal. For example, if a business process requires user input or you want to display specific data to users, use a screen flow. If you want to automate possible actions to take after a record is created, updated, or deleted, use a record-triggered flow.
Bring Your Own Large Language Model: The Bring Your Own Large Language Model (BYOLLM) capability enables you to add a foundation model hosted on an external platform and connect it with AI Models (formerly Einstein Studio). You can use the foundation model to create a model configuration to power your generative AI. Evaluate the model’s response to your prompts in Model Playground with different settings before you deploy the model to production.
Create a Model Configuration: After you add a foundation model, you can create an associated model configuration for a specific use case.
Edit a Model Configuration: Try different prompts and adjust your hyperparameters to evaluate how well the model responds to your prompts. However, if your model is already associated with prompts, create a new model instead. Updating model settings can affect the performance of associated prompts.
Monitor Model Activity: View your model inference use and errors in a dashboard. Use important indicators to help you track, adjust, and improve model performance.
Test Your Prompt: Before you put a model into production, test it using prompts in Model Playground to evaluate the generated responses based on your model’s hyperparameter settings.
Add a Foundation Model: Adding a foundation model in AI Models (formerly Einstein Studio) establishes an endpoint connection between the external model provider and Salesforce. After you connect the model, create a new model configuration and evaluate your model’s response to custom prompts.
Data Masking: Data masking settings in your Salesforce org and Model Playground determine the privacy of sensitive information and how that data is surfaced in a prompt response. Org-level settings are enabled by default.
Edit or Delete a Foundation Model: You can edit most of your foundation model settings as long as the model isn’t a source for other models. If it’s used as a source, you must first delete all associated model configurations. You can delete a foundation model if it’s no longer needed.
Create a Prompt Template: If you're satisfied with your model's response to one or more test prompts in Model Playground, create your prompt templates.
Configure and Test a Model in Model Playground: Create, edit, and evaluate your model configuration in Model Playground without the need for setup or specialized technical expertise. Model Playground uses a trust layer, which ensures data privacy and security.
Glossary for Predictive AI: Familiarize yourself with terminology that is commonly associated with predictive AI in AI Models (formerly Einstein Studio).
Set Up AI Models: To use AI Model Builder (formerly Einstein Studio Model Builder), your org must be provisioned with an applicable license, and users must be assigned the appropriate permission.
Activate Your Model: After saving your predictive model settings, the model is ready for activation. To consume predictions, you must first activate the model.
Manage Models: After creating a predictive model, you can edit to update your settings or delete it if it’s no longer needed.
Manage Large Language Model (LLM) Access by Hiding Configurations: Hide an LLM configuration when you want users in Prompt Builder to change to a different model in their prompt templates. They can select a newer LLM version or a different LLM, possibly from a different provider. You can suspend access to a new LLM until you've completed testing. Optionally, you can later show a hidden LLM configuration to make it visible for selection.
Use Predict Jobs: A predict job enables an active model to write predictive outputs to a data model object (DMO). Create a streaming or batch job in Model Builder and use the model's outputs in Salesforce workflows.
Predictive AI: Predictive AI uses models built with machine learning (ML) and your historical data to predict business outcomes and recommend ways to improve predicted outcomes.
Algorithms and Loss Functions: Predictive models analyze historical data to discover patterns and relationships that predict future outcomes. Model Builder provides algorithms for binary, multiclass, and regression models with configurable training settings. When you train a predictive model with a numeric outcome, loss functions measure how far predicted values are from actual values. During training, the model minimizes this loss to improve overall prediction accuracy. The selected loss function determines the types of prediction errors that the model prioritizes.These settings enable you to generate predictions that drive business decisions and surface customer insights.
Drive Better Decisions with Predictive and Generative AI: The Predictive Model reference action in Agentforce enables an AI agent to invoke a predictive model created in AI Models (formerly Einstein Studio). Use the action to generate on-demand, data-driven predictions, interpret the results, and provide recommendations for how to improve an outcome. The integration of predictive and generative AI improves the interpretability of results for taking the best actions.
Predictive Models in Companion Orgs: Access predictive models trained and activated in a home org from your Data Cloud One companion org. Data Cloud One supports multiorg access, which is ideal when a business is spread across different teams, countries, or brands. Model governance in the home-organization also simplifies model management and enables you to scale predictive AI across your entire ecosystem.
Topic Classification: Use topic classification in Data 360 to organize unstructured text such as reviews, case comments, or survey responses, into meaningful categories. To evaluate records for classification, the model applies a trained language model to a selected field. For each record, the model assigns a topic label and a confidence score. Data 360 adds these outputs to your dataset. Apply topic classification in reporting and queries.
Use Clustering on Structured Data: Use clustering models in Data 360 to automatically group data model object (DMO) records by shared characteristics. Run a predict job or batch data transform to assign new records to saved clusters, and use the results across Salesforce.
Clustering: Use clustering in Data 360 to uncover hidden patterns and natural groupings in your data.
Configure a Propensity to Buy Template (Beta): Configure the Propensity to Buy (beta) template to automatically create the data transforms, output data model objects, and predictive model required to generate propensity scores. Query the scores to assess a customer's likelihood of making a purchase within your specified prediction window.
Drift Monitoring (Beta): Use Drift Monitoring in Data 360 to track predictive model degradation, which occurs when the distribution of model predictions changes significantly over time. Drift indicates shifts in customer behavior, business conditions, or underlying data patterns.
Forecasting: Use forecasting in Data 360 to predict future values from historical data. Forecasting models automatically identify trends and seasonal patterns to generate predictions without requiring data science expertise.
Use Single Variate Forecasting: Predict future values based on historical patterns in a single variable by using the Holt-Winters algorithm. For example, use a data transform to forecast sales by account based on opportunity data.
Model Lineage in AI Models: Model lineage provides a traceable history of an AI model's lifecycle, from training data, versioning, deployment, and its use with live data. Data 360 integrates this capability in AI Models (formerly Einstein Studio) and Governance.
Predictive Model Monitoring: Maintain the accuracy, stability, and reliability of your deployed predictive models with model monitoring in AI Models (formerly Einstein Studio). This ongoing process provides in-depth insights into model's performance and identifies data quality or connectivity issues. Without consistent monitoring in Data 360, model performance can decline and lead to inaccurate predictions, unexpected outcomes, and reduced confidence in your results.
Improve Predictive Models: Model quality is a critical success factor in predictive AI solutions. AI Models (formerly Einstein Studio) supports continuous, iterative improvement for predictive models. Measure model quality in production over time. Use quality alerts to identify and address areas for improvement. Experiment with new model versions. Efforts to improve model quality result in better business outcomes.
Use the Predictive Model Agent Reference Action to Enhance Predictions: For an AI agent to provide and interpret outputs from a predictive model, you must create a custom agent action in your org. Let's look at how Agentforce can enhance predictions from a Predicted Attrition model that was created in Model Builder.
Sentiment Analysis: Use sentiment analysis in Data 360 to evaluate unstructured text and learn how customers feel about your brand, product, or service. For each record, such as a customer review or survey response, the model assigns a positive, negative, or neutral label and a confidence score. To filter audiences, build dashboards, and trigger automations, apply the model outputs.
Propensity to Buy Template (Beta): The Propensity to Buy template establishes a starting point for predicting which customer will buy a product or product category within a particular time frame. The template preconfigures model details, prediction inputs, and selects an algorithm. To align the model with your data, customize default variables and set a prediction window—the number of days used to calculate the purchase likelihood. After model training, Model Builder generates purchase likelihood scores on your records so you can identify high-value customer segments and drive targeted marketing campaigns.
Predictive Model Templates: Build predictive models quickly by using preconfigured templates in Model Builder. Templates provide a starting point, including model details, variables, and recommended algorithms that you configure before model training.
Access Model Runtime Settings: The new default runtime is the foundation for building all new predictive models in the AI Models tab in Data 360. This enhancement replaces the legacy runtime as the long-term platform for expanded predictive modeling capabilities.
Prepare Your Training Data: Effective data preparation is the key to getting great results with your predictive model. Harness the power of data integration in Data 360. Load and transform data from one or more data sources into a data model object (DMO) with optimized training data. You can pull data from Salesforce and external sources. You can also use third-party tools and utilities to further expedite data cleansing and wrangling tasks.
Use a Record-Triggered Flow to Automate Actions when a Prediction Changes: Automatically perform actions when a record is created, updated, or deleted. Let's look at how to use a flow to create actions when a prediction score on a case updates and it exceeds the threshold, indicating a case escalation.
Get Predictions in REST APIs: Use the Salesforce Data 360 Connect REST API to programmatically interact with predictive models. Get predictions, top factors, and prescriptions using the predict endpoint.
Retrieve Data: Use a retriever to search and return relevant data from a search index. Create and customize retrievers in AI Models (formerly Einstein Studio) for Retrieval Augmented Generation (RAG) in Data 360. Retrievers augment prompt templates by providing relevant, specialized grounding information for prompts.
Create an Individual Retriever: To improve the relevancy of the content returned to a prompt for a particular use case, create a custom individual retriever in AI Models (formerly Einstein Studio). To customize the retriever, select its search index, define filters, and specify what information it returns.
Retriever Support in Data Cloud One: Learn about retriever capabilities and limitations when you work with Data Cloud One home and companion orgs.
Create an Ensemble Retriever: Use ensemble retrievers to get the most relevant information from multiple searches or data sources. In AI Models (formerly Einstein Studio), an ensemble retriever is a collection of individual retrievers. When you run an ensemble retriever, it runs all the individual retrievers, combines their results into a single list, reranks the list according to relevance to the search request, and returns just the most relevant information to the prompt or agent.
Customize, Test, and Validate Retrievers with Retriever Playground: Fine-tune and validate retrieval logic for retrieval-augmented generation (RAG) systems by using retriever playground, reducing deployment risks, and verifying accurate AI outputs. Use AI Models (formerly Einstein Studio) retriever playground to configure pre-filters, select test data, and adjust result parameters to customize retrieval logic for domain-specific use cases, such as retrieving complaince documents or knowledge articles.
Billing Considerations for Testing Retrievers: When you test retrievers by querying search indexes, your data is processed in Data 360. The primary usage type for retriever testing is Data Queries. Use of retriever playground impacts the consumption of credits used for billing in the following usage types.
Test an Individual Retriever: Test a retriever configuration to ensure accurate responses from your AI agents. Save retriever configurations as new versions of the retriever in AI Models (formerly Einstein Studio). Activate them for use in agents with prompt templates, Flow, and apps.
Test an Ensemble Retriever: Test an ensemble retriever configuration in the AI Models retriever playground before you use it in prompt templates or AI agents. Add or remove retrievers to an ensemble, configure test data, and result parameters to verify the retrieval logic.
Retriever Metrics and Results: Review retriever metrics and the retrieved data chunks to understand the results of your test. Modify the retriever configuration to try different configurations and expected source data to get an optimal response. Save your configuration as a new version of the retriever.
Manage Retrievers: Create and customize retrievers for Retrieval Augmented Generation (RAG) in Data 360. To see the list of retrievers, navigate to the Retrievers tab in AI Models (formerly Einstein Studio). To learn about a retriever, select View Details from the dropdown menu. Each time you edit a custom retriever and save the changes, a new version is created. You can create multiple versions, but you can have only one version active at a time.
Activate or Deactivate a Retriever Version: Activate a retriever version in AI Models (formerly Einstein Studio) and use it in prompt templates. Only one retriever version can be active at a time. Activating a new version deactivates the current one. Deactivate a version only if no prompt templates depend on it.
Edit or Delete a Retriever Version: Edit retriever versions in AI Models (formerly Einstein Studio) to create versions for testing different configurations and responses within a prompt. You can edit only custom individual retrievers. If no prompt templates depend on a retriever, you can delete it.
Salesforce-Enabled Large Language Models: Use a Salesforce-enabled foundation model to power your generative AI experiences with Prompt Builder. Some models use geo-aware request routing to accommodate data residency requirements. As a result, the provider can host their model in multiple regions while working within the boundaries of the Einstein Trust Layer.
Use a Screen Flow to Enrich Records with Predictions: Create a screen flow to get records from Salesforce objects and add a prediction to a Lightning page. For example, you can add a lead conversion prediction score to a lead record.
Billing Considerations for Predictive AI: Predictive models in AI Models (formerly Einstein Studio) use Data 360 to ingest, query, and store data, and to create inferences. Using Data 360 services can impact the consumption of credits used for billing in these usage types.
Use AI Models: AI Models is your home base for AI in Data 360. Augment your business intelligence with AI to identify, surface, and visualize inferences in yourData 360 data. AI is integrated into your Data 360 environment so that you can quickly operationalize generative models, predictions, and improvements with clicks, not code.
Get Predictions, Prescriptions, and Top Predictors: Put your predictive AI models into action. After a model is activated, use it to get inferences—predictions, prescriptions, and top predictors—from your Salesforce workflows. Get inferences on demand, in batches, and in streams. Use them in flows, prediction jobs, batch data transforms, agents, and prompt templates. Get them programmatically in Apex and REST applications.
Allowlisting: An allowlist defines things that a system trusts. It’s used to control allowed traffic and rejects undefined traffic and can be applied at different layers of the OSI Model. Here’s how the allowlist works at the application and network layers for Data 360.
Amazon Ads and Amazon Marketing Cloud Insights in Activation: Delve into your customers’ unique characteristics, interests, and behaviors with Amazon Marketing Cloud (AMC) audience insights reporting. When you activate a segment to Amazon Ads AMC, an audience report is generated. The report provides demographic insights on customers who are actively researching products and their affinity groups based on lifestyle, long-term buying habits, and interests.
Analyze Sales Data with Calculated Insights: Calculate multidimensional metrics on your data within Data Cloud. You can create metrics at the profile, segment, and population levels. For example, evaluate performance at the channel level or use product metrics to understand purchasing and browsing behavior.
Analyze Service Data with Calculated Insights: Calculate multidimensional metrics on your data within Data Cloud. You can create metrics at the profile, segment, and population levels. For example, evaluate performance at the channel level or use product metrics to understand purchasing and browsing behavior.
Create an API Activation in Data 360: API activation expands where and how you can initiate activations. API activation gives you the flexibility to use a Data 360 activation from Flow by using an API call.
Refine Segments with Grouping, Ranking, and Limiting: In Data 360 Segmentation, after setting your initial criteria in the Include and Exclude tabs, go to the Rank and Limit tab to further refine your segment. The first container is automatically created and locked to your segment's primary entity. This container defines your innermost filtering rule.
Propagate Tags to Downstream Objects and Fields: In Data 360, apply tags automatically from primary data objects to their related downstream objects to ensure consistent policy enforcement.
Approximate Segment Population: When building a segment in Data 360, use the Approximate Segment Population field to quickly check that the segment count meets your expectations. The approximate count provides an estimated segment population, represented as a range of possible values with 95% confidence. When the segment population is small, or when the record count for Segment On is low, the actual population can differ from the estimated range. You can then adjust your segment rules and save your segment to get the final segment population. To use this feature, enable Approximate Segment Population in Feature Manager.
Classification Tags in Data 360: Classifications provide additional context and meaning within a taxonomy. They allow you to categorize data based on predefined criteria such as sensitivity or intended use, making it easier to manage, protect, and use data appropriately.
Simplify Segment Filters with Shortcuts: Save commonly used segment filter attributes with their data model paths as shortcuts to reuse them across segments and activations. Instead of navigating the attribute library to find the right data model object (DMO), path, and attribute each time, you can select a saved shortcut that has the required configuration.
Audience Data Model Object: Understand and analyze your activated audience over time using audience data model objects (DMOs). There are two types of related DMOs, audience and audience latest DMOs.
Audience DMO Schema Attributes: In Data Cloud, audience DMOs are created with a specific naming convention. Look for: Activation Audience - <Activation Target name>.
Audience Insights Data Model Object in Data Cloud: The Audience Insights data model object (DMO) provides insights into the audiences that you’ve activated on Google advertising platforms with the Ad Audiences connector. You can retrieve records from multiple activated audiences and delve into their similarities and differences. The DMO is updated every 48 hours and stores audience insights records for the previous 90 days, enabling you to track the long-term impact of a campaign and how your audiences evolve.
Audience Match Count for Advertising Activations: Match Count is the approximate number of users from your audience segments who have been successfully matched on an external advertising partner platform (like Facebook, Google Ads, or TikTok). Monitor the match count of your segment list activations to understand the reach and size of the audience you are activating.
Create a Data 360 Object from a Segment: Activations to the Data 360 activation target use segments and attributes to create objects. Each Data 360 org can have one Data 360 activation target, and each activation target can have multiple objects. The same object can be selected for multiple segments.
Authoring Methods for Insights: Design and implement insights using visual building tools or direct SQL syntax to meet specific analytical requirements. Authors can transform raw data by joining, aggregating, or filtering datasets through node-based workflows and streaming functions. With these methods you can clone existing logic, save drafts, and apply complex SQL operators to generate real-time or calculated metrics. You gain the flexibility to build sophisticated data models from scratch or use pre-configured packages for faster deployment.
Data 360 Auto Mapping Model Card: The Auto Mapping model automatically maps data lake object (DLO) source fields to data model object (DMO) target fields. The model reduces the amount of manual labor and time required to configure new data streams in Data 360.
Autopopulated Join Keys in Data 360: When you join two or more objects in Insight Builder, the join nodes and join keys are autopopulated based on their relationships in Data 360. A key join is a simple way to join link tables. Autopopulated join nodes make it easy to connect engagement data with a unified profile and override autosuggested joins.
Automapping: Automapping in Data 360 maps data lake object (DLO) fields to the correct data model object (DMO) fields. When you choose a specific DMO during field mapping, Data 360 calculates and applies the field mappings. You can then make any necessary changes and save the mappings.
Create an Activation for B2C Commerce Target: After you set the required data field mappings and create a segment in Data Cloud, create a B2C Commerce activation to publish the segment.
Sales and Order Filters: Here are some examples of common purchase order and service use cases.
Create a Batch DMO Activation in Data 360: Create a batch DMO activation to export an entire data model object (DMO). You can configure your activation to filter records, add additional attributes, and rank the audience before you publish.
Access Batch Data Transforms: Create and access batch data transforms using the visual in-app builder or custom code.
Additional Functions for Formulas: To build logic into formulas, use these functions with the text, number, and datetime data types.
Aggregate Node: Use an Aggregate node to roll up data. You can aggregate to a higher level of granularity or roll up hierarchical data.
Aggregate Node: Hierarchical Aggregation: Use Hierarchical Aggregation to sum measures across multilevel data. For example, you can roll up revenues, assets, or liabilities for companies and their subsidiaries. Or, you can roll salesperson data up the management chain to see aggregates by team, region, area, and beyond. Hierarchical Aggregation requires rows with an ID and a parent ID. Many rows can have the same parent ID, or the parent ID can be empty. Some rows can even share an ID, such as a record with multiple parents. You can also use the optional roll up percentage to apply percentage splits to allocate portions to different parents, such as commission among members of the sales team.
Aggregate Node: Roll Up Data to a Higher Level: Use an Aggregate node to roll up data to a higher granularity. You can aggregate using: unique, sum, average, count, maximum, and minimum. You can group by any dimension or date column. You can also pivot dimensions as columns. For example, you can group opportunities by account to get account-level details, like total opportunity amount, average number of cases, and the average days to close an opportunity for each account. You can then pivot on account type to analyze each combination of account and account type.
Best Practices for Batch Data Transforms: Keep your Batch Data Transforms fast and reliable by filtering early, keeping data narrow, and avoiding common design pitfalls like exploding joins and data skew. These guidelines cover planning and testing, node selection, join key design, and recommended limits — with the biggest performance gains coming from how you structure your data flow before it reaches joins and aggregations.
Categorize Date Field Values into Buckets: Use a transformation to create a calculated field based on a formula. You add transformations inside a Transform node. You can string together multiple transformations to manipulate data sequentially.
Categorize Text Column Values into Buckets: Use a column field to categorize and organize text values, including email, phone, and URL. For example, to group by region, you can create the buckets West, Central, and East.
Categorize Numeric Field Values into Buckets: Use a bucket field to categorize and organize numeric values. For example, to group by region, you can create buckets High, Middle, Low to group numbers of cases per account.
Build Batch Data Transforms: Ensure that you're getting the most out of your batch data transforms by keeping the following guidelines and recommendations in mind.
Connect and Reorganize Nodes or Branches in a Batch Data Transform: Connect disconnected nodes or branches to append data from any node to any other node in a batch data transform. You can also create a branch at an intermediate node, and append it to another path in the batch data transform.
Data Type Conversion Transformations: Convert Column Types: The data type of a column determines how you can query that column’s data. For example, you can filter and group by a text or date column, or perform math calculations on a number column. Sometimes, fields are tagged incorrectly. If needed, use the column-type conversion transformations to convert columns to the correct types.
Copy and Paste a Batch Data Transform: Use the toolbar or keyboard shortcuts to copy and paste nodes within a single batch data transform or across multiple ones. To use this feature, enable clipboard access permissions in your browser.
Create a Batch Data Transform: Create a batch data transform with data lake objects or data model objects by using the in-app builder.
Data Drift in Incremental Runs: When running a batch data transform in incremental mode, data drift can cause output objects to include changed records instead of filtering them out or replacing them. Data drift is corrected in the daily scheduled full run.
Data Preview Sampling: Use data preview sampling to see a more accurate picture of your data at each node in Preview. By widening the sample size and selecting the mode to arrange your data, data sampling helps prevent working without preview data. Select Filtered Rows to isolate specific values.
Data Space Filters in Data Transforms: Data space filter behavior in data transforms differs based on whether the transform uses DLO or DMO inputs. This distinction helps you anticipate which records appear in preview and which records the transform processes when it runs.
Date and Time Transformations: Calculate on Date Fields: Date fields are key to understanding trends over time or keeping teams aware of upcoming milestones and deadlines. To calculate based on dates or make them more helpful to your analysis, use date and time transformations. Insert a column with the current date and time in a specified format with the Now option. Calculate the duration between two selected date columns as days, months, or years using Date Difference. And add or subtract days or months from a date column with the Add or Subtract Days or Months function.
Date Functions for Formulas: Use date functions to adjust or calculate values from dates. For example, you can add time, find time until, and extract a day of the week from date fields. The arguments for date functions are date fields.
Extract Transformation: Get a Date Component: Use the Extract transformation to pull a selected component from a date field into a new field. For example, extract the hour component from the Case Created Date field to analyze case creation by hour of the day.
Delete a Batch Data Transform: Delete any batch data transforms that you no longer need. Deleting a batch data transform doesn’t delete its target data.
Keep DMO Mappings When Deploying Data Transforms via Data Kits: When you deploy a Data Transform using a Data Kit, the mappings between Output Data Lake Objects (DLOs) and Data Model Objects (DMOs) aren't automatically included. To carry over these mappings, explicitly add the Output DLOs to the Data Kit.
Drop Columns Transformation: Drop unwanted columns from a batch data transform. For example, you can add a Drop Columns transformation after a Formula transformation to drop input columns used for a calculated column.
Date Difference: Calculate the duration between two selected date columns as days, months, or years.
Edit Attributes Transformation: Change the Column Names and Value Formats: Use the Edit Attributes transformation to make column names more descriptive and apply consistent formats to column values. You can set the labels and API names of all columns. You can also set the precision and scale for numeric columns, character length for text columns, and date formats for date columns.
Filter Node: Use a Filter node to remove rows that you don’t need in your target. For example, you can filter case records to focus on escalated cases only. Filter values are case-sensitive.
Flatten Hierarchical Data: Use the flatten transformation to simplify nested, hierarchical data by converting it to a list. The list tracks where each node and the ones preceding it (referred to as its ancestors) appear within the hierarchy. To flatten data, specify the field that contains every node in the hierarchy and the field that contains their corresponding parent. After you run the transform, each record contains the list of ancestors.
Flatten JSON Data: Turn data ingested as a JSON string into tabular data in a batch transform. To use data in downstream features, such as identity resolution and segmentation, the data must be stored as rows and columns in a data lake object or data model object. You can flatten JSON data that’s stored as a single object, an array of objects, or an array of literals (scalar values).
Format Dates Transformation: Standardize the Date Format in a Column: Standardize the format for all dates in a text column with the Format Dates transformation. With a consistent format, you can correctly filter and group records by date, including filtering by date component, such as month. A consistent format also ensures that you can successfully convert the column type from Text to Date.
Formula Transformation: Create a Calculated Column Based on an Expression: Create a column that displays values based on a formula calculation. The calculation can include input from other columns in the same row or across rows. For example, you can create a Profit column based on input from Revenue and Cost columns. Enter formulas in SQL. SQL in Data 360 is a collection of standard and custom functions for numeric, string, and date data.
Input and Output Nodes: Add Data and Write to a Batch Data Transform: Add data to a batch data transform with an input node. To save batch data transform output, write your data to one or more output nodes. Input and output nodes can be either data lake objects or data model objects of type Profile or Other. You can’t have a data lake object input node and a data model object output node, and vice versa. Typically data aware specialists and engineers have access to data lake objects and data model objects, whereas marketers and analysts have access only to data model objects.
Add an Input Node to a Batch Data Transform: Create an input node for each set of data that you want to add to the batch data transform. The batch data transform creates an input node when you initially select your input data, but to add more data you add another node.
Join Objects in a Batch Data Transform: On the batch data transform canvas, use a node to create a join between data objects. To make sure that data from multiple data objects is combined accurately and not duplicated, always add a key qualifier field to create a fully qualified key (FQK) in a join. To ensure that null values in a key qualifier field are matched correctly, use a null-safe join by replacing null values with null strings or empty strings.
Considerations for Using Joins: Unlike a lookup, a join creates a separate record for each match in the output data when multiple rows match. Before using a join, ensure that you understand the implications of duplicate rows.
Cross Join: A cross join combines unrelated records and includes all rows from the left and the right input data source objects. Unlike other joins that use keys to find matches, the cross join pairs every row from one data source with every row of another data source.
Inner Join: An inner join includes only matching rows from the left and right input data source objects. The join includes all matched rows in the output data when multiple rows match.
Left Join: Like a lookup, a left join includes all rows from the left input data source object and only matching rows from the right object. Unlike a lookup, a join includes all matched rows in the output data when multiple rows match.
Lookup: A lookup returns all rows from the left input data source object and only matching rows from the right object, the lookup source. To ensure that the grain of the results doesn’t change, the lookup output data includes one row for each row in the left object.
Join Operations: Batch data transforms support these join operations.
Full Outer Join: A full outer join includes all rows from the left and right input data source objects, regardless of whether they have matches. The join includes all matched rows in the output data when multiple rows match.
Right Join: A right join includes all rows from the right and only matching rows from the left input data source objects. The join includes all matched rows in the data output when multiple rows match.
Merge Records with Multi-Definition Transforms: A transform definition specifies the sequence of operations applied to the data during a batch data transform. Definitions appear as nodes in the data flow canvas. You can combine multiple definitions to produce a single, unified output.
Multivalue Functions for Formulas: Multivalue data is limited to use in formulas. To work with multivalue data in downstream processes from batch data transforms in Data 360, such as reporting in CRM Analytics, convert it into a string.
Work with Nodes in Batch Data Transforms: Batch data transforms provide nodes that allow you to bring in data, transform it, and write the results to a target. You add nodes in the canvas. String together multiple nodes to sequentially change the flow of the data. A batch data transform can have multiple Input and Output nodes along with multiple Append, Join, Aggregate, Filter, Transform, and Update nodes.
Now: Create a column that displays the current date and time in a specified format. This transformation works without interacting with any other columns.
Numeric Operators and Functions for Formulas: Use numeric functions to calculate values from measures. For example, you can round, truncate, and determine the absolute value of a number. The arguments for numeric functions can be numbers or measure fields.
Write the Batch Data Transform Results to a Data Lake Object Output Node: To use your batch data transform as input to identity resolution, calculated insights, or segmentation, you write the results to a new or existing data lake object (DLO) in an output node. The object that holds the output is called a target data lake object. You can’t save a batch data transform without an output node.
Write the Batch Data Transform Results to a Data Model Object Output Node: Write the results of a batch data transform to an existing data model object or one created through the output node. The object that holds the output is called a transform data model object. You can only write to a transform type data model object within a data transform. You can use a transform type data model object as input to another batch data transform and downstream processes such as identity resolution, calculated insights, and segmentation.
Batch Data Transforms: To transform your data for further usage, such as identity resolution, segmentation, or calculated insights, or to derive insights in Salesforce reports, use a batch data transform. A batch transform is a repeatable series of operations that you can run when data updates. The first time you run a batch data transform, it pulls in data and defines it according to your steps. You can then run a transform manually or set it up to run at scheduled intervals.
Preview Results in a Batch Data Transform: Catch mistakes early by reviewing the results of each node and transformation that you add to a batch data transform. Each user can initiate up to 4,000 previews per hour. The preview shows a sample of the latest available data, up to 5,000 rows.
Run a Batch Data Transform: Run a batch data transform for the first time to create the data. Run it again to update it with the latest data.
Batch Data Transform Run Types: A batch data transform runs in either incremental or full mode. During an incremental run, the transform applies only to input data that has been added, updated, or deleted. These changes can occur through a data stream or from the target of another batch data transform that runs incrementally. A full run processes both new and existing data. You can’t configure the run type. The batch data transform selects the run type based on specific criteria.
Schedule a Batch Data Transform: To ensure that the most up-to-date data is available by a particular time or to run a job during non-business hours, schedule a batch data transform.
Split Transformation: Break Up Column Values: Split a string in a text column into two values by specifying a delimiter. To split column values into more than two parts, add multiple instances of the split transformation. For instance, you can use three split transformations to split an address into street address, city, state, and ZIP code.
Check Batch Data Transform Status: After you’ve created at least one batch data transform, clicking the Data Transforms tab leads you to the Recently Viewed screen, where you can view a data transform's status.
String Functions for Formulas: Use a string function to manipulate the text data type, including email, phone, time, and date values. For example, change text from uppercase to lowercase, concatenate values from multiple fields, and replace values.
Reserved Keywords in Formulas: Batch data transforms have reserved keywords that need special handling when manually writing formulas. Use double quotes “ when referencing fields that share a name with reserved keywords. To avoid cross-referencing your field names with this list, the best practice is to use double quotes around all field names.
Notation for String Formulas: If you manually type your formulas, use single quotes ‘ for string literals and double quotes “ for joined fields with dot notation or reserved keywords.
Transform Node: Transform the Data Before Loading It Into an Output: Use a Transform node to manipulate data based on the transformations added to the node. You can add one or more transformations to each Transform node. For example, you can add transformations to concatenate two columns, standardize the formats in a date column, and detect the sentiments of comments in a feedback column. Each transformation modifies the data in a unique way. To manipulate data at multiple stages of a data transform, add a separate Transform node at each stage.
Text to Date Transformation: Convert the Column Type: A date column is sometimes tagged as text if it contains unexpected string values or dates in different formats. To use date functionality, such as grouping by month, use the Text To Date transformation to change the Text column type to Date.
Text to Number Transformation: Convert the Column Type: To perform a mathematical calculation on the column, such as calculating the average, use the Text To Number transformation to change the Text column type to Number. You can perform mathematical calculations on numeric columns only. Numeric types include the percent data type.
Transformations for Batch Data Transforms: Use transformations to perform calculations on and manipulate your data. For example, you can use a transformation to create a calculated field based on a formula. You add transformations inside a Transform node. You can string together multiple transformations to manipulate data sequentially.
Update Node: Swap Column Values: You can swap column values with data from another data source when key pairs match with the update node. For example, to update selected account names after a series of recent acquisitions and mergers, replace the name from an uploaded spreadsheet based on matching account IDs. To minimize processing time, change column values with matching key pairs instead of every value in the column.
Window Functions for Formulas: Use window functions to perform calculations across rows. For each row of input data, perform a calculation on a selection—or window—of related rows. Unlike aggregation functions that group rows, window functions return a result for each row. For example, you can calculate the changes to the opportunity amount throughout the stages of each opportunity to see if you can hit your sales target.
Data Governance Considerations: Before you begin, it’s important to review supported features and considerations for using data governance features in Data 360.
Customer Data Platform Billable Usage Calculations: Previously named Customer Data Platform orgs are billed based on contracted entitlements. Understanding which usage is included for billing helps you make data management decisions.
Billing Considerations for Code Extension: Using code extension in Data 360 impacts the consumption of credits used for billing for orgs operating Data 360 under a Data Cloud license.
Billing Considerations for Data Federation: Data Federation uses Data 360 to query and activate external warehouse data without copying it. Use of Data Federation impacts the consumption of credits in these usage types.
Billing Considerations for Data Graphs: Use of data graphs impacts the consumption of credits in these usage types. A data graph creates a transform at the backend and hence reflects under the respective usage type in Digital Wallet.
Billing Considerations for Data Ingestion: Data Ingestion uses Data 360 to ingest data from external sources. Use of Data Ingestion impacts the consumption of credits in these usage types.
Billing Considerations for Data Lens: Data Lens uses the Data 360 query layer to fetch and display live data previews, which consumes credits from your Data Services or Flex Credits consumption card.
Billing Considerations for Data Transforms: Data Transforms use Data 360 to cleanse, enrich, and reshape data from DLOs and DMOs to prepare it for insights, segmentation, and AI features. Use of Data Transforms impacts the consumption of credits in these usage types.
Billing Considerations for Insights: Insights uses Data 360 to define and calculate multi-dimensional metrics across unified customer data to power real-time personalization and analytics. Use of insights impacts the consumption of credits used for billing in these usage types.
Billing Considerations for Multi-Currency Usage: Use of multiple currencies in Data 360 impacts the consumption of credits in these usage types. Credits are consumed for setting up currencies and maintaining them over time.
Billing Considerations for Segmentation: Segmentation uses Data 360 for processing and publishing segment data. Use of segmentation impacts the consumption of credits in these usage types.
Billing Considerations for Secondary Indexes: Data 360 Secondary Indexes enable faster query performance and more efficient data retrieval with attributes other than the primary identifier. Use of secondary indexes impacts the consumption of credits used for billing in these usage types.
Bring Your Own Model: Bring Your Own Model (BYOM) lets you connect models built in external platforms with Data 360. BYOM provides direct access to Salesforce data in external ML platforms and uses a zero-copy approach, which eliminates the need to duplicate data. After you connect a model, you can easily consume predictions. Creating, training, and deploying models requires coding expertise for setup in the external platform, but connecting the models to Salesforce is simple.
Restrict Access to Activation Targets for Specific Users: Use permission sets to make sure users only have access to specific activation target data. Since Marketing Managers, Marketing Specialists and Data Aware Specialists, can view all activation targets by default, use permission sets to manage their access. To manage access, clone an existing permission set and share access to specific activation targets with users assigned to that permission set only. For example, restrict access to specific activation targets for users in specific departments or geographies.
Create a Platform Event-Triggered Flow: Automate your business process and launch a flow internally when a data record changes. A flow collects data and performs actions in Salesforce or an external system.
Build and Manage Data Graphs: Create and manage data graphs in Data 360. You can view a data graph using the Data 360 user interface or an API.
Build and Share Data 360 Functionality: Extend the use of Data 360 by creating and installing data kits and packages in other orgs and environments. Set up a Data 360 sandbox to test functionality before building in your production environment.
External Activation Platforms: Create and package an External Activation Platform to determine file formatting, required data details, or where to publish the files during activation. Share your package on AppExchange or provide the package URL generated in Package Manager for Data 360 admins to install.
Build a Segment with Attribute Shortcuts: Speed up segment creation by using attribute shortcuts that store filter attributes with preconfigured data model paths. Instead of browsing the full attribute library, select from shortcuts that your team has set up.
Analyze Data from Data 360: Gain insight into your Data 360 data using the best analysis tool or product for your business. The following tools offer a variety of analytics features to use with Data 360. Find your tool and access learning resources from here.
Multiple Marketing Cloud Engagement Business Units in Segments: Filter segments by business unit (BU) to determine the true size of segments for campaign planning. If segments aren’t filtered by business unit, the difference between a Data 360 segment population and Marketing Cloud Engagement data extension count can be significant.
Zero Copy Data Federation: The Data 360 zero copy data federation is the process of querying data from multiple, physically distinct data sources. This approch allows users to treat disparate systems as a single logical system, called a federation, while maintaining the autonomy of each system. Importantly, data federation avoids duplicating data across system boundaries. Data 360.
Data Type Compatibility in Data Federation: When federating data from external sources, the source data is converted to the corresponding Data 360 data types. If the source data contains a data type that's not supported in Data 360, the column is ignored. During data stream creation, fields whose data types aren’t supported in Data 360 are listed under the Unsupported Fields tab. Array, struct, and other nested datatypes are not supported in data federation.
Customer 360 Data Model: The Customer 360 Data Model reduces the complexities of integrating data across cloud applications by providing standardized data guidelines. Extend the data model to create data lakes, generate analytics, train machine-learning models, build a single view of the customer, and more.
SQL Functions for Insights: Use these aggregate, datetime, analytical, formatting, math and statistical, and boolean functions when you author calculated insights in Data 360. The Aggregatable column indicates whether a function's output can be rolled up across fewer dimensions when queried in segments or activations.
SQL Rules for Insights: When you write SQL for calculated or streaming insights in Data 360, rules govern how each clause is interpreted and what constraints apply. You can also embed a nested subquery in a WHERE or JOIN clause to restrict which rows the outer query returns.
Calculated Insights in Activation: Use Data 360 Calculated Insights to enable activation journey decisions and message personalization.
Calculated Insights in Segmentation: Use a calculated insight to control data-driven insights in segmentation. You can also describe the calculated insight’s metrics and dimensions in a segmentation filter.
Using the Join Clause for Insights: A calculated insight can simultaneously access multiple data model objects (DMOs). A calculated insight query that accesses multiple rows of the different DMOs at one time is called a join query. Data 360 supports four types of joins in a calculated insight: inner, left, right, and full.
SQL Operators for Insights: Use these arithmetic, currency, and logical operators in SQL expressions when you author calculated insights in Data 360.
Insights List View: After creating a calculated insight in Data 360, see the details and edit your record from the Calculated Insights list view. The status, last run time, last run status, and schedule fields are updated in near-real time.
Case Data Model: The Customer 360 Data Model case subject area groups and defines recorded issues, such as laptop connectivity problems or support tickets.
Category: When creating a data stream, select a category or type of data found in that data stream.
Customer Data Platform License Billing and Limits: Customers with this license have access to Data 360 on a Salesforce org. This license is no longer available for purchase and can't be renewed. Orgs with this license are billed based on the numbers of profiles, engagements, and other records in their orgs. Other features have limits. Not all Data 360 features are available on Data 360 orgs with this license.
Change the Category of a Data Model Object: In most cases, a data model object’s category can’t be changed after the object is created. The category can be changed only if it’s changing from Profile to Other or from Other to Profile on an object that isn’t used in segmentation or activation.
Data 360 Usage and Access Changes: Changes to documentation about Data 360 licensing, feature availability, limits, billing, and permission sets are logged in this document.
Check Connector Status: Get the current status of your Web Content (Crawler or Sitemap) connector.
Sensitive Data Classification in Data 360: Classifying data helps ensure it’s handled appropriately, securely, and in line with regulatory or internal standards. You can classify data by considering both its sensitivity (how confidential it is) and its usage (how and by whom it’s accessed or used).
Data Cleansing and Preparation: Cleaning and preparing your data is crucial for success when using the Data 360 segmentation and activation capabilities.
Clone an Insight: Create a calculated insight using metrics from an existing insight. You can duplicate the SQL function from the calculated insights record in Data 360 and edit the attributes such as dimensions and filters.
Clone a Data Graph: When you clone a data graph, the clone inherits the data space and primary data model object (DMO) from the original data graph. You can’t change the data space or primary DMO, but you can select different related objects and fields. You can also change the cloned data graph’s name, API name, filter settings, and description.
Clone a Data Share: You can create a data share by cloning an existing one. You can add or remove data objects during cloning.
Customer 360 Data Model: Individual and Contact Points: When using the Customer 360 Data Model, Data 360 prepares a list of Salesforce-published objects, fields, metadata, and relationships to ensure consistency across applications and business processes. Individual and contact point objects are important concepts for successful and complete data streams.
Code Extension: Bring your custom Python code into Data 360 to extend Data 360's native capabilities by using code extension. If the native Data 360 features don't meet your business requirements, deploy your custom logic to supported Data 360 features.
B2C Commerce Connector: Connect your B2C Commerce instance to a production site and create a B2C Commerce data stream. The data stream ingests all data from active production sites, both protected or unprotected. This connector is for production sites only. You can’t set up a B2C Commerce connector to test, sandbox, or non-live production site.
Capping Communications for B2C Engagements: Set caps on communications with B2C customers, leads, or prospects to ensure compliance with regulatory requirements, enhance the customer experience, and avoid message fatigue. Optimize your campaign spend budgets across lines of business by applying department-level thresholds. Stay compliant with regulatory requirements by avoiding unsolicited, unregulated communications to targeted entities.
Configure Communication Capping Parameters and Values: Define communication capping parameters such as channels and departments that are relevant to your organization and business strategy. You must configure the capping parameters and values before creating capping rules.
Delete a Companion Connection: To prevent errors in a companion org, address your multi-org data dependencies before deleting a companion connection. If you try to disconnect a companion org that has dependencies, Data 360 shows you the existing dependencies.
Set Up a Data Cloud One Companion Connection: Establish a Data Cloud One Companion connection between the Data 360 org and another Salesforce org. You can't create a companion connection to a Salesforce org that's already connected to another Data 360 via a companion connection. You also can't connect a sandbox org to a production org.
Objects Shared with Companion Orgs: Data model objects (DMOs) and calculated insights objects (CIOs) in shared data spaces are shared with the companion org. You can reference data from these objects with Data 360 features and use the data with platform features, such as Flow and related list enrichments.
Data Cloud One Companion Connections: With a Data 360 companion connection, you can create a bidirectional connection between a single Data 360 and one or more Salesforce orgs. Companion connections build on the capabilities of standard connections and sync unified data back to the companion Salesforce org. With a companion connection, your Salesforce orgs get a 360 degree view of their customers built with data from all your company's data.
Data 360 Connection Details on Companion Orgs: In a companion org, you can identify your home org, check what data spaces are shared with your org, and check the health of your companion org's connection in Setup. Use the Connected Org name and Instance details to identify the Data 360 your org is connected to and the geographical region it's housed at.
Data 360 Features on Companion Orgs: Use the Data Cloud One app on a companion org to access Data 360 features shared from a Data 360 home org. Full Access means that users with the appropriate permission set can create, update, and delete configurations for the feature on the org they’re logged into.
Compare Standard Salesforce CRM and Data Cloud One Companion Connections: You can connect a Salesforce org to Data 360 using a standard Salesforce CRM connection or a Data Cloud One companion connection paired with a standard connection. The table lists some of the differences between these types of connections.
Composite Key: Use a composite key to uniquely identify a record.
Configure Automatic Content Tagging (Beta): Configure automatic content tagging for unstructured files and define a confidence threshold. Data 360 approves tags that meet or exceed the threshold, but others require manual review.
Create a Communication Capping Configuration: Create the communication capping configuration and associate it with a data space in Data Cloud. You can apply it to either all configured parameters or a specific subset. Each organization is limited to one active communication capping configuration.
Configure the Data 360 Engagement Timeline Widget: The Data 360 engagement timeline widget shows engagement data in a chronological format on Account, Contact, Lead, and Person Account records. Configure up to 10 types of engagements at once, and your users can sort them newest or oldest first.
Configure Periodic Full Refresh Schedule: The periodic full refresh schedule for a Salesforce CRM connector data stream is determined by a number of variables, including a full refresh interval that you can configure to determine when the full refresh is triggered.
Set Up a B2C Commerce Connection: After you create your Data 360 instance, you can set up a B2C Commerce Connection. Then you can control the scope of data ingestion from B2C Commerce. The B2C Commerce connector only supports connections to production instances of B2C Commerce.
Connect Salesforce Data to Data 360: Data drives Data 360 functionality. In Data 360, you can connect to many data sources. To get started with standard connections, connect Data 360 to a Salesforce CRM org and install standard data bundles. Then, ingest and further map and refine your data.
Set Up an External Activation Platform: Reach more customers by connecting an external activation platform to Data 360. Install an external activation platform from AppExchange or a packaged URL.
Connect Salesforce CRM Orgs to Data 360: Connect your Salesforce orgs to Data 360 to ingest data from Salesforce objects for processing and unification in Data 360. Depending on the type of connection that you select, you can send back only data actions or harmonized Data 360 data. To access Data 360 data and features in your Salesforce CRM orgs, set up a Salesforce companion connection. Platform components, such as Flow, Apex, CRM, and data enrichment, can also access Data 360 data across companion orgs.
Connect a SageMaker Model: To consume your predictions in Salesforce, connect your SageMaker model with Data 360 and define the prediction criteria for your use case.
Data Source Configuration in Data 360: To connect data sources, review the admin tasks that must be completed first. After they’re configured in Data Cloud Setup, data streams can be created for each data source. Find information about all the varieties of data source connectors, their configuration, and data stream setup under Connect Data.
Data Sources in Data 360: Data sources are ingested into or connected to Data 360 in different ways with specific requirements and steps that an admin configures before creating a data stream.
Considerations for Selecting Related Attributes in Data 360 Activations: Related attributes are data fields that belong to data model objects (DMO) connected to your primary or activation DMO. These attributes help you access information from other objects that are linked through a defined relationship path. There’s a direct relationship with the DMO, related attributes, and the filters you can apply.
Ethics, Privacy, and Consent: Understand the concept of ethical data use and learn how to incorporate data ethics into your Data 360 implementation. Learn about how to protect your consumer’s privacy, data rights, and manage consent.
Add Filters to Contact Points in Activation: When activating a Data 360 segment, you can filter and sort contact points to ensure you are targeting the right audience on the right channel with the right permissions. This feature is essential for managing customer consent, preventing duplicate messages, and improving the personalization of your campaigns.
Contact Points and Source Priority Order: Contact point selection determines which objects and fields are included in an activation and sent to an activation target. Use the source priority order to determine which contact point value is selected for the activation.
Filtering Using Containers and Attributes: On the segmentation canvas, you can define rules to filter your population to a targeted group. You can create and combine filters in different ways for different results. When you define a filter using a related attribute, a container is added to the canvas with applicable rule options. The related attribute’s data model object (DMO) becomes the container object.
Event Time in Data 360: An event date is a time-based attribute in a DMO that is defined at the data stream level during ingestion. A DMO can have multiple event dates. The Event Time Field applies only to DMOs designated with the Engagement category.
Container Paths: Some containers have multiple paths back to the segmentation entity. When a container has multiple access paths, you can select a path to help Data 360 understand how to build your segment. The paths available when you first create a filter may differ from those available when you edit it later. This is expected behavior driven by relationship-cardinality rules.
Content Harmonization Known Limitations: Certain functionalities aren't available for content harmonization. Keep these limitations in mind while harmonizing content and viewing it on the Content Viewer.
Content Viewer User Engagement Insights: When you enable content harmonization, Data 360 automatically starts collecting information about user interaction with the Content Viewer, providing you with insights to improve your content.
Control and Metadata Change Events: Set up notifications for your Data 360 data in Flow Builder and create reports to see your Data 360 statuses.
Controlling External User Access in Data 360: Use data governance policies to control what external users can access in your Data 360-powered apps. By defining clear, policy-based access, you make sure that unidentified external users and authenticated external users see only the data intended for them.
Copy Field Enrichment Matching: Copy Field enrichments enhance the records in your org by adding data from other data sources. To avoid returning data that isn’t relevant to the records in your org, the enrichment matches data from your org to data in Data Cloud. How the data is matched determines what data is returned.
Copy a Segment: In Data 360, you can copy an existing segment to populate it with the same filters and customizations as an existing segment.
Accept or Reject a Clean Room Invitation: If you’re a provider, when you receive an invitation to a Data 360 clean room collaboration, you can accept or reject the request. If you accept, you can choose how to map your data for the collaboration.
Activation Use Case Template: The activation use case provides consumers with a privacy-first method to activate their first-party audience data through a provider's media or advertising platform. Rather than transferring entire segment lists, consumers identify matched audiences within the clean room. Data 360 writes the matched records directly to the provider's Amazon S3 bucket for use in campaign activation.
Allowlist a Clean Room Provider: To collaborate directly with a trusted provider, add them to your Data 360 org allowlist. When you add a collaborator to your allowlist, you don't need a clean room package to create a clean room collaboration with them.
Billing Considerations for Clean Rooms: Clean rooms uses Data 360 to ingest data securely into a clean room collaboration. Use of Clean rooms impacts the consumption of credits in these usage types.
Cancel a Query Run: Cancel a query run that's in progress when you no longer need the result or you started the run by mistake.
Clean Room Activation: The clean room activation use case is a privacy-first way to activate first-party data in a provider's advertising platform. Data 360 matches the data in the clean room, then delivers the shared audience list directly to the provider's Amazon S3 bucket. The provider activates it from there, in the demand-side platform (DSP) or media system they already use, without building a new integration.
Clean Rooms: Use a clean room to collaborate securely on audience data with other parties without exposing raw data or compromising confidentiality. Providers can use their data to help companies find appropriate audiences for their products and services. Consumers can use the insights to refine their targeting strategies. Use case templates enforce rules around input data, supported queries, and privacy regulations.
Clone Clean Room Mapped Templates: Clone a mapped template to reuse its data mappings as a starting point for a new template, without mapping each attribute from scratch.
Collaborate in a Clean Room: Set up a query in a Data 360 clean room collaboration to compare a consumer segment with the provider's data, without exposing raw records. Run a query manually for an immediate output, or schedule it to run each time the segment publishes.
Collaboration with an AWS Clean Rooms Provider: As a consumer using Data 360 clean rooms, you can initiate collaborations with a provider who uses AWS Clean Rooms. AWS-based providers use Data 360 once to create and publish the clean room package, then complete every other action in AWS Clean Rooms. Consumers manage their side of the collaboration entirely in Data 360.
Configure an AWS Identity Provider for Clean Room Activation: For each clean room activation collaboration invitation, configure your AWS account so that Data 360 can securely write query results to your S3 bucket, then accept the invitation in Data 360.
Configure Snowflake Trust Relationship for Clean Room Identity Boost: For each collaboration invitation for the identity boost use case, complete the required Snowflake setup and then accept the invitation in Data 360. Data 360 tests the Snowflake connection automatically on acceptance.
Clean Room Consumers: Consumers are advertisers seeking insights from providers’ data to find appropriate audiences for their products. To initiate a clean room collaboration, deploy a clean room package from AppExchange, or allowlist a provider. If you allowlist a provider, you don’t need the clean room package to initiate a clean room collaboration. Use the Salesforce-provided collaboration template to initiate the collaboration.
Create a Clean Room Package: Providers can offer clean room packages that contain collaboration templates on AppExchange. Consumers interested in collaborating on your data can download the clean room package and initiate a collaboration.
Create a Clean Room Collaboration and Invite a Provider: To initiate a collaboration in a Data 360 clean room, you first create a collaboration. You can use an existing collaboration template to map your data or create one. When you save the collaboration, an invite is sent to the provider.
Custom Collaboration Template Sample JSON: Copy the template code below or download it directly in Data 360 to begin creating your custom template. Fill in the required and optional fields with appropriate values.
Custom Collaboration Templates: Custom collaboration templates are JSON files that providers can create to specify the required metadata attributes and the terms of the collaboration. Custom templates can support multiple use cases and multiple queries under each use case. To create a custom template, copy the sample JSON, specify the field values, and upload the template while creating a package. You can also download the sample JSON from Data 360 Setup when you’re creating the clean room package.
Supported Data Types for Custom Templates: The data types supported in custom templates vary based on the clean rooms platform. Adhere to supported data types for successful query execution.
End a Clean Room Collaboration: When you end a clean room collaboration, you can’t restart it. If you have permissions to run queries and receive results in the collaboration, you can still access query results and query logs from past query runs.
Data 360 Clean Room Collaboration Process Overview: In a clean room collaboration, you and another party can run permitted queries on each other's data while keeping raw records private. The provider specifies which data to share, under what terms, using a collaboration template. Data 360 enforces appropriate privacy and security measures, and gives both parties full visibility into how their metadata is accessed and queried.
Identity Boost Custom Collaboration Template: The identity boost custom collaboration template is a JSON configuration that supports identity enrichment use cases in Data 360 Clean Rooms. Use this template to enrich your first-party individual data with third-party identity attributes, such as real IDs from identity resolution providers, in a privacy-safe environment.
Identity Boost Custom Template Sample JSON: Copy the template code below or download it directly in Data 360 to begin creating your custom template for the identity boost use case. Fill in the required and optional fields with appropriate values.
Identity Boost: Use identity boost to collaborate with identity providers in a clean room and enrich your first-party data with missing contact points. Use the enriched data in your activations to improve match rates.
Make Segment Names Available in Data 360: Segment names are stored in the Salesforce Platform. To include them in your clean room query results, make them available in Data 360 by connecting to the Market Segment data by using the CRM Connector. If you don't ingest segment names into Data 360, your query reports only contain the segment IDs.
Manage Scheduled Queries: Review the queries scheduled to run on segment publish in a collaboration, check their status, and delete a schedule that you no longer need.
Map Data for a Clean Room Use Case: Create a mapped template to save your data mappings for a specific use case. You can create a template before a clean room collaboration is active. You can also map your data while creating a collaboration or accepting an invitation to collaborate.
Clean Room Packages on AppExchange: If you want your use case template to be publicly discoverable by potential collaborators, create a clean room package in Data 360 and then publish it on AppExchange. Only an AppExchange partner can publish a package to AppExchange.
Plan Your Snowflake Environment for Identity Boost: Before accepting identity boost collaboration invitations, decide how to isolate customer data in your Snowflake account and which region to use. These decisions affect the architecture of every collaboration that you accept, so make them before you receive your first invitation.
Prepare Your Data for a Clean Room: To share segments in a clean room, complete these steps to prepare your data. Preparation includes required steps such as ingesting the data, mapping the data lake object (DLO) to a data model object (DMO), hashing fields to protect personal information, and creating segments. You can share segments only in a clean room.
Clean Rooms Privacy and Security: Data Cloud clean rooms use robust privacy and security measures to keep your data secure. To collaborate, both parties must agree to the terms and conditions. All sensitive data must be encrypted with the SHA256 hashing algorithm before it enters the clean room. Hashing helps protect user identities and reduces risks if the data environment were ever compromised.
Clean Room Providers: Providers are publishers or companies that own a platform and can offer access to data or advertising space to other companies. Data 360 Clean Rooms enable providers to collaborate using Salesforce Data 360 clean rooms or AWS Clean Rooms.
Publish a Clean Room Package to AppExchange: After creating a clean room package in Data Cloud Setup, use data kits to package it. Then use the package manager to create a managed package and publish it on AppExchange. You can only publish managed packages on AppExchange.
Queries in Clean Rooms: Run queries in a clean room to compare your data with a collaborator's data and gain insights, without exposing raw records.
Validating Clean Rooms in a Sandbox Org: To verify query results, test your clean room packages, configurations, and data mappings in a Developer or Developer Pro Data 360 sandbox. When the query run yields expected results, deploy the mapped templates from a sandbox org to a production org.
Segment Overlap Use Case Template: The segment overlap use case helps providers and consumers understand shared audience segments. Consumers can identify overlaps with specific audience segments in the provider database. Providers can demonstrate the value of their audience data. Both parties can plan co-marketing efforts and refine their targeting strategies without exposing raw data.
Set Up Clean Room Activation: After a clean room activation collaboration is active, the provider receives matched audience data directly in their Amazon S3 bucket. The provider can then use the data to run targeted campaigns in their advertising platform or downstream media system.
Set Up Clean Room Identity Boost: Setting up and using a clean room identity boost involves steps for both the provider and the consumer. The provider creates a package and sets up their Snowflake environment. The consumer installs the package, creates a collaboration, and maps their data. After the provider accepts the invitation and runs the enrichment job, Data 360 automatically refreshes the enriched data in the consumer's org.
Salesforce-Provided Collaboration Templates: Data 360 provides a predefined template to identify segments that overlap. If you’re initiating a collaboration by allowlisting a trusted provider, use the Salesforce-provided collaboration template for the collaboration.
Clean Room Collaboration Templates: A collaboration template contains details about the required metadata attributes and the terms of the collaboration. Providers can create custom collaboration templates that support multiple use cases. If you’re a provider using Data 360 clean rooms, you can also use Salesforce-provided templates.
View Clean Room Query Results: Check the latest output for a clean room query whenever a manual or scheduled run completes.
Use SQL to Create Insights: Write SQL to define calculated and streaming insights in Data 360. Each query must follow a specific structure and use supported functions, operators, and data types.
Create a Copy Field Enrichment: Add information from Data 360 data model objects (DMO) to record pages in your org, so you can show information and interactions from multiple systems, orgs, or business units in a related list.
Create a Custom Field for a Custom Object in Data Cloud: Add custom fields to your custom objects. You can capture your unique business data by storing it in custom fields. When you create a custom field, you can configure where you want it to appear and optionally control security at the field level.
Create a Custom Object in Data 360: Create a custom object to integrate data actions with a flow. Custom objects store information that’s specific to your company or industry. It helps you track everything related to your business in one place.
Create a Record-Triggered Flow in Data Cloud: Create a record-triggered flow to publish platform events of Data 360 actions. You can use the published platform event to resume your paused auto-launched flow. With a record-triggered flow, you can update another record, send a notification, initiate a process, or maintain data consistency. A record-triggered flow runs only when a record is created or updated. It can update a record 10 times faster than a record-change process.
Create a Data Action in Data 360: Use a data action to send an alert or event to targets such as Salesforce Platform, Webhook, or Marketing Cloud Engagement, or Amazon Kinesis. Create the data action to focus on near real-time events and insights at different levels, including the channel, product, account, service, or individual.
Create a Webhook Data Action Target: Send a data action event to a webhook target and protect the message integrity with a Salesforce-generated secret key. A webhook is a type of API that’s event driven rather than request driven. The secret key validates the payload requests sent from Salesforce. The webhook transports the HTTP payload with events in the format of DataObjectDataChgEvent.
Create a Data Extension in Data 360: Quickly create a data extension for Journey Builder API Events of a data action directly in Data 360. This automated process removes the need to go to Marketing Cloud Engagement.
Create a Data Graph: Before creating a data graph in Data 360, decide which existing data model object (DMO) to use as the primary DMO. The primary DMO determines which related objects you can include in the data graph.
Create a Data Graph from a Data Kit: Avoid the time involved in creating a data graph manually by using a data graph from a published data kit. You can deploy a data kit containing data graphs using both managed and unmanaged packages. Changes that you make to the original data graph don’t affect the data graph in the data kit.
Create a Dynamic Segment: Create a dynamic segment based on a Data Model Object (DMO) and run segment queries without persisting data in Segment Membership DMOs. Define attribute filters with placeholders that accept dynamic values at execution time.
Create a Marketing Cloud Engagement Data Action Target: Send automated emails and journeys to customers based on behavioral or transactional event data routed from Data 360. Trigger emails and journeys to customers from data model objects (DMOs) and calculated insights. For example, a customer purchases an item and searches for “installation manual” on your website. You trigger an event that sends the customer an email with a how-to video or installation instructions.
Create a Real-Time Data Action in Data 360: Create a real-time data action to send an alert or an event and get responses in real time. Use a real-time data action to focus on real-time events and insights at different levels, such as the channel, product, account, service, or individual level.
Create a Real-Time Segment: Create a real-time segment that completes on demand in milliseconds. To make the segment available in your real-time data graph, add the Segment ID and Timestamp fields from the segment membership DMO object to the real-time data graph. You can’t use a real-time segment with exclusion criteria and nested batch segments. You also can't use segment counts and manual publish with a real-time segment.
Create a Direct-DMO Related List Enrichment: Add information to a Salesforce object related list from a Data Cloud DMO by using a direct relationship between the DMO and the Salesforce object. The direct relationship doesn't require that you complete identity resolution for the DMO. For example, a Vehicle object in Automotive Cloud can have a related list of Vehicle Telematics Event DMO records from Data Cloud.
Create a Related List Enrichment: Add information from Data Cloud DMOs to record pages in your org, so you can show information and interactions from multiple systems, orgs, or business units in a related list.
Create a Streaming Insight Using SQL: Write SQL expressions to compute streaming metrics across dimensions from your near real-time data sources. A streaming insight analyzes data at specific points in time. Streaming insights help build time series aggregations in near real time to orchestrate and optimize your data within Data 360.
Create a Tag in Data 360: Tags are essential for organizing, classifying, and managing data at scale. They act as metadata labels that describe the content, sensitivity, usage, or purpose of a data object or field. Tags can have one parent and multiple child tags, but they don't support more than one level of hierarchy.
Create a Waterfall Segment: If you’re running a campaign with multiple offers, use a waterfall segment to process a list of existing segments in a priority order. The primary goal is to ensure that an individual who qualifies for more than one segment is placed only into the highest-priority segment they match. This creates a series of mutually exclusive audiences, which is ideal for campaigns where you want to send customers only one, most-relevant offer.
Create an Activation Template: Create an activation template to define reusable activation configuration logic, including channel selections, source priority, contact point filters, enrichment data, and membership filtering. Apply this configuration when creating new activations or reference it in segment-triggered flows. Templates are created based on the combination of DMO and the activation destination, which allows you to reuse the same template across all activation targets for a given destination.
Create an Allow All Policy in Data 360: To maintain broad user access without enforcing restrictions, create an Allow All policy in Data 360. This policy grants access to objects within the data spaces assigned to users through permission sets.
Create an Attribute Shortcut: Save a frequently used attribute and its data model path as a shortcut so that you and other segment builders can reuse it without navigating the full attribute library each time.
Create a Data 360 Loyalty Activation Target: To publish segments for use with Loyalty Cloud, create an activation target in Data 360. You can create one activation target for Salesforce.
Create a Classification in Data 360: In Data 360, define sensitivity or usage levels to categorize data and drive governance policies such as masking and access control.
Create a File Storage Activation Target: Create an activation target in Data 360 to publish segments and batch data model objects (DMOs) to external file storage activation platforms like Amazon S3, SFTP, Google Cloud Storage (GCS), or Microsoft Azure blob storage.
Create a B2C Commerce Data Stream: The only way to create and map B2C Commerce data streams is to create a data bundle called B2C Commerce Order Bundle. This data bundle includes specific entities, some of which support custom field attributes for mapping to a data model object (DMO), and it doesn't allow for individual data stream deployment. This deployment requires you to be a Data 360 admin.
Create Communication Capping Rules: Create capping rules to apply to the communication capping activation targets in Data Cloud. You can create a capping rule in the UI or by uploading a JSON file with the rule conditions. You can apply rules individually at each parameter level or cumulatively at the enterprise level.
Create a Communication Capping Activation Target: Create a governed target for which segment membership is prioritized and filtered during activation in Data Cloud. You can set a cap for Marketing Cloud Engagement, Microsoft Azure, and SFTP targets.
Create a Salesforce CRM Data Stream: Create a data stream to begin the flow of data from a Salesforce CRM data source to Data 360 for processing and unification.
Create a Salesforce CRM Starter Bundle: Create a data stream using a starter bundle to begin the flow of data from a Salesforce CRM source into Data 360.
Create a Custom Data Model Object: If a standard data model object doesn’t meet a business need, you can create a custom data model object (DMO) to store information that is unique to your business. You can create a custom data model object from an existing DMO, from a file, or from scratch.
Create an Activation for a DMO Using a Custom Event: When you create a DMO activation in Data 360 using a custom event, add the required attributes for a tailored activation experience. Conversion API (CAPI) activations to Ad Audience partner targets use DMO activation.
Create a Custom Taxonomy in Data 360: Custom taxonomies allow you to define your own categories and subcategories based on your specific data, use cases, or business needs. You can create tags, group them into a hierarchy, and align them with internal policies or specialized regulatory requirements.
Create a Data Share: Select the Data 360 objects that you want to include in a data share to share with an external system.
Create a Data 360 Activation Target: Create an activation target in Data 360 to publish segments and batch data model objects (DMO) to the Audience DMO. You can access data from segments and DMOs without a direct connection to an internal or external target system by publishing the data to the Audience DMO.
Create an External Platform Activation Target: To publish segments to your external advertising platforms like Amazon Ads, create an external platform activation target in Data 360. An external platform can have multiple activation targets, but you can attach only one activation target to an external platform account.
Create an Activation for an External Platform: In Data 360, create an activation to publish a segment to an external platform, such as an AppExchange partner (Liveramp, Criteo, Tradedesk) or a strategic partner (Google, LinkedIn, Meta, or Amazon Ads) target. For supported Ad Audiences partner targets, you can use identity providers and enrich Data 360 identity attributes during activation.
Create and Install Your Managed Package for Data 360: Use a managed package to build a Data 360 app and distribute it on AgentExchange. Packaging is the container that you fill with metadata, and it holds the set of related features, customizations, and schema that make up your app. A package can include many different metadata components, and you can package a single component, an app, or library.
Create Data Streams from a Marketing Cloud Engagement Starter Bundle: To begin the flow of data from a Marketing Cloud Engagement source to Data 360, create data streams from starter bundles. Data 360 only supports formula fields for the Salesforce Marketing Cloud (SFMC) Enterprise Profile Attribute data stream.
Create an Object or Field-Level Access Policy in Data 360: In data governance, define who can access specific data objects or fields based on user and data attributes. Create object-level or field-level access policies for users.
Create a Real-Time Insight Using Builder: You can create real-time insights with a real-time data graph and related data model objects (DMOs). A related DMO is a DMO not defined in the real-time data graph but can be used as a real-time dimension.
Einstein Segments in Data 360: Create segments with Einstein Segment Creation generative AI. Enter a high-level description of your segment and receive a suggested segment and all its attributes. Easily draft another segment by changing the description, or edit the segment’s attributes to make adjustments. After you’re satisfied with your segment, publish and activate it.
Create an Einstein Segment in Data 360: Describe your segment to get suggested related attributes that are based on the data found in your org. To avoid bias and inaccuracies, all demographic attributes are removed. Attributes that produce fewer than 10 results for the segment population are also removed to improve the segment's accuracy.
How to Write a Good Prompt for Einstein Segments in Data 360: When creating a segment, use certain formats and language to get the best results possible. Provide descriptive names for data model objects (DMOs) and attributes to ensure that generative AI can understand them. OpenAI was trained on data that leans toward the United States, which influences Einstein’s responses. If you require a specific country, enter the name of the country in the prompt.
Create a Segment from a Data Kit: Instead of creating a segment from scratch in Data 360, use a predefined segment from a data kit. You can edit and fine-tune the segment.
Visual Segment Creation to Data 360: Segmentation enables data analysts to curate relevant portions of data from their analyses in Tableau and seamlessly transfer them as segments to Data 360.
Create a Semantic Enrichment: Add business context to improve how the Data 360 Data Q&A Agent interprets and responds to questions about your data.
Create an Activation for a DMO Using a Standard Event: When you create an activation in Data 360 using a standard event, the required attributes from the data model object (DMO) are automatically added. Conversion API (CAPI) activations to Ad Audience partner targets use DMO activation.
Create a Streaming Insight Using Builder: Use near real-time data in Data 360 by creating a streaming insight. A streaming insight focuses on data at a specific time. You can write SQL expressions to compute streaming metrics across dimensions from your real-time data sources. Use a streaming insight to perform cross-functional orchestration and optimize your data in Data 360.
Use an Alias for a Salesforce CRM Connection: As of July ‘24, an alias is required when setting up a Salesforce CRM connection. Existing connections aren't assigned an alias. The names of the data stream and data lake object are derived from the alias.
CRM Connections in a Sandbox: Connect your Salesforce org to a sandbox to ensure the safety and integrity of your production environment. When you set up CRM connections from your Salesforce org to a sandbox org, the connections are copied from the production org to the sandbox org.
CRM Connector Streaming: Batch and stream processing handle data differently. Batch mode checks the data once at scheduled intervals of 10-15 minutes, and then processes and stores it in a Data Lake Object (DLO) that you can access. Streaming mode processes data with minimal latency, and reflects changes as soon as they're made in the org.
Configure Periodic Full Refresh Interval: The periodic full refresh schedule for a Salesforce CRM connector data stream is determined by a number of variables, including a full refresh interval that you can configure to determine when the full refresh is triggered. By default, the full refresh configurable interval is disabled (set as None). You can configure its value during a bundle or multi-object data stream deployment or for an existing data stream.
Salesforce Commerce Data Bundle Mappings: Salesforce Commerce standard bundles include Business-to-Business (B2B) Commerce, Direct-to-Consumer (D2C) Commerce, and Salesforce Order Management bundles. These bundles map the data source objects to the data model objects (DMO) and establish DMO relationships that enhance segmentation results.
Financial Services Cloud Bundle Data Mappings: The Financial Services Cloud data bundle for Data Cloud helps financial services firms increase employee productivity, accelerate time to value, and deepen customer trust with every interaction. These data mappings relate to Financial Services Cloud bundle available for the Salesforce CRM connector. You can add to or customize the data mappings.
Loyalty Management Bundle Data Mappings: The Loyalty Management data lets you track and manage reward and recognition programs. Loyalty Management is a Salesforce Industry Cloud application. The data is available from Salesforce Platform objects, including Loyalty Program, Loyalty Tier, and Member Benefits. These data mappings relate to the Loyalty Management bundle available in Data 360. You can add more mappings by the bundle, and customize the data mappings. The Loyalty Management bundle is accessible only when object permissions have been set for your Loyalty Management objects.
Data Refresh Process for Extraction: Data refreshes have two phases: full refresh, which replaces all destination data, and incremental refresh, which updates data between full refresh runs. You can perform both full and incremental refresh on demand. Learn about the different refresh scenarios, and the special behavior of ContentVersion and Knowledge_kav objects.
Disable or Re-enable Salesforce CRM Connector Data Stream Fields: Disable fields in a Salesforce CRM connector data stream to stop new data ingestion, clear existing data for that field, and remove it. If you need a field again later for a standard or custom data lake object (DLO), re-enable it. Give a new field a different name than any disabled field.
Set Object Sharing Access for Salesforce CRM: Sharing access for certain objects, such as QuickTextUsage and MacroUsage, are set to Private by default. To ingest these objects into Data 360, change the organization-wide access to Public.
Salesforce Commerce Object Permissions: When you enable object and field permissions for Salesforce Commerce objects, object names can differ under the connector integration permission sets.
Store or Remove Deleted CRM Connector Data Stream Records: Store soft deleted data stream records that have been in the Salesforce Recycle Bin for up to 15 days. To store deleted data stream records, create a Deleted Record data lake object (DLO). You can use this DLO to view your stored records and use them in Data 360. For example, you can use the Deleted Record DLO to store deleted contacts outside of your Recycle Bin. You can only store deleted records of structured data streams.
Object Support for CRM Data Streams: When you create a Salesforce CRM data stream, the CRM connector doesn’t support the ingestion of all objects or object types from the connected CRM orgs. Use only supported objects and object types for your data stream.
Currency Data Type in Segmentation: You can use the currency data type to convert currency when performing aggregations and creating the required groups in segmentation filters.
Set a Currency for a Batch Data Transform: To use currency values in a data transform, set a currency. Otherwise, data remains unchanged. This setting converts data with multiple currencies to a single value. For example, if your data includes GBP and USD and you set the currency to USD, then GBP values automatically convert to USD for use in the transform.
Data 360 Features and Learning Path: Organize and unify data across Salesforce and other external data sources. After data has been ingested or connected to Data 360, use that data to drive personalization and engagement through the creation of audience segments. Also with identity resolution you can achieve a single, actionable view of your customer built on the world’s #1 customer platform.
Data 360 Profiles License Billing Considerations: Adding a Data 360 Profile or Data 360 Enterprise Profile license to a Data 360 org changes its consumption billing and adds limits for certain features.
Data 360 Profiles License Billable Profiles Calculations: In orgs with a Data 360 Profiles license, the billable number of profiles is based on whether you have active Identity Resolution rulesets. You’re billed at your overage rates for additional profiles if the number of billable profiles exceeds your entitlement.
Data 360 Profiles License Billing and Limits: Data 360 Profiles and Data 360 Enterprise Profiles licenses can be added on to Data Cloud licenses to change how a Data 360 org is billed and limited. When a Data 360 org has this license, its unified profiles, segments, data transforms, and insights are limited but don't consume credits.
Manage Data Cloud Data Access Levels for Salesforce Users: You can configure permission sets to grant Salesforce users view access to related list enrichments or other SOQL queries to Data Cloud data without granting access to Data Cloud or permission to view Data Cloud in other contexts.
Object, Field, and Record Level Security: Data governance policies in Data 360 control access to data at different levels such as object, field, and row, based on user roles, attributes, or relationships.
Data Action Targets in Data 360: The result of a data action is triggered in a data action target. The target receives the data action in near real time. You can send a data action to a target to drive event-based orchestration.
Data Actions in Data 360: Monitor data changes in data model objects and calculated insight objects in Data 360, and send the change data events to the Salesforce Platform Event, Marketing Cloud Engagement, Webhook, or Amazon Kinesis targets.
About Salesforce Data 360: Ingest, harmonize, unify, and analyze streaming and batch data with Data 360. Then use that data to unlock meaningful and intelligent experiences across Customer 360 applications and beyond.
Data 360 Architecture Strategy: As you plan your multi-org architecture, consider these data tenancy, residency details, and administrative management details. In some cases, you need more than one Data 360 instance to fulfill legal or business requirements.
White-label and Distribute a Connector: Use the metadata-based Data Cloud Connector Framework to package and white-label an existing connector, improving the discoverability of your product. To white-label a connector and distribute it, you configure a Salesforce project before publishing the managed package to AppExchange.
Considerations for Data Kits in Data 360: Review these considerations for data kit creation, publishing, and deployment to ensure successful implementation and avoid common issues.
IP Addresses Used by Data 360 Services: Data 360 uses these IP addresses to communicate with external services such as Amazon S3, Google Cloud Storage, Azure blob storage, Snowflake, or SFTP. If your security policy requires a strict network ACL (access control list), make sure to include these IP addresses to your allowlists.
Data Cloud License Billing and Limits: Adding a Data Cloud license enables Data 360 on a Salesforce org. Orgs with this license purchase consumption credits which are consumed by use of Data 360 services.
Data 360 Unified Profile Lightning Apps: Display data about unified profiles on certain pages using Lightning apps. In the Profile Explorer, the Data 360 Unified Profile Lightning apps are used to display data on the unified individual profile record page.
Data Cloud One: Streamline multi-org architecture and simplify Data 360 access with Data Cloud One. Data Cloud One allows users of connected companion orgs to access data and Data 360 features from one unified Data 360 home org.
Access Data Cloud One from a Companion Org: The Data Cloud One app is available in place of the Data 360 app on companion orgs. Use it to access Data 360 features such as profile explorer.
Tagging, Policies, and Access in Data Cloud One: Data Cloud One uses the same tagging, policy creation, and access management as the rest of Data 360, with added specifications. For both tagging and policy creation in Data Cloud One, the home org functions as a standalone org that can perform further syncing.
Metadata Ownership in Connected Orgs: An object is owned by the org in which it originated, even when it's shared with Data 360 or from Data 360 to other connected orgs. You can only edit or delete data in the org that owns it.
Considerations for Data Cloud One in a Sandbox Org: Avoid common mistakes by reviewing these considerations before setting up a Data Cloud One cluster. Create sandbox orgs from your production orgs before establishing Data Cloud One connections, or clean up Data 360 metadata before recreating your Data Cloud One cluster. You can’t use Data Cloud One to connect a sandbox org to a production org.
Version Mismatch Errors in Data Cloud One Orgs: Data Cloud One supports mixed versions between home and companion orgs, but version mismatches can cause temporary metadata syncing delays in some circumstances. Metadata syncing between a home org and its companion org can be delayed when an object's schema changes between minor Salesforce versions. You can continue using all previously synced metadata even when syncing is in a temporary error state. Version mismatch errors resolve themselves as soon as both orgs upgrade to the same version.
Remove Legacy Metadata from a Data 360 Sandbox: Data 360 sandboxes configured before August 2024 may contain legacy metadata. Use the cleanup process to remove legacy metadata from the sandbox.
Data 360 in a Sandbox: Use a Data 360 sandbox to test data ingestion, modeling, and identity resolution without affecting live customer profiles. A sandbox provides a secure testing environment by copying metadata from your production Data 360 org. Sandbox usage consumes credits from your production org.
Create a Data 360 Sandbox: Develop, test, and train on Data 360 without affecting your production org. Data 360 supports all sandbox types—Developer, Developer Pro, Partial Copy, and Full Copy—but provisions only metadata. Even when you create Partial Copy or Full Copy sandboxes, your production data stays protected. No data is replicated to Data 360 sandboxes.
Deploy Data 360 Changes Across Orgs: You can make Data 360 changes in a sandbox and then deploy them to a production org or another sandbox org. Additionally, you can deploy changes from one production org to another production by using a sandbox. Use a DevOps data kit and a change set to deploy the changes to a production org.
Data Explorer: With Data Explorer, you can validate data lake objects, data model objects, calculated insights, and data graphs to ensure that your data and formulas are accurate.
Data Extension Considerations for Data 360: If you ingest a data extension into Data 360, you should be aware of some core functionality within Marketing Cloud Engagement and how they operate within Data 360.
Data Flow: Use the power of Data 360 data in and out of Data 360. Data can be ingested and stored in Data 360 or can be connected via metadata and queried without storage.
Data Governance in Data 360: Data governance provides a robust framework for securing and managing data through the combined use of tags, classifications, user attributes, and policy-based governance. Enabling policies and other governance features improves control and ensures users have access to the right data at scale.
Data Cloud Governance Feature Availability by License: Salesforce offers a tiered licensing model for data governance within Data 360, enabling customers to scale from foundational governance to advanced, AI-driven controls. Understand which data governance features are available with which Data Cloud license.
Govern and Secure: Establish and control access, visibility, and data security with key features including data spaces, data governance, and encryption in Data 360.
Data Graph Data Structures: Understand the underlying components of a data graph so that you can create and use data graphs effectively.
Data Graph Refresh History: Use Refresh History to see a list of recent refresh events associated with a specific data graph. Refresh information varies based on the refresh schedule selected for the data graph. The refresh history tab includes the most recent refresh runs.
Data Graph Statuses: Each data graph has two separate statuses: a status for metadata and a last run status for materialized data. These statuses work together to provide you with insight into the current configuration and refresh state of a data graph. Use them to monitor the data graph's lifecycle in Data Cloud, identify any issues, and determine if the data graph is ready for querying or further updates.
Data Graph Tag Propagation: A data graph supports access and security management through tag-based controls, similar to how calculated insight objects (CIOs) are managed. Row-level security (RLS) defined on participating DMOs isn’t automatically enforced in data graphs. Depending on the level of data protection required, administrators can enforce access by using tag propagation.
Data Graphs: A data graph brings together structured data from your Data Model Objects (DMOs) in Data 360. It combines this data to create easy-to-use views of your information.
Connect Data: To get started with Data 360, you connect a data source, through either data federation or data ingestion. Data federation sets up direct connectivity with data sources. It uses a zero copy integration that eliminates the need to ingest data into Data 360. During data ingestion, data is brought into Data 360 in its original form and stored in a data lake object (DLO).
Data Federation, Ingestion, and Modeling: Data federation and data ingestion bring data from external data sources into Data 360. Data modeling is the next important phase to process data in Data 360.
Data Interpretation with Fully Qualified Keys: Fully qualified keys help to accurately interpret data ingested from different sources. Each data stream is ingested into Data 360 with its specific keys and attributes. When multiple data streams are harmonized into a single data model object (DMO), the various keys can conflict and records can have same key values. Fully qualified keys avoid conflicts by adding key qualifier fields and interpreting the data accurately.
Data Objects in Data 360: When working with Data 360, it’s helpful to know about Data 360 data objects including data lake objects (DLO) and data model objects (DMO).
Visualize Your Data with Data Lens: Data Lens provides you with an intuitive and interactive environment for in-depth data exploration and visualization. Moving beyond static reports, you can directly interact with your data to reveal hidden, valuable insights and patterns.
Data Lineage Best Practices: Applying these best practices helps maintain consistent data visibility and governance alignment across your organization.
Data Lineage Fields: Data 360 adds fields to a data object to capture data source details.
Data Mapping: Data ingested by all data streams is written to data lake objects (DLOs). After creating your data streams, you must associate your DLOs to data model objects (DMOs). Only mapped fields and objects with relationships can be used for Segmentation and Activation.
Data Mapping Best Practices: Proper data mapping in Data 360 is a key component for success. Follow some best practices for data mapping to ensure your data can be used throughout Data 360 for segmentation, insights, and identity resolution.
Data Mapper Views: Select table or visual view when mapping your data in Data 360.
Edit Data Model Object Properties: You can edit Data Model Object (DMO) properties either on the Data Model tab or on the data mapping page.
Data Model Object Relationships: A Data 360 data model object (DMO) can have a standard or custom relationship with other DMOs. The relationship can be structured as a one-to-one or many-to-one relationship. You can view the relationships and their statuses on a DMO record’s Relationships tab.
Data Model Objects (DMOs): Create, view, and manage data model objects (DMOs). DMOs are fundamental to Data 360. Familiarize yourself with the definitions, concepts, and resources related to DMOs before creating or working with them.
Data Kits: Use a data kit in Data 360 to streamline package creation and installation. You can manage all packageable Data 360 features in the data kit. You can create data stream bundles and also include data model customization and relationships.
Data Q&A Agent Considerations and Troubleshooting: Review key considerations for the Data 360 Data Q&A Agent, and troubleshoot common issues such as unexpected results, low confidence scores, or missing data.
Use the Data Q&A Agent: Ask the Data 360 Data Q&A Agent questions in natural language to explore your data, review the responses and generated queries, and refine your analysis with follow-up questions.
Data Shares: Data Shares provide easy access for external systems to use data in Data 360. With access to data lake objects (DLOs), data model objects (DMOs), and calculated insight objects (CIOs), you can build powerful analytical insights and machine learning models. Zero copy data sharing with data shares lets you share Data 360 objects without loading the data into an external system and gives you near real-time access to data.
Supported Regions for Google BigQuery Integration: Data Cloud supports cross-cloud data sharing with Google BigQuery. In cross-cloud data sharing, Google BigQuery can be in any location where data sharing is available.
Data Space Aware Objects and Features: User access to data space-aware objects and features is controlled by setting the object permissions and feature permissions in the user's permission sets. Not all objects and features are data space aware.
Data Space Mapping Filter Operators: Use these operators to define filters when mapping Data Lake Objects (DLOs) to a data space. These filters make sure that only the relevant subset of your data is accessible within a specific data space.
Replicate Metadata Across Data Spaces Using Local Data Kit Deployment: Use a local data kit deployment to replicate Data Cloud metadata and process definitions across multiple data spaces within the same sandbox org. Packaging configurations into a local data kit reduces manual effort, ensures consistency, and accelerates time to value when your data space strategy requires standardized definitions across different spaces. Standardization is especially important for deployments across geographies or brands where processes are defined centrally.
About Data Spaces: A data space is a logical partition to organize your data for profile unification, insights, and marketing in Data 360. You can segregate your data, metadata, and processes into categories, such as brand, region, or department, and then enable users to see and work on data only in the context of their category. You can also merge and analyze data in data spaces.
Associate a Permission Set with a Data Space: When Data Cloud is enabled, the default data space is associated with standard Data Cloud permission sets, except the Data Cloud Architect. To give access to other data spaces, change the data space permission set that it’s associated with or create a permission set.
Create a Data Space: Every Data Cloud org is created with a default data space. Until you create other data spaces, all Data Cloud objects are mapped to the default data space. To segregate your brand, region, or department data and services, create more data spaces.
Manage Feature Access for Data Space-Aware Data Cloud Features: By default, Data Cloud standard permission sets control access to Data Cloud features with preset object permissions. You can further restrict feature access within each data space assigned to a permission set by changing the data space’s feature permissions.
Understand Data Spaces in Companion Orgs: Metadata is synced only between a Data 360 home org and its connected companion org when the metadata is part of a data space that is shared with the companion org. Metadata syncing enables companion orgs to reference data in Data 360 objects in shared data spaces.
Secure Your Data with Data Spaces: Logically segregate your data using Data Spaces so that different brands, regions, or business units can operate independently within the same Salesforce Data 360 instance. This segregation ensures that users access only the data that belongs to the Data Space they’re assigned to. Control access to data spaces with permission sets. By default, users with standard permission sets are granted access to the default data space. Create additional permission sets and customize which data spaces they’re associated with to change how users can view, update, or manipulate data in your org’s data spaces.
Create and Publish a Standard Data Kit: Streamline package creation and the installation experience for Data 360 objects by wrapping together the metadata and relationships into a standard data kit. Use a Developer Edition org that supports managed packages to create and publish a standard data kit. Use a DevOps data kit to migrate metadata across orgs.
Data Stream Settings and Refresh Modes: With data streams, you can schedule or run jobs to ingest data from a source system. These jobs pull data from the source system into the connected data stream. The method and frequency options for data ingestion depends on the type of connector. Use the data stream settings to create or change the execution parameters of a data stream.
Data Stream Schedule in Data 360: Data streams can operate on different refresh schedules. Learn more about how and when these data streams update.
Data Streams in Data 360: Data streams are the connections and associated data ingested into Data 360.
Data Streams Tab Navigation: On the Data 360 Data Streams tab, you can view all existing data streams, including the data streams you own. You can also create a data stream.
Data Subject Rights: A customer’s data subject rights, such as those granted to them by the European Union’s General Data Protection Regulation (GDPR), relate to the storage and usage of their personal data. Data 360 offers tools to support your consumer’s data subject rights in alignment with various data protection and privacy regulations.
Data Types in Field Mappings: When mapping a data lake object (DLO) to a data model object (DMO), the data types of the DLO and DMO fields must be compatible.
Attribute Data Query Costs to Agentforce Sessions and API Requests: The CorrelationIdentifier__c field in Digital Wallet billing events connects Data 360 query credit consumption to source processes such as the Agentforce session, AI agent, or API call that triggered the query.
Data 360 Reportable Resource References: Resource references are standard usage tags that let you view your credit consumption by the feature that generated the usage. You can build custom Data 360 reports on resource types to view, filter, and sort your usage by its source.
Data Services Billable Usage Types for Data 360: Usage of certain Data 360 features impacts credit consumption. If you purchased credits before February 24, 2026, your org consumes Data Services credits for Data 360 services. Orgs purchasing or renewing after February 24, 2026 can have either Data Services or Flex Credits. To monitor your usage in these Data Services usage categories, access your org’s Digital Wallet. If your org has a Data 360 Profiles-type license, some usage types aren't consumed. For more information on how usage is billed, refer to your contract or contact your account executive.
Data Builds the Foundation for Marketing: Data 360 works together with Marketing Cloud Next to help marketers build and optimize marketing campaigns with personalization, AI, and data-driven insights.
Data to Decisions: Data 360 and Tableau Next: Tableau Next, Tableau Semantics, and Data 360 work together as a connected ecosystem to unlock the value of your data. With all three natively built into Salesforce and integrated with Agentforce, any team can discover analytical insights to make strategic decisions quickly.
Use Data 360 Data Spaces to Organize Data in Tableau: Use data spaces to segregate your data, metadata, and processes into categories, such as brand, region, or department. Set up permissions so that users can see and work on data only in the context of their brand or region.
Monitoring Data Stream Problem Records: Records that can’t be ingested into Data 360 due to data quality issues are logged as problem records and stored in a problem record data lake object (DLO). Problem records are created when data is ingested through: all structured ingest connectors, Web and Mobile App connectors, and Ingestion APIs.
Segmentation Operators in Data 360: For each attribute dragged to the segment canvas from the attribute library, select an operator to modify it and limit records that are selected. The operators that are available depend on the data type of the attribute.
Data Cloud One Support for Data Graphs: Access data graphs from a companion org through the Data 360 Companion app. From the Data Graphs tab, you can view a list of available data graphs and select any graph to explore its structure, selected fields, JSON preview, and filter settings.
Assign Data Access: For data governance, assign access to Data 360 objects using either role or attribute-based policies, or maintain an Allow All policy.
Better Together: Data and AI: AI solutions grounded in trusted, accurate, and relevant data are the most helpful agents. Data 360 helps you consolidate disparate data sources into unified profiles of customers or accounts, which supercharge AI and Agentforce.
Billing Impact of Data 360–Powered Features: Some Salesforce CRM features are powered by Data 360 data. These features impact Data Services Credit consumption by increasing usage of associated processes in Data 360 (such as querying, storage, processing, and analytics). Before configuring Data 360-powered features, review feature and usage documentation. Contact your account executive to understand how features impact Data Services Credit consumption in Data 360.
Data 360 Editions and Licenses: Data 360 is available in several supported Salesforce editions. Some Data 360 features also require additional licenses.
Unlock Hidden Insights with Tableau: After you've combined, unified, and harmonized your data in Data 360, it's now ready for you to analyze. Use Tableau to find useful and actionable insights that can drive business success and create a personalized experience for your customers. Data 360, Tableau, and Salesforce 360 apps work together to consolidate your data and create a single customer record. Unlock hidden insights, and make decisions based on all your customer data.
Data 360 and Tableau Use Cases: Tableau helps you explore, analyze, and find hidden insights in your data. The Tableau to Data Cloud connectors use the Hyper query engine to provide fast processing. You can explore your Data 360data in near real time enabling quick and accurate decision-making. These example use cases illustrate how Data 360and Tableau work together to help you achieve your business goals and deliver a personalized experience for your customers.
About Data 360 Releases: Data 360 releases new features on a monthly basis, often outside of the Salesforce three major releases. Updates are generally compatible unless special advance notifications are provided.
Data 360 Resources: In addition to help content, find links to videos, Trailhead, Developer, and other content available to help learn and implement Data 360.
Delete a Data Graph: In Data 360, when you delete a data graph, the record’s DMO schema structure and the queryable JSON blob are also deleted. Deleting a data graph removes it and all related data. You can’t undo this action or retrieve a deleted data graph.
Delete a Data Stream: You can delete a data stream in Data 360 if the connection is no longer needed.
Manage Data 360 Objects and Field Mappings: Edit or remove objects and field mappings associated with a data model object (DMO) or data lake object (DLO). Manage mappings and objects to ensure data accuracy and maintain the integrity of your data model.
Delete a Data Share: In Data Cloud you can delete an unlinked data share that's no longer needed.
Delete Ingested Records in Data 360: You can delete data ingested into Data 360. Your data stream must have an upsert refresh mode. The deletion process depends on the type of connector that you’re using.
Delete Records by Using a Delete File: To delete data records ingested by using the Amazon Simple Storage Service (Amazon S3), Google Cloud Storage (GCS), Microsoft Azure, Ingestion API, or Secure File Transfer Protocol (SFTP) connector, create a delete file. Create the file in CSV or Parquet file format. S3, GCS, and Azure data streams support Parquet file ingestion.
Data Drift in High-Frequency Data Graph Refreshes: When you schedule a data graph to refresh more frequently than once per day, you can experience data drift. Data drift occurs when rapid refresh cycles don’t fully capture or reconcile updates from the source system, causing temporary inconsistencies in the data graph.
Control Data Graph Access: Data graph governance in Data 360 controls how you access data by enforcing permissions at the object and field levels, so you see only the data you’re permitted to access. Governance policies are defined using tags inherited from source data model objects (DMOs) and applied to the data graph.
Special Handling of DMO Relationships in Data Graphs: Data model object (DMO) relationships can affect how data is processed and reflected in data graphs, particularly in real-time data graphs. Review DMO relationship types when designing data graphs to make sure that processing behavior aligns with your expected update patterns.
Reapply Tags for a Data Graph: After updating or removing tags at the DMO level, reapply tags to synchronize a data graph with the latest tag configuration from its source DMOs. When you reapply tags, all existing tags on the data graph are removed and replaced with the current tags from source DMOs and fields.
Disable Source Fields: After your schema is created you can disable source fields as needed.
Deactivate or Delete Data Q&A Agent: Control whether the Data 360 Data Q&A Agent is active and accessible in your org, or remove it entirely.
Disable a Data Stream Field: If a field is no longer required in a data stream, you can disable it. Disabling a field removes it from the data stream, and no new data is ingested. You can’t assign the name of a disabled field to a new field.
Deactivated Segment: If a segment is no longer needed, it can be deactivated through Data 360 and applies to all chosen targets. A deactivated segment no longer publishes, can’t be chosen for an activation, and can’t be re-enabled. If you plan to use the segment again, stop the publish schedule instead.
Disconnect a B2C Commerce Instance Connection: You can delete a connection to your B2C Commerce instance. Removing the connection stops data ingestion from your B2C Commerce instance to Data 360.
Disconnect a Data Source: If needed, a data source can be disconnected and removed from Data 360. This disconnect removes all provided data.
Disconnect a Salesforce CRM Connection: When you no longer need a connection, you can delete it. After you remove a standard Salesforce CRM connection, Data 360 stops ingesting data from the connected org and deletes all the data it ingested from that org, except trust files. Data ingested from the org is removed from unified profiles the next time that identity resolution runs.
Disconnect an Omnichannel Inventory Connection: You can delete a connection to Omnichannel Inventory if you don't need it. Removing the connection stops Data 360 from using your inventory as a data source and data action target.
Fully Qualified Keys in Data Objects: Data 360 adds key qualifier fields to data lake objects (DLOs) and data model objects (DMOs). Key qualifier fields can also be added when you create object relationships and object mappings between DLOs and DMOs.
Activation for Data 360 Data Model Objects: Use data from various sources to build complete customer profiles by activating a data model object (DMO). This feature presents data in a readily actionable format and facilitates personalized customer experiences. Data 360 offers two methods for DMO activation: streaming and batch, with each designed for different use cases and destinations.
Honor “Do Not Share” and "Do Not Sell" Requests in Data 360: Understand how to comply with the California Privacy Rights Act (CPRA) and other laws that give consumers the right to restrict sharing their data with or selling it to third parties.
Docling Parser: The Docling parser is a third-party intelligent document parsing framework that brings together multiple open-source models for layout understanding and table extraction. To enhance responses from information available in images and complex tables, combine the Docling parser with image processing by using Large Language Models (LLMs).
Document AI: Document AI in Data 360 uses large language models (LLMs) to extract structured data from unstructured documents such as invoices, resumes, lab reports, and purchase orders.
Billing Considerations for Document AI: Document AI uses Data 360 to extract structured data from unstructured documents and Flex Credits to call generative AI. Use of Document AI impacts the consumption of credits in these usage types.
Manage Document AI Configurations: View, edit, publish, export, and rebuild Document AI configurations. Package Document AI configurations in data kits. Save Document AI configurations as drafts, and refine your extraction schemas over multiple sessions.
Create a Document Schema Configuration Using Auto-Extraction: Configure a document schema to represent data extracted from your source files into a Data Lake Object (DLO). Use the auto-extraction option in the document schema builder to have an LLM define the structure of the document schema. The builder auto-populates all fields, tables, and columns.
Create a Document Schema Configuration Manually: Configure a document schema to represent data extracted from your source files into a Data Lake Object (DLO). Use the document schema builder to manually define the structure of the document schema by adding fields and tables.
Document AI Example Schemas: The following prompts are JSON-formatted schemas that you can use when creating an Document AI configuration in Data 360. Use these examples as starting points, or modify them for your specific use cases.
Dynamic Data Masking Policies in Data 360: Dynamic data masking conceals sensitive data without altering its usability, accuracy, or relationships. It applies to all Data 360 objects and fields, regardless of the access method.
Edit an Insight: If you created an insight using SQL or Insight Builder, you can change its attributes or logic and reuse it.
Edit a Calculated Insight Schedule: In Data 360, you can change the schedule of calculated insights that are created with a SQL and the builder.
Edit a Data Graph: You can change a data graph’s name and description, associate new data model objects (DMO), and make additive changes to the existing primary and associated DMOs.
Edit or Delete a Data Space in Data Cloud: You can change a data space’s name and description, but you can’t modify its prefix. You can delete a data space that you no longer need. However, you can’t delete the default data space.
Edit a Data Stream: Edit a data stream in Data 360 for the Amazon Simple Storage Service (Amazon S3), Google Cloud Storage (GCS), Azure, Marketing Cloud Engagement, and Secure File Transfer Protocol (SFTP) connectors. Changing the name of a data stream doesn’t change the name of the data lake object (DLO) that the data stream uses.
Edit or Delete a Classification in Data 360: Edit the name and description of a classification, manage its associated tags, or delete it if no longer needed. You can't delete classifications that are used in policies. This includes default classifications provided by Data 360.
Edit or Delete a Custom Taxonomy in Data 360: You can change the taxonomy's name and description. You can’t change the taxonomy type or its API name. You can't delete taxonomies that are used in policies. This includes default taxonomies provided by Data 360.
Data Explorer Columns and Filters: You can personalize your data view in Data Explorer to show only the most relevant data for your selected object in Data 360. You can show up to 10 columns, which are organized alphabetically by default. You can sort the column data in ascending or descending order.
Edit a Data Share: You can add or remove the data lake objects, data model objects, and calculated insights objects in a data share. When you add a Data 360 object to a data share, it’s accessible through the linked data share target. Similarly, when you remove a Data 360object, it’s inaccessible through the linked data share target.
Edit a Segment: After you create a segment in Data 360, you can edit its rules or properties to provide updates or change the purpose. Changing the segment name doesn’t change the segment API name.
Add or Remove B2C Commerce Site Selections: In Data 360, you can change which sites B2C Commerce ingests data from after you have set up a connection. Make sure that there’s a B2C Commerce connection before editing the sites.
Source Object Schema Modifications: You can modify Data 360 source object schema on the Data Stream details page. All changes are applied to future data imports. To apply logic retroactively, pull from the source and ingest into Data 360.
Edit or Delete a Tag in Data 360: You can delete a tag or edit the tag name, API name, and classifications. You can't delete tags that are used in policies. This includes default tags provided by Data Cloud.
AI and Data Usage in Data 360: AI is built on data. Review this table to find out which AI models Data 360 features support and the implications of turning off access to a model.
Use Automatic Tag Suggestions in Data 360: Data 360 suggests tags by analyzing the metadata of objects and fields, such as their names and types. This helps streamline tagging by providing intelligent, context-aware suggestions without scanning the actual data.
Grant or Remove Access to Data Explorer or Profile Explorer: Data Explorer and Profile Explorer are enabled by default for Data 360 admins and data aware specialists. To make them available to users in other roles, or to remove access from roles that have it by default, create custom permission sets.
Enable Data 360 Data Q&A Agent: Turn on the Data 360 Data Q&A Agent and Metadata Knowledge Graph and Semantic Enrichments features to start asking natural language questions about your data.
Enable Data 360 in Tableau: Using the Data 360 connector, you can discover and analyze unified profiles and other customer insights.
Salesforce CRM Permissions: When you connect to an org, the Data Cloud Salesforce Connector permission set is created to allow access to Data 360 objects and fields. You can edit the Data Cloud Salesforce Connector permission set to meet your organization’s needs.
Engagement Data Model: The Customer 360 Data Model engagement subject defines interactions with a party. Example interactions could be speaking to a customer over the phone or receiving a customer email.
Enrich Your Org with 360 Data and Insights: Add cleaned and harmonized data from across your organization to your org through Data 360 enrichments. Create related list and copy field enrichments for your record pages with data from Data 360.
Enrichment Considerations: Learn vital information about security, data storage, cost, deletion, and other considerations before you create enrichments to use Data 360 data in your org.
Data Ethics: Earning and keeping customer trust increasingly means adopting data ethics best practices when using Data 360.
Segmentation Filter Examples: Review examples of filters for various types of segmentation use cases. Use these examples as a starting point for your segments, which you can tailor by adding more filters to fit your campaigns.
Explore and Optimize: Optimize and explore your unified Data 360 data. Use Data Explorer and the Query Editor to inspect and interrogate your data. Build Data Graphs to precompute the fields your real-time apps need most. Add Secondary Indexes to speed up high-volume queries. Use Profile Explorer to validate that identity resolution produced the unified customer views your business expects.
Create Custom Metadata for Your External Activation Platform: Create custom metadata for your external activation platform before you can package your platform for use by Data 360 admins. This applies only to platforms created using the manual workflow.
Enable Data 360 Features: You can enable Data 360 features using the Feature Manager. This screen is visible only when there are one or more features to enable or disable.
File Name Examples for File Storage Activation: Based on the values configured for the fields of your file storage activation target, here are some examples of file names for Data 360 external activations.
View Filter Population Breakdown: After you start a report, open it to see how each Include and Exclude filter changes the audience, from the first filter through the final count.
List Which Pages Synced Successfully: View the details of the pages and attachments that were successfully ingested by the Web Content (Crawler or Sitemap) connector.
Find Your Instance and Endpoint Information: Data 360 runs on both Salesforce's first-party infrastructure and public cloud infrastructure, referred to as a functional domain (FD). Use these steps to find your Data 360 instance so that you can understand the impact when there’s an unforeseen incident or if you have a support case where additional information is required.
Flex Credits Billable Usage Types for Data 360: Usage of certain Data 360 features impacts credit consumption. To monitor your usage in Flex Credits usage categories, access your org’s Digital Wallet. If your org has a Data 360 Profiles-type license, some usage types aren't consumed. For more information on how usage is billed, refer to your contract or contact your account executive.
Create a Formula Field: When you create a Data 360 data stream, you can choose to generate more fields. These supplemental fields can be hard-coded or derived from other fields in the data stream.
Formulas, Operators, and Calculated Insights: Create audiences in different ways and at different stages of your workflow using formulas, operators, and calculated insights in Data 360. During ingestion, you can use formulas to perform operations on row-based data used downstream. For segmentation, audiences are created with segment operators.
Full Refresh: Learn more about the Full Refresh extract method in Data 360 and when to use it.
Fully Qualified Keys: Use Fully Qualified Keys (FQK) to avoid key conflicts when data from different sources are harmonized in the Data 360 data model.
Follow Steps to Ingest Google Analytics into Data 360: Connect your CRM and Google Analytics data in Data 360 using the following configurations. This multi-step solution includes multiple roles and permission sets to complete steps in Data 360, Sales Cloud or Service Cloud, CRM Analytics, and Google Analytics.
View Activations in Data Explorer: View your activation data for a specific activation target in Data 360. You can view results up to the past 30 days.
Use Outputs from Predictive Models: There are many ways in which you can consume predictions, prescriptions, and top factors returned from predictive models.
Create a Calculated Insight Using SQL: Write SQL expressions to create your metrics and dimensions from mapped objects and fields. You can create a calculated insight in Data 360 using SQL.
Get Started with Data Governance: Before using Data Governance features, it's important to review the prerequisites and the steps required to enable governance across your organization’s data.
Google Audience Insights in Activation: Delve into your customers’ unique characteristics, interests, and behaviors with insights from a Google audience report within Data Cloud. After you activate a segment to Google, an audience report is generated in 5–7 days. The report provides insights in terms of demographics and customers who are actively researching products and their affinity groups based on their lifestyle, long-term buying habits, and interests.
Using an Existing Data Lake Object to Create a Data Stream: You can use an existing data lake object (DLO) when creating an S3, Google Cloud Storage, or SFTP data stream. The data stream that initially created the DLO defines the characteristics, such as the primary key, event date time, and record modified field. You can convey how the new data stream’s fields conform to the existing DLO fields.
Guidelines and Limits for Automations: You can configure automations based on your requirements using the Data 360 actions. When planning an automation, make sure to review the guidelines and limits. Throttling limits the maximum number of API requests triggered within a specified time period.
Content and Knowledge Harmonization: Use Harmonization to transform knowledge articles and unstructured content ingested into Data 360. Harmonization normalizes and enriches content, resulting in a single harmonized schema suited for consistent rendering in the Content Viewer. Access the viewer through Data 360's Data Explorer or in Service Cloud's Enterprise Knowledge component.
Hybrid Search Autodrop Results: Dynamically filter out results that show a sharp decrease in relevance scores to increase the precision of search results for your prompts and RAG use cases. Data 360 hybrid search autodrop is a method by which you can filter out irrelevant search results that can mislead upstream applications like prompt builder using those results.
Hybrid Search Best Practices: Enhance your hybrid search retrieval and search relevance by chunking the correct fields, selecting pre-filter fields, and adding ranking factors.
Hybrid Search Fusion Ranking: Ranking reorders the search results so that the most relevant results to a user query are at the top. Data 360 Hybrid search retrieves results from two indexes—keyword and vector— based on the similarity with the query and then merges (fuses) the results. The ranking model then reranks the merged results.
Hybrid Search: To provide relevant information to an LLM with gen AI applications and generate the correct response, requires both vector search and keyword search. Vector search understands semantic similarities and context, but lacks specific domain vocabulary. Keyword search excels at lexical similarity but not at semantic similarity. In Data 360, hybrid search combines the strengths of semantically aware vector search with the precision and speed of keyword search.
Create a Hybrid Search Index with Advanced Setup: Configure a hybrid search index for a data model object (DMO) or an unstructured data model object (UDMO) to provide relevant information to generative AI applications. Data 360 breaks up the referenced data into semantically related chunks and generates searchable vectors, and then generates a vector index and a keyword index.
Hybrid Search Query Expressions: Enhance your hybrid search queries with pre-filtering fields and join expressions to access fields.
Influence Hybrid Search Relevance Ranking: Increase the accuracy of search index query results and improve the quality of RAG applications by sending relevance-specific parameters. Using SQL, you can send relevance configuration settings for supported fusion ranker models and improve the result relevance for your specific use case.
About Identity Resolution: Identity resolution is the processing engine that generates unified profiles from source profile data stored in Data 360. Unified profiles act as a system of reference to unlock our source data. Understand how and when source profiles are processed and what you can do with unified profile output.
Configure Real-Time Matching in Data 360: You can configure any ruleset for real-time matching after its first ruleset run is completed by creating a real-time data graph based on the ruleset’s output unified profile data model object.
Delete an Identity Resolution Ruleset: Deleting a ruleset permanently removes all unified customer data, eliminates dependencies on data model objects, stops all processing of the ruleset, and deletes the history of previous runs. In some cases, it’s better to update the match or reconciliation rules instead of deleting the ruleset.
Filter Source Records for Identity Resolution: Control which source records enter the identity resolution processing pipeline by defining filter conditions on your ruleset's primary data model object. Filters act as a preprocessing gate, so that only records matching your conditions enter the unification pipeline. Using filters helps keep unified profiles clean and reduces credit consumption.
Multiple Identity Resolution Rulesets in Segments: Compare the impact of different identity resolution rulesets for the same entity on segment population counts. After using identity resolution to create different rulesets for the same entity, entities and attributes created by both rulesets are available in segmentation. Use attributes from both rulesets to validate, or A/B test, different population counts. Entities and attributes names include the Ruleset ID at the end when you select what entity to Segment On or add attributes in the attribute library.
Identity Resolution Match Rule Terminology: Understand the roles that match rules, match criteria, and match methods play in identity resolution rulesets. Adding, adjusting, and removing match rules, match criteria, and match methods can change the consolidation rate of the overall ruleset.
Match Rules for Accounts: Identity resolution provides two preconfigured default match rules based on Account Name combined with other common data. Default match rules allow you to quickly select one of the four common match rule configurations. You can combine default and custom rules in a ruleset to create multiple ways for records to match. To get the best results from account matching, we recommend using match rules that match by account name and geographical location. The specific address data you select impacts the quality and precision of your matches.
Tips for Improving Account Matching: Creating rules with overly broad criteria can result in poor account matching. Consider these guidelines when setting up identity resolution match rules for unifying accounts.
Match Rules for Households: You can add only one match rule to a ruleset when configuring household matching in Data 360 identity resolution. Each unified household contains one or more individuals. We recommend matching individuals into households based on either last name and address or on party identifier.
Match Rules for Individuals: Identity resolution provides four preconfigured default match rules based on Individual First Name and Individual Last Name combined with other common data. You can combine default and custom rules in a ruleset to create multiple ways for records to match.
Scheduled and Real-Time Matching in Identity Resolution: Identity resolution can perform two types of ruleset runs: scheduled or real-time. They differ in how and when the two types of ruleset runs are performed, and how they interact with other features.
Optimize Identity Resolution: Consider these best practices and troubleshooting tips for the data you bring in and send out of Data 360 to get the most of your unified profiles.
Processing Frequency for Identity Resolution: Identity resolution processes rulesets as frequently as every 60 minutes to 24 hours, depending on the data source. Each data source is processed on its own timeline according to when data is received and how many changes accumulate.
Real-Time Identity Resolution: Real-time identity resolution identifies customers in milliseconds so you can personalize your site or communications during an engagement.
Identity Resolution Rulesets: Rulesets contain match and reconciliation rules that tell the customer data platform how to link multiple sources of data into a unified profile. Unified profile information is stored in data model objects created by the ruleset.
Create Identity Resolution Rulesets: Get started with identity resolution by setting up a ruleset to unify accounts, individuals, leads, or households. The primary data model object for your ruleset determines which records are unified. Match and reconciliation rules you add to your ruleset after creating it guide how records are matched.
Anonymous and Known Profiles in Identity Resolution: A unified individual profile is considered known if any source profile was designated as known in the source data. A unified individual profile created only from anonymous source profiles is considered an anonymous unified profile. Account profiles are always known profiles.
Explore Individual Profile Unification in Tableau: Dig into your unification data visually with the Identity Resolution Tableau accelerator. Pre-configured charts provide at-a-glance information such as how many profiles were ingested from each source and how they contributed to your unified individual profiles.
Identity Resolution Terminology: Understand what terms like rulesets and unified objects mean when used in the context of identity resolution.
Profile Unification Types: Identity resolution in Data 360 offers several different ways to unify your records. The unification type you select determines what types of objects you can base your unified profiles on, the output objects created by the ruleset, and the speed at which records can be unified.
Unify Individuals or Accounts with an External Link: Matching on an external link lets you unify profiles that have been identified as matching in an external data source. Your source data must include an ID that identifies the matched records.
Group Individuals into Households: Household matching lets you consolidate unified individuals into groups of individuals called households. Households use the Unified Individual object as the primary data model object for the ruleset and output to the Unified Household object.
Unify Individuals or Accounts: Identity resolution in Data 360 offers several different ways to unify your records. The unification type you select determines what types of objects you can base your unified profiles on, the output objects created by the ruleset, and the speed at which records can be unified.
Match Leads to Accounts: Cross object matching lets you match your leads to existing accounts so that you can identify customers you already have a relationship with.
Unify Individuals or Accounts Based on Party Identifier: Party identifier refers to the exact match of the Identification Number, Identification Name, and Party Identification Type fields in the Party Identification object. This type of match is useful for matching unique identifiers, such as government ID, first-party cookies, external MDM systems, and vendor-specific IDs.
Unify Individual Profiles in Real Time: Real-time profile unification reviews profile information immediately and completes real-time profile matching in milliseconds. Real-time matching matches information about an active end user to a unified profile so that you can personalize your app or website within milliseconds.
Unify Source Profiles: Use identity resolution in Data 360 to consolidate data from different sources into comprehensive views of your customers and accounts. Identity resolution uses matching and reconciliation rules to link data about people or accounts into unified profiles. Identity resolution creates complete, actionable views of your customers but doesn’t create golden records and isn’t a master data management (MDM) system. Each unified profile contains all the unique contact point values from all sources. You choose which view of your customers to use based on each business use case you want to solve.
Data 360 and Cross-Cloud Implementation Guides: Use printable PDF guides to set up, administer, and configure Data 360 features and applications. PDF guides are updated regularly, so check the date of any downloaded and saved guides before beginning your implementation.
Delta Extract: Marketing Cloud Engagement supports two types of incremental extracts: delta extract by date and by number.
A Solid Data Foundation: Data 360 and Informatica IDMC: Salesforce offers foundational data management capabilities with Data 360 and Informatica’s Intelligent Data Management Cloud (IDMC). IDMC offers advanced data quality, lineage, governance, and Master Data Management (MDM) capabilities. Data 360 connects, harmonizes, and unifies data into unified profiles to provide a secure and trusted data foundation. You can use Data 360 and IDMC in parallel or individually at different stages in your project.
Ingest Google Analytics into Data Cloud: To gain insight into your customer interactions, connect Sales or Service Cloud and Google Analytics data in Salesforce Data Cloud. Enrich your customer information with web engagement data to better segment key audiences and personalize customer interactions.
Ingest Hierarchical Data: Bring Salesforce CRM organizational hierarchies such as Role, Manager, and Territory into Data 360. After ingestion, you can reference these hierarchies in hierarchy-based record-level security (RLS) policies.
Ingest and Map Data from Sales Cloud: In Data Cloud, you can connect to many data sources. Many of the Salesforce connectors have a starter data bundle to help you quickly set up a data streams, create data lake objects, and map to the data model automatically. However, you must map custom objects and some required fields to the Customer 360 Data Model to use Identity Resolution.
Ingest and Map Data from Service Cloud: In Data Cloud, you can connect to many data sources. Many of the Salesforce connectors have a starter data bundle to help you quickly set up a data streams, create data lake objects, and map to the data model automatically. However, you must map custom objects and some required fields to the Customer 360 Data Model to use Identity Resolution.
Ingesting Consent Preferences: Ingest consent preferences from wherever you’re storing them using connectors. Common storage locations include data extensions (Marketing Cloud Engagement Connector), Sales or Service Cloud standard or custom objects (Salesforce CRM Connector), or external sources (Cloud Storage Connector).
Categorize Anonymous and Known Profiles: During ingestion, a source profile is categorized as either anonymous or known based on the value in the Is Anonymous field on the Individual DMO in data streams. A source profile without that field mapped is always considered known. Add the mapping to differentiate between known and anonymous profiles.
Track Calculated Insight History: When you start history tracking on a calculated insight, Data 360 creates a calculated history insight.
Build Data Insights with the User Interface: Create metrics in Data 360 with Visual Insight Builder. Insight Builder is a business-analyst focused and user-friendly metrics authoring tool that can be found in the Calculated Insights tab in Data 360.
Create a Shopping Cart Insight: Create a shopping cart insight to understand customer interactions and optimize their journey. This process involves selecting an individual data model, choosing real-time or batch processing, and defining event values. Gain valuable insights into shopping trends and improve conversion rates.
Define Relative Time-Window Metrics: Get real-time Data Cloud insights with metrics based on rolling time windows. Determine metrics using relative timeframes, such as a specified number of past seconds, minutes, or days.
Shopping Cart Insight Template: The Data 360 shopping cart insight template creates a calculated or real-time insight based on actions in your customer’s shopping cart. Analyze customer interactions with their shopping cart, from items added to purchases completed or abandoned. Shopping cart insights provide a view of the customer's current shopping cart state.
Data Types for Insights SQL: When you define a calculated or streaming insight with SQL in Data 360, use the supported data types for your measures and dimensions. Measures are the numerical values you aggregate; dimensions are the qualitative fields you group by.
Create Insights from Templates: Create Data 360 Insights efficiently using templates. To simplify insight creation, templates validate the required Data 360 DMOs, ask simple configuration questions, and set up your new insight.
Enhance Data with Insights: With insight features, you can define and calculate multidimensional metrics from your entire digital state stored in Data 360.
Authoring Considerations for Insights: With strategic planning your Data 360 insights deliver accurate, high-performance data visualizations for your business users. You can optimize your data processing with complex joins, relative time-windows, and advanced metric layering.
Customer Data Subject Rights in Insights: A customer’s data subject rights relate to the storage and use of their personal data. When an insight uses the individual data model object (DMO), Data 360 automatically applies your customers’ data subject rights requests to the insight output.
Create a Data Stream from a Data Kit: When you create a data stream from a data kit in your target org, the data model objects are available automatically. When adding a data model to a data kit, a custom data model is added as is. If you’re adding a standard data model, only the custom mappings and custom fields are packaged.
Install Standard Data Bundles Powered by Data Kits: After you create your Data 360 instance and set up your Salesforce CRM connection, you can install standard data bundles that are powered by data kits to deploy data within Data 360.
Integrating Data 360 Data Actions with Flow: Data 360 data actions can send streaming events and insights to Salesforce Flow to drive an orchestration. An orchestration uses stages, steps, and decisions to organize complex business processes.
Intelligent Context in Data 360: Process unstructured or structured data and create search index configurations tailored to specific business contexts by interacting with prompts in the Intelligent Context workspace.
Create an Intelligent Context Configuration: Creating an Intelligent Context configuration includes these key steps: uploading sample documents for evaluation, configuring the Intelligent Context configuration, testing the results, iterating on the configuration, and publishing to a search index.
Marketing Cloud Personalization Bundle Mappings: When creating a data stream in Data Cloud using a Marketing Cloud Personalization (formerly Interaction Studio) starter bundle, the system creates standard mappings for you.
Marketing Cloud Personalization Connector: With Marketing Cloud Personalization (formerly Interaction Studio) data in Data 360, you can ingest user profiles and behavioral events to enrich the customer profile and increase personalization. The connector also lets you use Marketing Cloud Personalization as an activation target.
Issues with Using Cyclic Paths in Segment Attributes: When you choose a DMO more than one time while selecting a related attribute, you create a cyclic path. Cyclic paths are relationships that use cyclic behavior (a→b→a). Cyclic paths degrade segment performance and can lead to count or publish failures. To avoid these issues, Data 360 prevents you from creating cyclic paths through Segment Builder by default.
Join Data in a Streaming Transform: Joins enable data augmentation by combining data from two separate streams based on a primary key. In streaming data transformations, `INNER JOIN` and `LEFT JOIN` clauses in SQL statements are used to combine data from two distinct streams. A join expression consists of two objects: one identifies the object that triggers the streaming transform on update, and the other specifies the object used to evaluate the join.
VPC Interface Endpoints for Amazon Kinesis Connection: Data Cloud uses the VPC Endpoints listed here to communicate with Amazon Kinesis. If your security policy requires a strict network ACL (access control list), make sure to include these endpoints to your allowlists.
Migrate Users from a Customer Data Platform License to a Data Cloud License: When you add a Data Cloud license to an org that was previously operating Data 360 under a Customer Data Platform license, the permission sets granted by the Customer Data Platform license no longer grants access to Data 360. To provide users continued access to Data 360, you must assign the permission sets associated with the Data Cloud license to your users.
Data 360 Licenses, Billing, and Limits: How you pay for data services provided by Data 360 depends on how your Data 360 org is licensed. Your licenses determine both billing method and limits for your org. We recommend working with your account exec before buying Data 360 or when your contract renews to ensure your license is the best choice based on your usage and growth potential.
Data 360 Lifecycle Management: To manage your Data 360 lifecycle you can renew your license or delete your Data 360 instance to reset the org. To keep your Data 360 instance, renew your license within the 120-day grace period. To set up a new Data 360 instance with an active license, use the Data 360 cleanup process to delete its associated data, metadata, and configurations. You can also use the cleanup process to connect your org as a Data Cloud One companion to an existing Data 360 instance.
Manage Data Returned in Data Graphs: Use conditions and sort and limit to manage the data that’s included in a data graph. Conditions control which records are included based on conditions, while child record limits control how many related child records are returned. Together, these features reduce storage, improve performance, and include only the most relevant data. The sort and limit and condition filters can be applied together for better performance.
Data 360 Limits and Guidelines: Guidelines define best practice recommendations to optimize your adoption of Data 360 for best performance. Limits are boundaries beyond which features are unavailable, performance is throttled, or usage billing charges are applied. Since Data 360 is built to scale, some limits can be adjusted to meet your business needs. Work with your account executive to find a solution that meets your goals.
Customer Data Platform Limits and Guidelines: Guidelines define best practice recommendations to optimize the adoption of Data 360 by Customer Data Platform customers for best performance. Limits are boundaries beyond which features are unavailable, performance is throttled, or usage billing charges are applied. This article is about the legacy Customer Data Platform license, which is no longer available for purchase.
Data 360 Profiles License Base License Limits: Limits in this document represent the base limits of a single Data 360 Profiles or Data 360 Enterprise Profiles license. Some limits can be raised by purchasing multiple copies of the license or buying add-on licenses.
Link or Unlink Data Shares and Data Share Targets: Link data shares with a data share target to make them available to an external system. You can also unlink a data share from a data share target.
LLM-based Parsing and Preprocessing: Use LLM-based parsing and preprocessing to extract and prepare text, metadata, tables, and images from unstructured documents for chunking.
Configurations: Use these configuration steps to welcome new loyalty members to your program.
Design Considerations: Keep these design considerations in mind when you welcome new loyalty members to your program.
Workflow: Learn how data flows through the configurations when you welcome new loyalty members to your program.
Loyalty Data Model: The Customer 360 Data Model loyalty subject area tracks and manages reward and recognition programs.
Manage a Communication Capping Configuration: Each organization is limited to one active communication capping configuration in Data Cloud. If you want to modify the dimensions in a communication capping configuration, create another communication capping configuration. To create another one, you must disable the active communication capping configuration.
Manage Data Shares: Create, edit, clone, or delete data shares in Data 360. You can also link or unlink a data share to a data share target.
Manage Data 360 Segment Schedules: The segment scheduling options provide you with granular control over segment publish frequency. You can schedule your segments to run at the frequency that best suits your needs.
Manage Semantic Enrichments: Edit, deactivate, or delete semantic enrichments as your data model and business rules evolve.
Data 360 Governance Permissions: Review the permissions and permission sets required to effectively use data governance features in Data 360.
Managing Insights: Manage calculated insights to drive data-informed decisions with precision. Configure history tracking to monitor long-term metric trends and maintain data integrity. You can schedule automated processing, manually trigger updates, and validate results to ensure your team accesses accurate analytics. Efficiently organize your insight using the list view to edit or delete records as your business needs evolve.
Map Data Model Objects: Data mapping relates Data Lake Object (DLO) fields to Data Model Object (DMO) fields, paving the way for unifying your source data into a single standardized Customer 360 data model.
Map Data Model Objects in a Data Space: Create connections between data lake objects (DLOs) in a data space and data model objects (DMOs). You can use only mapped fields and objects with relationships for segmentation and activation.
Ingest and Model Digital Identifiers for JSON-based External Activation Targets: Ingest and model your digital identifier data by using the standard ContactPointDigitalId data model object (DMO). Independent Software Vendors (ISV) use the activation toolkit to build activations that query this DMO to resolve digital identities across digital media platforms.
Mapping Org IDs: The internal organization ID is commonly referred to as the Salesforce.com Organization ID or simply the Org ID. It’s a reference ID to the business unit or other internal organization that owns the data record and is a standard audit field for every standard DMO. However, this reference ID value is not populated by the CRM connector.
About the Data 360 Marketing Agent: The Data 360 Marketing Agent helps you with segmentation in Data 360. Describe what you want in natural language, and the agent works in the Agentforce panel to complete the request.
Add Filters to a Segment: With a segment open on the segment canvas, the Data 360 Marketing Agent adds filters to that segment instead of creating a new one. Work on the canvas whenever you can. The canvas gives you better feedback and lets you validate the segment as the agent builds it.
Build a Segment by Describing It: Describe the audience you want in plain language, and the Data 360 Marketing Agent works out the filter criteria and creates a segment for you. The agent builds standard and nested segments, and filters can include calculated insights.
Marketing Cloud Engagement Data Action Targets: Use data and insights to drive business metrics and reduce delay in interacting with customers with Journey Builder API events and transactional emails sent in Marketing Cloud Engagement. Automate and personalize transactions by combining Data 360 with Engagement. You can automate simple operational tasks with data-triggered email messages and journeys, maximize conversions, and minimize customer drop-off rates.
Marketing Cloud Engagement Refresh Mode: When ingesting data into Data 360 from Marketing Cloud Engagement, the refresh mode is either upsert or full refresh. Each handles your data differently.
Identity Resolution Match Rules: Match rules tell Data 360 which profiles to unify during the identity resolution process. Each match rule contains one or more criteria. Profiles are matched when all criteria within a match rule are satisfied.
Default and Custom Match Rules: Identity resolution provides out-of-the-box, default match rules for different types of matching. Use custom match rules to resolve use cases that aren’t covered by default rules. Developing balanced, effective sets of match rules across different types of criteria improves consolidation rates for individuals and accounts.
Identity Resolution Match Methods: Match rule methods determine how source data is transformed for comparison during matching. Changing the match method on a field typically changes the consolidation rate based on that field.
Marketing Cloud Engagement Data Bundle Information: When ingesting data from Marketing Cloud Engagement, Data 360 creates data streams and data lake objects (DLOs) for each starter bundle, along with schema and mappings to data model objects (DMOs).
Marketing Cloud Account Engagement Connector: Ingest data from Account Engagement into Data 360 to drive calculated insights and build segments based on marketing asset engagement.
Marketing Cloud Account Engagement Data Mappings: A data stream maps data model objects (DMOs) in Data Cloud to objects in your Salesforce org. The Marketing Cloud Account Engagement CRM data kit contains predefined data streams and mappings for activity related to emails, landing pages, web pages, marketing forms, and marketing links.
Data Foundation Considerations and Best Practices: Generally, we recommend using the Individual portion of your data foundation as much as possible. When executing a journey that uses content personalization or decisioning attributes from the Unified Individual portion of the data foundation, keep these considerations and best practices in mind.
Use Case: Cumulus Bank and Financial Services Event-Triggered Journeys: Cumulus Bank and Financial Services want to integrate Data 360 with Marketing Cloud Engagement. They’re using Data 360 to establish a data foundation in Marketing Cloud Engagement. They then want to use the data foundation as part of their campaigns, including their initial customer campaign: a 30-day journey to welcome new auto policy holders and to introduce them to their policy and benefits.
Cumulus Creates Core Activations in Data 360: In this use case, to build their data foundation the Cumulus team creates Data 360 activations for their audiences to Marketing Cloud Engagement. These activations comprise their data foundation.
Cumulus Activates Segments and Adds Attributes: Because the team has chosen to create a Collective Core Audience Model, the marketer repeats these steps for each activation in their data foundation.
Cumulus Establishes Data Foundation Requirements: Cumulus Bank and Financial Services want to integrate Data 360 with Marketing Cloud Engagement. They’re using Data 360 to establish a data foundation in Marketing Cloud Engagement. They then want to use the data foundation as part of their campaigns, including their initial customer campaign: a 30-day journey to welcome new auto policy holders and to introduce them to their policy and benefits.
Implement a Data Foundation in Marketing Cloud Engagement Using Data from Data 360: You can establish a data foundation by activating data from an external system into Marketing Cloud Engagement and linking it to the Engagement Contact Model. You can then refresh and reuse this data across Journey Builder for journey decisioning, wait activities, and exits. You can also use the data for the content personalization of event-triggered campaigns. This solution kit offers a general blueprint on how to set up a data foundation using Data 360.
Establish Data Foundation Requirements for Marketing Cloud Engagement: When integrating Data 360 and Marketing Cloud Engagement, clearly delineate the roles and responsibilities between each product. Data 360 is designed to ingest, resolve, transform, segment, and enrich data. Marketing Cloud Engagement is an engagement product that orchestrates personalized messaging campaigns.
Is This Data Foundation Solution Kit Right for My Organization?: You can establish a data foundation in Marketing Cloud Engagement in multiple ways. Each customer implementation is unique and has different factors to consider. Use this grid to help guide your decision on which data foundation pattern makes sense for your organization. Your needs might require a hybrid approach and a combination of aspects from multiple patterns.
Data Foundation Solution Workflow: Review the solution workflow for integrating your initial data foundation in Marketing Cloud Engagement.
Use Data 360 Data Throughout Marketing Cloud Engagement: It’s important for Marketers to understand that their interaction with the data foundation varies based on the type of campaign that they’re activating. These recommendations maximize system performance.
Activate Marketing Cloud Data Extensions to Paid Media Channels: Use existing data extensions from Marketing Cloud Engagement (MCE) and Journey Builder to create paid media advertising campaigns on Data 360. Migrate your first-party data into Data 360 to enhance your paid media strategy.
Use Case: Enhance Paid Media Campaigns with First-Party Data: A marketing team wants to activate their existing MCE Data Extensions to paid media channels for more targeted advertising campaigns. They need to ingest their MCE data into Data Cloud, create audience segments in Data Cloud, and activate the segments to platforms such as Google Ads and Meta.
Query Data using SOQL: In Data 360, you can analyze segment membership data using SOQL in Data Explorer.
Data 360 Governance Mergeback with DevOps Data Kits: Use sandboxes to develop and validate your data governance strategy, and then deploy it to production through mergeback. Mergeback is the process of moving validated metadata from a sandbox org back to a linked production org or another linked sandbox org. DevOps data kits move governance metadata between environments and maintain a consistent security posture.
Metadata Extraction and Refresh: The metadata extraction framework extracts standard and custom metadata from a Salesforce org. Data 360 stores the metadata in a metadata data lake object (mDLO). A metadata extraction pipeline monitors the metadata's lifecycle, extraction, and ingestion into the catalog. The extraction pipeline handles bulk (Day 0), incremental metadata ingestion, and refresh between the Salesforce org and Data 360.
Metadata Grounding Capabilities: With metadata grounding, you discover, understand, and reuse metadata through natural language interactions. Metadata grounding capabilities accelerate development and customization by making your Salesforce ecosystem accessible and actionable.
Metrics on Metrics: Organize complex Calculated Insights (CI) SQL data into logical steps and stitch powerful workflows. With metrics on metrics you can improve and reuse metrics for multiple scenarios.
Migrate Data Shares with Legacy Snowflake Data Share Target: Move data shares that are linked to a legacy Snowflake data share target to a Snowflake V2 data share target. Verify that data sharing works before you unlink the legacy target.
Loyalty Filters: Here are some examples of common loyalty use cases.
Review Data in Marketing Cloud Engagement: After the segment is activated, a data extension is added to the Marketing Cloud Engagement business unit that you identified in the activation.
Use Case 2: Loyalty Status App Push Notification: Cumulus Bank and Financial Services wants to use MobilePush to send an app notification to customers who have reached Gold loyalty status, and they're eligible to redeem a $100 gift card from their favorite food delivery service.
Monitor the Event Activity of a Data Action: Track key metrics for a data action, such as events identified, processed, published, and failed in the Event Activity dashboard. Get enhanced visibility into your data as it moves through the data action pipeline in near real time. This gives you transparency around the volume of events and data delivery.
MuleSoft Connectors in Flow: MuleSoft connectors are available within Flow Builder. When configuring an action in your activation-triggered flow, you can select from these connectors. To find the supported connectors and their corresponding actions, see Third-Party Connectors.
Disable a Currency Connection in Data Cloud: While we recommend keeping the currency connection in Data Cloud enabled, if you plan to disconnect a standard Salesforce CRM connection from Data Cloud, disable the currency connection to Data Cloud first.
Using Currencies in Data Cloud: Data Cloud uses the currencies and static exchange rates configured in your Salesforce CRM org and applies them to data transforms, calculated insights, segmentations, reports, and other processes.
Create a Nested Segment in Data 360: Reuse filters and maintain consistency in your data by using an existing segment built on profile objects to build a new nested segment. A nested segment is a combination of an outer segment and inner segment. Each nested segment can have up to 50 filters in each tab.
Normalized and Denormalized Data: Data originates from multiple sources and can be normalized or denormalized. The Data 360 standard data model is normalized, so incoming data must be normalized before it can be mapped to the data model. Because not all source systems provide normalized export options or normalize data, assess your field-level data to establish how to transform and map the source data to the standard data model.
Enable Notebook AI For Data Spaces and Users: Grant Data 360 Notebook AI access to data in data spaces and give enterprise users access to Notebook AI with custom permission sets. In a multi-org architecture, enable Notebook AI in both the home org and companion org.
Generate Research Reports with Deep Research: Use Deep Research to generate comprehensive research reports directly in your Notebook AI workspace. Instead of querying multiple sources separately, submit a single research query. Notebook AI analyzes CRM records, uploaded documents, enterprise search results, and web sources, then delivers a report complete with findings, recommendations, and inline citations.
Manage AI Notebooks: Manage your generative AI notebooks in Data 360 Notebook AI. By default, a notebook is private to the user who creates it.
Explore and Research Content with Notebook AI: Data 360 Notebook AI is a generative AI notebook for uploading personal, enterprise, and public content, conducting research, and generating insights.
Enable Notebook AI Web Access: Give enterprise users access to Data 360 Notebook AI web application. Authorize the Notebook AI web application to connect to your Salesforce org and allow users to log in to the application from the web.
Billing Considerations for Notebook AI: Notebook AI uses Data 360 to upload content, ask questions, and save insights. Your data is processed through Data 360 and Agentforce. Use of Notebook AI impacts the consumption of credits in these usage types.
Omnichannel Inventory Connector: In Data Cloud, you can establish a connection to Omnichannel Inventory using the provided connector. The connection process creates the data streams and maps the data source objects to the data model objects (DMO) and establishes DMO relationships.
Omnichannel Inventory Data Mappings: The Omnichannel Inventory connector maps the data source objects to the data model objects (DMO) and establishes DMO relationships.
Omnichannel Considerations and Best Practices: There are important aspects and trade-offs to consider before implementing this solution. This guide is just one example of how to enable an omnichannel experience for your customers. For example, instead of separate single-channel journeys, users can create seamless customer experiences by using Data Cloud to orchestrate one journey with all Email, SMS, and MobilePush activities together.
Create a Calculated Insight to Select the MobilePush SubscriberKey: Data 360 has numerous controls to select and activate a channel contact point. However, for some scenarios, you must create a Calculated Insight to filter or select a specific channel contact point. For example, Data 360 doesn’t support activating the Email, SMS, and MobilePush channels together.
Add Consent and Preference Filters to Omnichannel Journeys: For the email channel, Marketing Cloud Engagement stores consent at the SubscriberKey level and not the email address level. By using the MobilePush Key to send an email to the email address stored in the data extension, Cumulus might have violated their own consent strategy.
Create Omnichannel Journeys Using Unified Individual Profiles in Data Cloud: Learn how to set up and use audience activations from Data Cloud to power an omnichannel journey in Marketing Cloud Engagement. This guide walks you through an example of how you can enable an omnichannel experience for your customers. Instead of separate single-channel journeys, users can create seamless customer experiences by using Data Cloud to orchestrate one journey with all Email, SMS, and MobilePush activities together.
Configure Omnichannel Journey Settings: Next, Cumulus configures their omnichannel settings. When a journey uses the Omnichannel Credit Limit Alert data extension Entry Source, Journey Builder defaults all orchestration, personalization, and sending lookups based on the value of the MobilePush Key. This Key is represented by the MobilePush SubscriberKey selected from the Calculated Insight. This action enables you to natively send messages using MobilePush activities.
Configure Omnichannel Journey Settings in Marketing Cloud Engagement: After the scheduled activation, a data extension is added to the Marketing Cloud Engagement business unit identified in activation. Edit this data extension and update its send relationship to relate the MobilePush Key attribute to SubscriberKey. As a result, Journey Builder defaults all orchestration, personalization, and sending lookups based on the value of the MobilePush Key (representing the MobilePush SubscriberKey selected from the Calculated Insight you created).
Model Your Contact Data for Omnichannel Journeys: This solution kit is designed for customers who have implemented single-channel contacts in Marketing Cloud Engagement. Customers who have implemented single-channel contacts in Marketing Cloud Engagement must follow this solution kit to execute omnichannel journeys. Determine whether you’ve implemented omnichannel or single-channel Contacts.
Stage Your Data in Data 360: Customers commonly implement Marketing Cloud Engagement to create many different SubscriberKeys (Individuals) that represent the same person. Such SubscriberKeys are often channel-specific and, as a result, can’t natively execute omnichannel journeys. To build an omnichannel journey, use Data 360 and build a Unified Individual that represents a single person consisting of separate Marketing Cloud Engagement channel-specific SubscriberKeys (Individuals) and contact points.
Omnichannel Journey Solution Workflow: Review the solution workflow for creating an omnichannel journey in Marketing Cloud Engagement using data from Data 360.
Automate Flows in Data 360: Define automations and chain together Data 360 processes that include ingestion (Salesforce CRM and Amazon S3 data streams), segmentation and activation, identity resolution, and calculated insights. You can run processes in a sequence as needed instead of waiting to run them at a scheduled time. For example, you can chain the processes to refresh calculated insights or run segmentation when data ingestion is completed. You can define automations to monitor flow runs or check their progress. Use Flow Builder to create Data 360 flows. With Flow Builder, you can build complex enterprise-scale automation with automated triggers, reusable building blocks, and prebuilt solutions.
B2C Commerce Order Bundle Data Mappings: The B2C Commerce Order Bundle shows a sample of the key mappings organized by source objects. You can view all the mappings by reviewing your data streams after the bundle is deployed.
Organization Unit Identifier: The Organization Unit Identifier is an optional field that allows you to specify business units in your Data 360 data.
Package Your External Activation Platform for Distribution with Package Manager: After you created your External Activation Platform and custom metadata, use Package Manager to package and distribute your External Activation Platform to Data 360 Admins. A managed package is recommended because you can provide updates to admins when anything changes with the package. Only managed packages can be distributed through AppExchange.
Find External Activation Platform Segments in Cloud File Storage: After admins install your external activation platform, you can receive segments to Cloud File Storage (Amazon S3). For each activation target, you receive a success file, metadata file, and activation output files once a day for each identifier type when the output is grouped by user identifiers (PER_USER). Because files arrive only once a day, there’s a delay between Data 360 customers updating segments and those changes arriving to S3.
Packaging Data 360 Enrichments: Use second-generation managed packaging (2GP) to move Data 360 enrichments between orgs.
Packaging in Data 360: A package can contain small individual components or a large set of related apps. You create a data kit with the packageable features and add the data kit to a package. You can distribute this package to other Salesforce users and orgs, including people outside your company.
Parent-Child Hierarchy in Data 360: A parent-child tag hierarchy organizes tags in a logical, multi-level structure, where broader, general tags (parents) are linked to more specific, detailed tags (children).
Parsing and Preprocessing Content: Prepare unstructured data for chunking by using Data 360 content parsing and preprocessing methods. Parsing and preprocessing identify document structures, such as headings, paragraphs, or tables, remove irrelevant content, and prepare the text for chunking and indexing.
Party Data Model: The Customer 360 Data Model party subject groups data model objects (DMO) that reflects contact information (personal or business) for a specific customer or account.
Plan Your Data 360 Data Strategy: Before you implement Data 360, it’s important to plan ahead and discuss your strategy. Review org architecture, your own organizational data, and Data 360 data concepts.
Platform Encryption for Data 360: Some customers require tenant-specific encryption keys, control over key material, or improved key visibility for auditing and compliance reporting. Platform Encryption for Data 360 extends Data 360’s robust security services by adding options for encryption key control and visibility.
Policy-Based Governance in Data 360: Data governance policies are rules and guidelines that define how data is accessed, used, protected, and managed. These policies help align data handling with security requirements, compliance standards, and business objectives.
Policy Behavior on Core Applications in Data 360: Core applications such as Related Lists and Copy Field Enrichment follow specific policy behaviors. While some features honor user-level access controls, others run in system mode and require additional security measures at the CRM level.
Policy Behavior When Creating Data Streams and Data Objects: When you create data streams, data lake objects (DLOs), or data model objects (DMOs), Data 360 enforces access policies to make sure you can only interact with the objects and fields you're allowed to use.
Policy Behavior on Process Definitions: Data 360 process definitions describe the workflows and tasks used to manage data such as data streams, transformations, calculated insights, segments, and identity resolution. These definitions help standardize how data operations are created and executed. Data governance policies are enforced when users create or consume these definitions.
Policy Behavior When Querying Data: When you query data in Data 360, whether through Data Explorer, Profile Explorer, or Tableau, governance policies control what you can retrieve. Even if metadata is visible in the UI, actual access is enforced at query time to protect sensitive data.
Policy Enforcement in Data 360: Policies are enforced across different feature areas in Data 360 depending on whether the user is creating or consuming data. Policy enforcement doesn't take effect immediately in dashboards.
Policy Types in Data 360: Data 360 offers two policy categories in data governance: role-based access control (RBAC) and attribute-based access control (ABAC).
Preconfigured Taxonomies in Data 360: Preconfigured taxonimies are built-in, ready-to-use classification structures provided by the system. They cover common data categories such as personal information, financial data, or health records. Preconfigured taxonomies get you started quickly and follow standard best practices without the need to define categories from scratch.
Set Output Name for Activation Attributes: When you configure an activation in Data 360, you can customize the attribute names in your output file or stream. Custom attribute names help match the naming conventions required by your activation target. Use the output namefield ( preferred name) to define these values.
Prepare and Model Data: Data being brought into Data 360 can be transformed and prepped before mapping. During data mapping, the preserved data source schema is mapped to an opinionated and extensible data model, called the Customer 360 Data Model. After the data is mapped, you can further query and manipulate your data to make it easier to work with for use across Salesforce.
Convert a Data Cloud Home Org to a Data Cloud One Companion Org: To convert a home org into a companion org, first remove all Data Cloud data from your org, then connect it as a companion org to a different Data Cloud instance. You can’t convert an org that has Data Cloud enabled.
Primary Key: To create a data stream in Data Cloud, select a primary key or unique identifier for that source. If a data source doesn't have a field that can be used as a primary key, create a composite key by combining two or more source fields using a formula field.
Privacy Data Model: The Customer 360 Data Model privacy subject tracks and stores certain data privacy preferences. Keep in mind that deciding how to honor customer privacy preferences is up to you.
Add a Private Network Route for Amazon MSK: Connect your Amazon Managed Streaming for Apache Kafka (MSK) data source and your Data Cloud tenant with a dedicated network connection using Private Connect for Data Cloud. You can create a single connection to a specific instance regardless of how many Data Cloud instances you have. Before setting up a connection for Amazon MSK, establish a private network route.
Private Connect Considerations: Private Connect for Data 360 establishes a secure connection between Virtual Private Clouds (VPC), bypassing the public internet and enhancing security.
Private Connect for Data 360: Private Connect uses Amazon Web Services (AWS) PrivateLink to establish a point-to-point network integration between your AWS virtual private cloud (VPC) and your Data 360 tenant. Private Connect provides a connection from Data 360 to your data source without exposing your network traffic to the public internet.
Add a Private Network Route for Redshift: To enable federation connections, connect your Redshift data source and your Data Cloud tenant with a dedicated network route using Private Connect for Data Cloud. Private Connect supports self-managed PrivateLink. Redshift-managed PrivateLink isn't currently supported.
Update Your Snowflake Network Policy: To ensure access, modify your Snowflake network policy to include the Data Cloud virtual private cloud (VPC) endpoint ID.
Process Content in Data Cloud: Use generative AI-based tools in Data Cloud to process unstructured data for use in AI, automation, and analytics workflows.
Data 360 Search Index: Data 360 uses a search index to manage structured and unstructured content in a search-optimized way. Create a search index configuration to chunk and vectorize your content. Chunking breaks the text into smaller units, reflecting passages of the original content, such as sentences or paragraphs. Vectorization converts chunks into numeric representations of the text that capture semantic similarities.
Unstructured Data: Use unstructured data in Data 360 to ground Agentforce agents, generative AI, analytics, and automation use cases with business-specific data that delivers deeper insights for your users and customers.
Get Started with Data 360: Before you begin with Data 360, review the checklist, considerations, and product navigation.
Product Subject Area: The Customer 360 Data Model product subject area defines anything you plan to sell or any part of a product to track for service purposes.
Profile Explorer in Data 360: Get a complete view of a unified profile for an account or individual for purposes such as validating unified profile data and checking for discrepancies. Customers can deploy a set of Lightning apps to customize and expand the experience.
Profile Explorer Considerations: When using Profile Explorer or the Data 360 Unified Profile Lightning apps, keep these considerations in mind.
Add a Data Kit to a Package: After creating and publishing your data kit in Data 360 source org, add the data kit to a package and upload it using Package Manager in your Salesforce org.
Publish a Segment in Data 360: After defining your segment in Data 360, you can publish it to your activation targets whenever you want or based on a schedule when the segment was created. The standard publish schedule is 12 or 24 hours. You can also change the segment schedule by editing its properties. When you’ve reached the segment publish concurrency limit, Data 360 defers the publish of other segments. This deferral continues until the number of simultaneously running segments falls below the limit. This means that a segment scheduled at a specific time sometimes doesn’t start publishing at its scheduled time.
Purchases Filters: Here are some examples of common purchases use cases.
Fully Qualified Keys for Query API: Use Query API to query data in Data 360. Identify key qualifier fields in a data lake object with the Data 360 Metadata API, and include key qualifier fields in all table joins to get accurate results.
Create a Secondary Index: Create secondary indexes in Data 360 to improve query performance and reduce credit usage on data lake objects (DLOs) or materialized data model objects (DMOs) by optimizing data retrieval beyond primary key fields.
Query Data in Data 360: A query is a structured request for specific information from Data 360. It's similar to asking a precise question of your data. Data 360 offers multiple ways to query your data.
Query Editor: With the Data 360 Query Editor, you can directly query harmonized customer data using Data 360 SQL (Structured Query Language). You can build queries on Data Lake Objects (DLOs), Data Model Objects (DMOs), Calculated Insights Objects (CIOs), and data graphs.
Create Queries in Query Editor: Build Data 360 SQL queries on Data Lake Objects (DLOs), Data Model Objects (DMOs), Calculated Insights Objects (CIOs), and data graphs.
Manage Workspaces in Query Editor: Create and delete workspaces in Query Editor to build, execute, and save SQL queries for Data Lake Objects (DLOs), Data Model Objects (DMOs), and data graphs in Data 360.
Use Query Editor in a Data Cloud One Companion Org: Query Editor has the same features in the Data Cloud One app as it does in Data 360. However, its settings and data access capabilities vary depending on the companion org.
Optimize Queries with Secondary Indexes: When you create a secondary index on a data lake object (DLO) or materialized data model object (DMO), Data 360 quickly finds and returns data based on defined secondary field values. A secondary index improves performance and reduces credit usage.
Process an Insight Manually: Instead of waiting for a calculated insight to be processed in a scheduled batch, run it manually.
Activate the Segment, Add, and Filter Related Attributes: When your segment is complete, the next step is to activate it and add related attributes. Because this campaign is specific to users with new auto policies within a specific date range, add filters to prevent other policies from being sent to Marketing Cloud.
Use Case 3: New Auto Policy Welcome Series: Cumulus Bank and Financial Services wants to create a three part journey to welcome new auto policy holders that introduces them to their policy and benefits.
Using Retrieval Augmented Generation: Retrieval Augmented Generation (RAG) in Data 360 is a framework for grounding large language model (LLM) prompts. By augmenting the prompt with accurate, current, and pertinent information, RAG improves the relevance and value of LLM responses for users.
Group, Rank, and Limit Segment Audience: The grouping, ranking, and limiting rules in Data 360 Segmentation adds advanced, multi-level filtering to the segment builder. After your standard filters are applied, you can use sequential logic to group, rank, and limit your segment population, creating more structured and balanced audiences.
Increase Segment Refresh from Data 360 to the Activation Targets: Increase the frequency of segment refresh and shorten publishing time for activations from Data 360 to the activation targets using Rapid Publish. When you create a segment, choose Rapid Publish to run on a daily schedule at a 1-hour or 4-hour interval.
Real-Time Capabilities in Data 360: By using the real-time capabilities of Data 360, you can enable consistent, synchronized customer data across all platforms. Real-time features provide sub-second data processing across multiple customer interactions.
Identity Resolution Reconciliation Rules: A reconciliation rule specifies how to select a single value to save to a unified field that can’t have multiple values, such as an individual’s name, during the identity resolution process. Reconciliation rules are set at the object level and can be overridden by field-specific reconciliation rules. The reconciliation process doesn’t delete or change any source profile data, update your connected source systems, or tell you which source data to use when working with your customers.
Set a Default Reconciliation Rule: Set a default reconciliation rule for each object in your identity resolution ruleset. The default reconciliation rule tells the identity resolution process how to select values for all fields in an object.
Reconciliation Rule Warnings: Reconciliation rule warnings help you identify fields to change or review. You can ignore a reconciliation rule warning. Identity resolution processes run even with these warnings.
Record Modified Field: The record modified field is set when defining the source object during setup in Data 360.
Redeploy a Data Kit When Its Data Graph Is Deployed: When you redeploy a data kit that contains an already-deployed data graph, Data 360 leaves the data graph unchanged and marks it as successful. This process ensures that the overall data kit deployment succeeds. The data kit version can change between deployments, and the new version might or might not include data graph changes. In either case, the redeployment doesn’t fail.
Refresh a Data Graph: In Data 360, refreshing a data graph updates its underlying data with changes that occurred since the last refresh. A refresh takes the same amount of time as initial creation (15 minutes up to several hours, depending on data quantity). Data remains accessible while a refresh is in progress.
Troubleshooting CRM Connections: If you have trouble with creating, refreshing, or disconnecting a Salesforce CRM data stream, see these common issues.
Refresh for Data Extension Data Streams: For a data extension data stream with the refresh mode of upsert, you can choose to perform either an incremental or a full data refresh.
Related Attributes in Activation: Personalize Marketing Cloud Engagement communications using direct and related attributes from Data 360. Control the volume and variety of data that can be used in marketing segments.
Review the Data in Marketing Cloud Engagement: After the segment is activated, a data extension is added to the Marketing Cloud Engagement business unit that you identified in the activation.
Remove a Field from a Data 360 Page: A FlexiPage represents the metadata associated with a Lightning page. A Lightning page is a customizable screen made up of regions containing Lightning components. A Lightning page is created only for derived data model objects (DMO) that have unified objects such as UnifiedIndividual and UnifiedAccount. You can remove a field from the Data 360 page to remove field mappings.
Remove or Delete a Key Qualifier: To reduce clutter and avoid reaching your org's limit for key qualifiers, remove and delete unnecessary key qualifiers from the system. To delete a key qualifier, first remove it from all the data lake object (DLO) fields that it’s associated with and then delete it. You can also just remove a key qualifier from a DLO field but not delete it from the system. Removing a key qualifier from a DLO field can potentially affect the accuracy of calculated insights, segments, activations, and API queries.
Replicate Data 360 Configurations Using Local Data Kits: Move Data 360 metadata and process definitions between data spaces within the same sandbox using the local data kit deployment method. Create a standard data kit in your default data space, and then use a change set to deploy it to other data spaces.
Data Mapping Requirements in Data 360: Mappings and relationships are set up automatically only for data streams that are deployed through standard data bundles. However, you must ensure that the required mappings and relationships are set up properly for custom data streams.
Identity Resolution Ruleset Processing Results: Summary statistics are available after a ruleset job runs for the first time. Results of the most recent run are shown in the Resolution Summary. Results from previous days are listed in the processing history table. If the ruleset was processed more than one time per day, the results in the processing history table reflect the last run completed during the day.
Identity Resolution Calculated Insight: Consolidation Rates for Unified Profiles: Review the consolidation rate of matched profiles for each data source. Data sources with duplicate profiles are expected to have higher consolidation rates than data sources with many unique profiles. Use this calculated insight after creating unified profiles with an identity resolution ruleset.
Data Quality Calculated Insight: Contact Points Per Individual: Review the number of contact points for each source record to find outliers. Outliers can help you identify issues with your source data or data mappings. Use this calculated insight after data ingestion, but before creating an identity resolution ruleset.
Data Quality Calculated Insights: Contributing Contact Points: Review occurrences of repeat values for Contact Point Address, Contact Point Email, Contact Point Phone, and Party Identification objects. Use this calculated insight after data ingestion, but before creating an identity resolution ruleset.
Identity Resolution Calculated Insight: Outlier Unified Profiles: Review the number of matched contact points for each unified profile. A high number of matches per contact point, like profiles with hundreds of source records matched with the same email address, could indicate issues with data quality or source data. Use this calculated insight after creating unified profiles with an identity resolution ruleset.
Data Quality Calculated Insight: Summary by Source: Review the number of unique contact points per data source. The number of unique contact points is the number of source records consolidated in identity resolution. Use this calculated insight after data ingestion, but before creating an identity resolution ruleset.
Resolve Connection Errors in Data Cloud One Sandbox Orgs: Establish a Data Cloud One connection from a home org sandbox to a companion org sandbox using the same steps as with production orgs. The connection succeeds when there’s no metadata drift between the orgs. If metadata drift occurs, delete the specified drifted entities in the error log in the companion sandbox to establish the connection. Manual cleanup of all Data 360 metadata isn’t required.
Retrieval Response with Enriched Search Index: Enriched indexing enhances retrieval response by matching a question to a passage with the best possible answer in your knowledge base. While Data 360 hybrid search combines the strengths of sematically aware vector search with the precision and speed of keyword search, enriched indexing closes the gap further by finding the chunks closest to the users' question.
Retriever Filters Reference: Configure filter conditions for retrieval accuracy when you create or test retrievers in AI Models (formerly Einstein Studio). Use filter conditions to filter records retrieved for search queries. You can configure up to ten filter conditions for an individual retriever.
Build and Deploy Related List Enrichments from a Data Cloud Sandbox: In a Data Cloud sandbox, build new related list enrichments or modify the existing ones that are automatically generated. After you finish testing your enrichments, package them in a data kit and deploy it to production using change sets.
Root Key Lifecycle: Root key statuses in Platform Encryption define the usage of each key for data encryption and decryption.
Sales Order Data Object: The Customer 360 Data Model sales order subject defines the future revenue or quantity for an opportunity by product family and rolls it up by territory, management (role), or hierarchy.
Sales and Service Bundle Data Mappings: Use the Sales Cloud and Service Cloud datasets to manage leads, track progress, and automate sales processes. Sales Cloud data is extracted from Salesforce standard objects, such as Account, Contact, and Lead. The Service Cloud dataset lets you engage your customers at the right time as they go through the support process. These data mappings relate to the Sales Cloud and Service Cloud bundles available for the Salesforce CRM connector.
Unify Leads for Targeted Selling: Improve sales experiences by unifying data with Data Cloud. Use that data to unlock insights to enhance Sales Cloud. Combine your Sales Cloud data to identify opportunities with priority customers to sell products and services.
Salesforce Catalog: The Salesforce Catalog is a unified registry that discovers, indexes, and connects metadata across Salesforce and its ecosystem. This semantically rich foundation powers intelligent automation, agents, and high accuracy natural language queries at scale.
Billing Considerations for Salesforce Catalog: When you use Salesforce Catalog, you process unstructured data and search index configurations in Data 360. Use of Salesforce Catalog with Data 360 impacts the consumption of credits in these usage types.
Salesforce Connectors for Data Cloud: In Data Cloud, you can connect to other Salesforce data sources. Many of the connectors for these data sources have a starter data bundle to help you quickly set up a data stream. A starter data bundle is a Salesforce-defined data stream definition that includes mapping to the Data Cloud DMO structure. You can also customize a bundle. Some connectors offer multiple data bundle options.
Salesforce CRM Connector: A standard Salesforce CRM connection establishes a connection between Data 360 and a Salesforce org, allowing Data 360 to ingest data from the connected org. Data 360 ingests data and sends back only data actions. This type of connection doesn’t share any data back to the connected org.
Save an Insight as a Draft: When creating a calculated insight using Insight Builder or SQL Expression Builder, you can save it as draft and continue to make updates until you publish it. You can then validate the calculated insight when you’re ready to deploy it. However, you can’t save an existing calculated insight as a draft while editing it.
Schedule a Secondary Index Refresh: Set how often Data 360 refreshes a secondary index so that the index stays current with the underlying object. Choose a refresh frequency that keeps the index fresh enough for your queries without refreshing more often than your data changes.
Data Object Concepts and Schema in Data 360: During ingestion, Data 360 retrieves a sample of your data and recommends a source schema. You can then review and modify the recommended schema.
View Unified Profiles in Profile Explorer: Unified Account and Unified Individual profiles are shown in Profile Explorer. You can search for them by attributes or related attributes.
Citations for Content Chunks: Verify the accuracy of AI responses grounded in your unstructured data with inline citations for content chunks. When you create a search index in Data Cloud, the index creates citations for each chunk.
Chunk DMO: The chunk data model object (CDMO) stores chunked content for each field from a chunked object.
Chunk and Index Data Model Objects: When you create a search index configuration, Data 360 creates two additional data model objects: a chunk data model object and an index data model object. These objects store the corresponding data and generated embeddings in Data 360.
Create a Vector Search Index with Advanced Setup: When you create a vector search index configuration for a data model object, Data 360 breaks up the referenced data into semantically related chunks and generates searchable vectors. Your RAG and agent workflows use these vectors to find and return semantically similar items when they run queries. To get more granular control over your search index configuration, use the advanced setup, which guides you through each step of the process.
Create a Search Index Configuration with Easy Setup: Follow a few automated steps to create a hybrid search index configuration for a data model object (DMO) or an unstructured data model object (UDMO). Data 360 automatically applies defaults for the chunking and vectorization strategies, and creates chunk and index model objects.
Edit Search Index Configurations: If needed, you can edit Data 360 search index configurations to add fields or file extensions for chunking. You can view but not modify other existing settings.
Enriched Indexing of Content Chunks: When you create a vector or hybrid search index from unstructured data in Data 360, you can enrich the generated content chunks by extracting metadata from the content. Extracted metadata includes keywords, entities, topic overviews, questions answered by the content, and content summaries.
Use Search for AI, Automation, and Analytics: Grounding Data 360 search on unstructured and structured data enhances your use of generative AI, analytics, and automation tools across Salesforce. Grounding brings customer-specific data, including diverse forms of unstructured data (such as text documents, multimedia files, social media content, web content, chat logs, sensor data, and customer feedback), into applications like Agentforce, Tableau, and Flow Builder. This alignment results in more accurate and relevant AI-generated content, deeper insights from analytics, and more efficient automation workflows for your teams and customers.
Chunk Data: To add your data to Data 360’s search index, you must first chunk it. Chunking break your data into meaningful chunks, and Data 360 turns those chunks into machine-readable vector embeddings.
How the Max Token Setting Affects Chunking: When you set the chunking strategy in a search index configuration, you set the max token limit to control how much text is included in a chunk. In Data 360, the max token limit is set to 512 by default.
Optimizing Search Indexes: Field Selection and Chunking: When you create a search index in the advanced builder, you can optimize your search index to deliver more accurate results by paying attention to the field selection and chunking strategies you use.
Vector Search Query Expressions and Pre-Filtering: Enhance your vector search queries with pre-filtering fields. Use logical operators and manage case sensitivity for field names and values to ensure precise data retrieval.
Rebuild a Search Index Configuration: If a Data 360 search index doesn't process properly, you can trigger a full rebuild of the index from the list page or the object record view.
Search Index Reference: Data 360 search supports many different file formats with unstructured data, embedding models, and pre-filter operations on search results.
Retrieving Content with Vector Search: With vector search and AI agent queries, you can capture queries, search across structured and unstructured data sources, and receive summarized responses that improve efficiency and accuracy.
SFR-v2-small Embedding Model Reference: The SFR-v2-small embedding model offers an enhanced context window of 8,000 tokens and significantly improves user experience and model capabilities.
Chunking Strategies: Data 360 supports several chunking strategies. Data 360 automatically chooses the optimal chunking strategy based on the content type you're working with.
Vector Search: In Data 360, you can create searchable semantic vector embeddings from your unstructured and structured data. Vector search helps your generative AI, automation, and analytics applications understand semantic similarities and context between embeddings.
Enhancing Data Analysis with Vector Search in Tableau: Find and analyze data based on meanings, not just keywords, by using vector search in a Tableau query. Vector search broadens the scope of data exploration as well as enhances the accuracy and relevance of the insights by identifying semantic similarities rather than relying solely on exact keyword matches.
View Search Index Status and Process History: View details of search index configurations from the Search Index list view and the object record view to monitor the indexing process.
Segment Canvas: Use the segment canvas in Data 360 to select direct and related attributes to narrow down a created segment to your target audience.
Segment Membership Data Model Object: Each time you publish a segment, a data model object (DMO) is created or updated to store segment membership information. To identify who's included in a segment, use the Segment Membership DMO (type Segment_Membership). If there are no dependencies on the DMO, you can delete segment membership DMOs manually.
Segment Membership DMO Schema: The segment membership DMO has these object-level and field-level schema attributes. These fields are available in the latest and history segment membership DMOs.
Segment On: Segment On defines the target object used to build your segment. You can choose an object marked as Profile or Engagement type during ingestion. For example, you can build a segment on Unified Individual, Unified Household, Account, Reservations, Sales Order, etc.
Segment Types and Statuses: Learn more about the values provided about each segment. View details in the Segments section of Data 360, or in Publish History for a specific segment. Depending on how you create the segment, the type can be UI (created via the Segment Builder) or DBT (created via the API).
Hierarchical Aggregation in Segment Filters: Hierarchical aggregation provides a comprehensive view of your data by rolling up revenues for accounts and their subsidiaries. You can then define more precise segment filters based on an aggregate value across multiple levels.
Segment Your Data with Attributes: After you create a segment, narrow your data based on related or direct attributes on the segment canvas interface. Narrow down a subset of the population from a filtered audience to see who’s included and excluded. Segment exclusion is optional.
Create Segments in Data 360: Use segmentation in Data 360 to break down your data into useful segments to understand, target, and analyze your customers. After you create a segment, you can publish it to an activation target.
Find Data Cloud Segments in Marketing Cloud Engagement: After you create and activate segments to Marketing Cloud Engagement, view them in Contact Builder. A Data 360 segments subfolder is created when you publish your first activation.
Guide Data Q&A with Semantic Enrichments: Add semantic statements to help the Data 360 Data Q&A Agent better understand your data's business context, domain-specific terminology, and relationships.
Session Extension in Real-Time Data Graphs: To maintain consistency between a real-time data graph and a standard data graph, session extension preserves your data for a longer period while the standard graph updates.
Set Up an Amazon Kinesis Target Connection: Send data action events from Data 360 to an Amazon Kinesis data stream. Use an access key or Salesforce as an OpenID Connect identity provider.
Set Up an SFTP Target Connection: Create an SFTP connection in Data 360 using your own SFTP server that’s not provided or used by Marketing Cloud Engagement.
Set Up a CRM Salesforce Org Connection: A data source and data action target are automatically created for the org where you set up Data 360. To connect other Salesforce orgs, manually set up a connection.
Set Up a Connection to Marketing Cloud Personalization: After you create your Data 360 instance, connect to Marketing Cloud Personalization (formerly Interaction Studio) to enrich customer profiles with user profile data and events.
Set Up a Marketing Cloud Engagement Connection in Data 360: After you create your Data 360 instance, control the scope of data ingestion and activation from Marketing Cloud Engagement and other sources. Before integrating with Data 360, make sure that your Engagement user’s default business unit (BU) is set to the Enterprise ID (EID). Set up your data sources to tell Data 360 where to pull data from, also known as ingest, for data mapping and segments. You can connect multiple EIDs to a single Data 360 org, where each EID has its own connection to Data 360. For each of these connections, you can set up multiple data streams using data bundles or data extensions. Each connection also has its own business unit-to-data space mapping configuration. Data 360 supports only Engagement Enterprise 2.0 account connections.
Connect Tableau in Data 360: Set up a connection to Data 360 from Tableau. Different steps are required depending on which Tableau product and version you use.
Set Up Your Data in SageMaker: Access Salesforce data from an Amazon SageMaker notebook using the Data 360 Python connector. The intuitive interface lets you quickly execute run queries and extract the data that you need.
Set Up Connectors with Google OAuth: To create a connection in Data Cloud securely without sharing passwords, some external connectors allow access using OAuth (Open Authorization). If using OAuth, there are several prerequisite implementation steps to complete before you connect the source to Data Cloud.
Set Up Connectors with LinkedIn OAuth: To create a connection in Data Cloud securely without sharing passwords, some external connectors allow access using OAuth (Open Authorization). If using LinkedIn OAuth, there are several prerequisite implementation steps to complete before you connect the source to Data Cloud.
Set Up Connectors with Microsoft OAuth: To create a connection in Data Cloud securely without sharing passwords, some external connectors allow access using OAuth (Open Authorization). If using OAuth, there are several prerequisite implementation steps to complete before you connect the source to Data Cloud.
Set Up Connectors with OpenId Connect OAuth: To create a connection in Data Cloud securely without sharing passwords, some external connectors allow access using OAuth (Open Authorization). If using OAuth, there are several prerequisite implementation steps to complete before you connect the source to Data Cloud.
Set Up Platform Encryption for Data 360: When you enable Platform Encryption, your first Data 360 root key generates automatically. This key encrypts all previously ingested data in Data 360. After the initial setup, you can continue to use the generated root key or configure an external key using External Key Management (EKM), or leverage your own key material using Bring Your Own Key (BYOK) in the Salesforce UI to encrypt data in Data 360. You can rotate root keys periodically and view key metadata, such as creation time and creator, in Setup.
Set Up and Turn On Data 360: Capture, unify, segment, and activate your customer data with Data 360. Automatic provisioning occurs for most orgs that have a Data Cloud license. However, if auto provisioning isn't available or if you’re working in a Developer Edition or sandbox org, you must manually enable it.
Share Data Between Data 360 Orgs: Share Data 360 objects, such as data lake objects (DLOs), data model objects (DMOs), and calculated insights objects (CIOs), between Data 360 instances in different regions or with other Data 360 orgs using a data share.
Share Data with Databricks: Share Data 360 objects with a Databricks account using a data share. Link the data share to a Databricks data share target to make the Data 360 objects available to the Databricks account.
Simple Start in Data 360: Streamline your initial Data 360 setup with Simple Start. Bypass manual bundle configuration and field mapping by syncing metadata and creating standardized data structures for your Salesforce CRM data in minutes. Because the system initially syncs only metadata, you consume credits only after activating related data streams and downstream processes.
Ingest Lead Ad Data into Data Cloud: Set up the Lead Capture app to pull leads from Meta (formerly known as Facebook) into Sales Cloud, connect Sales Cloud to Data Cloud, and receive leads into a data lake object (DLO). Having lead information in Data Cloud can help with event orchestration, activation to ad networks, and lead scoring.
Fiscal Calendar Options for Data 360 Queries: Use standard or custom fiscal calendar options to configure how Data 360 queries handle fiscal years. With these settings, you can align query results with your organization’s fiscal periods rather than calendar years.
Marketing Cloud Engagement Starter Data Bundles in Data 360: Marketing Cloud Engagement offers starter data bundles for Email Studio, MobileConnect, MobilePush, and WhatsApp. Data 360 doesn’t support formula fields for Marketing Cloud Engagement data streams.
Stop Engagement Data Collection: User engagement data is collected by default to generate valuable insights, but you can choose to stop data collection for a specific engagement data stream by deleting the engagement data stream and associated Data Lake Object (DLO).
Streaming Audit Data Model Object: Use the streaming audit data model object (DMO) to track and audit the data sent from your real-time streaming activations.
Streaming Data Graphs: Use streaming data graphs to automatically refresh your data when underlying data changes. Streaming refresh reduces latency, removes the need for scheduling, and provides more predictable refresh behavior.
Customer Data Subject Rights in Data Transforms: A customer’s Data Subject Rights relate to the storage and use of their personal data. Data 360 offers tools to support Data Subject Rights in alignment with various data protection and privacy regulations.
Merge Multiple Data Streams Use Case: The Marketing Cloud Engagement connector in Data 360 uses two profile data streams to ingest data from Marketing Cloud Engagement. Both data streams include the same set of primary keys. When these data streams are mapped directly to the Individual data model object (DMO), duplicate records appear in the DMO. To solve this problem, merge the data streams into one data lake object (DLO) before mapping to the Data 360 data model.
Streaming Data Transforms: A streaming data transform reads one record in a source object, reshapes the record data, and writes one or more records to a target object. The source and target objects must be different objects. A streaming data transform runs continuously as a streaming process, picking up new or changed data.
Set Up a Streaming Data Transform: When you set up a streaming data transform, it reads and processes all records in the source and writes the data to the target object. The source and target objects must be different objects.
Split a Data Stream Use Case: A data stream containing guest checkout information includes profile and engagement data. Split the data so that it can be mapped into separate data lake objects (DLOs), one for profile information and one for engagement.
Streaming Data Transform Use Cases: These use cases show how you can use streaming data transforms to normalize your data, filter it, and better align it with the Data Cloud data model.
Filter Streaming Data Transform Records: Use the WHERE clause in the streaming data transform SQL statement to limit which source object records the streaming data transform reads and processes.
Use Value Suggestions in Segmentation: You can enable value suggestions for data model object (DMO) fields if the data type is Text. Enable or deactivate the feature in the DMO record home. You can enable value suggestions for up to 500 attributes for your entire org. It can take up to 24 hours for suggested values to appear.
Next Steps with Data Cloud: The use cases detailed in this guide give you a glimpse into how you can use Data Cloud to power your unified Customer 360. When you connect and unify more data sources, the more useful your unification, insights, and analytics become.
Syncing Copy Field Enrichments: Learn how data is copied and synced between Data 360 and your org for copy field enrichments. Get tips for troubleshooting data syncing and changing enrichments.
Tableau Accelerators for Data 360: A Tableau accelerator provides ready-to-use dashboards and workbooks geared for a particular industry or business use case. All Tableau accelerators use the Salesforce Data Cloud connector to connect Tableau and Data 360.
Tag Propagation in Data 360: Tag propagation helps maintain consistent data classification by extending tags from a primary data object to related or downstream objects and fields. This ensures that related objects in your data model carry the same metadata labels and reduces the need for repetitive tagging.
Data Governance Taxonomy: In Data 360 data governance, a taxonomy is a hierarchical set of categories and subcategories (or tags) that classifies data based on its type, sensitivity, usage, or domain. Using taxonomies ensures consistent tagging, improves data discoverability, and enables accurate application of policies such as masking or access control. They also help align data classification with privacy and compliance requirements.
Testing an Activation Platform Package: Test an activation platform package by installing it either in an Enterprise or Developer Edition org that has a provisioned Data 360 license. For more rapid testing and inspection of file outputs as you develop your app, use a Dev org to trigger manual activation publish. When testing your activation platform app in an Enterprise org, activation files are published every 24 hours if the activation output is grouped by user identifiers (PER_USER). If the activation output is grouped by segments (PER_SEGMENT), the refresh schedule is tied to the segment publish schedule.
Data 360 Time Zones: Data 360 uses the time zone set in Salesforce when publishing and refreshing segments and activations. The dates and times added to a segment in the segment canvas also use the Salesforce time zone. To change the time zone for Data 360, change the time zone in Salesforce.
Create a Data Transform Record Event Flow: Create a record-triggered flow for data transform event actions. With a record-triggered flow, you can update another record, send a notification, or initiate a process.
Troubleshoot Data Stream Errors: Data streams can encounter various issues when importing data into Data 360. Use this troubleshooting section to identify and resolve common data streaming errors.
Troubleshoot Data 360 Governance Mergeback Deployments: When you migrate your data governance metadata, you can sometimes encounter deployment errors. Review the symptoms and solutions for common mergeback issues so that you can successfully complete your deployment across environments.
Dynamic Masking Types in Data 360: Dynamic data masking policies protect sensitive data by changing or hiding values when you query them. This is done based on user information, roles, or policy rules, but it doesn't change the data itself.
Normalize Denormalized Data Use Case: In this use case, the data comes from a single Marketing Cloud Engagement data extension but must be normalized to map to the Data 360 data model objects (DMO).
Data Model Concepts: Improve the interoperability of data across applications, and map data between your connected data sources and the Data 360 Customer 360 Data Model.
Unified Individual for Activation Membership: To take advantage of identity resolution, use the Unified Individual data model object (DMO) as the Activation Membership. With identity resolution, you combine data from disparate data sources to create one Unified Individual. When selecting Unified Individual as the DMO, the Individual ID attribute is included and not the Unified Individual ID.
Unify Sales Data with Identity Resolution: Link multiple sources of data into a unified profile using an identity resolution ruleset. A ruleset contains match and reconciliation rules based on the Account data model object or the Individual data model object.
Unify Service Data with Identity Resolution: Link multiple sources of data into a unified profile using an identity resolution ruleset. A ruleset contains match and reconciliation rules based on the Account data model object or the Individual data model object.
Masking Unstructured Data in Data 360 (Beta): Mask sensitive information in unstructured files, such as PDFs and text documents, so that only authorized users can view it. Masking policies work on sensitive data that’s automatically tagged or identified.
Configure Harmonization for Unstructured Content: Use this workflow to create an unstructured content harmonization configuration for Data 360 content. The content must be ingested with a supported connector.
Unstructured Data in Data 360: By bringing unstructured data in Data 360, you can ground your Agentforce agents, generative AI, analytics, and automation use cases with business-specific data that delivers deeper insights for your users and customers.
Billing Considerations for Unstructured Data and Search Index: When you use unstructured data and search index configurations, your data is stored and processed in Data 360. Use of unstructured data and search index in Data 360 impacts the consumption of credits in the following usage types.
Content Object Data Mappings: Content data streams enable you to upload and ingest files that you’ve attached to Knowledge objects. Manually map the listed fields of the Content Document and Content Document Link objects to DMOs in Data 360.
Ingest File Attachments from Salesforce CRM Objects into Data 360: Use the Salesforce CRM connector to ingest into Data 360 content files such as documentation or customer support files. You can ingest content files attached to standard and custom CRM objects, including 100K of the most recently uploaded historical files and all new files going forward. Data 360 converts the content into searchable vectors for generative AI, analytics, and automation while preserving search context.
Using Unstructured Data in Data 360: Use unstructured data in Data 360 to ground Agentforce agents, generative AI, analytics, and automation use cases with business-specific data that delivers deeper insights for your users and customers.
Mapping UDLOs to UDMOs: Workloads in Data 360 that consume unstructured data do so through unstructured data model objects (UDMOs). When you create a UDLO, you map it to a UDMO.
Query Unstructured Data Using Query API: Use Query API to retrieve a URL to view unstructured data files. Queries return a presigned URL that you can open in your browser.
Unstructured Data Workflow: Regardless of your use case for unstructured data, there's a general workflow to ingest and process it in Data 360.
Update a Data Kit in Data 360: You can update an existing data kit when you modify Salesforce CRM and B2C Commerce data streams, data models, mappings, and relationships. You can also retrieve and deploy the updated data kit from Data 360 with Package Manager in your Salesforce org.
Update a Schema: After your schema is created in Data 360, update formula fields or source fields as needed.
Segment Time Zone: Segment publish schedules are interpreted in the user's time zone. Your Data 360 org sets the publish time zones on the initial segment save and segment canvas. Segment and activation fields display the Publish Schedule Start Date and Time in the user's time zone.
Data Stream Updates Using File Upload: Update the data of an existing data stream created using File Upload. Quickly update your data by re-uploading the revised CSV file with added or removed records. Updating data replaces the data that was previously stored in the associated Data Lake Object (DLO). It also ensures that the data stream is always current with the latest information.
Create a Container for Segmentation: When a filter is defined using a related attribute, a container is created on the canvas with options to define the rules that you need. You can include up to 20 filters within a container.
Use an Activation Template When Creating an Activation: Use an activation template when creating an activation to automatically fill in settings like contact points, source priority, filters, and enrichment data. You can then review and adjust these settings before saving your activation.
Use Amazon SageMaker Models: You can build, train, and deploy custom machine learning (ML) models with Amazon SageMaker and data in Salesforce. After registering a model in SageMaker, define your prediction criteria and connect the model with Data 360 to get predictions and insights for your use case. Use the insights to enrich customer profiles, create segments, and personalize experiences across various channels.
Replicate Data 360 Sandboxes Using Cloning: Clone an existing parent sandbox to reuse Data 360 metadata and configurations, create testing environments faster, and streamline complex DevOps workflows. When you clone a sandbox with Data 360 provisioned, you automatically carry over the same Data 360 metadata and configurations to the cloned sandbox. This process saves setup time and avoids manual configuration.
Use a Package Subscriber Org for Testing: When using a Developer edition org in the context of a packaged app, Salesforce uses the terms Packaging org and Package Subscriber org. The Packaging org is where the package is created. The Package Subscriber org is an org that has installed a package from a Packaging org. To speed up testing, developers can now install their package in another Dev org and manually trigger an activation publish.
Use Unified Lineage: Explore and interact with the lineage graph to view how your data objects connect. Search for objects, trace upstream and downstream dependencies, and use side-panel details, field-level views, and tag overlays to understand your data flow and governance context.
Use Your Customer ID as a Party Identifier: By default, the Marketing Cloud Personalization bundle maps the Profile ID to the Data 360 Individual ID. Personalization also connects the Salesforce Marketing Cloud contact key to the party identification number. To unify profiles for identity resolution, use your own Customer ID as the party identifier in Marketing Cloud Personalization and Data 360.
Data 360 Standard Permission Sets: A permission set defines the level of access and visibility that Salesforce users have to data and features in Data 360. Depending on your license, there are several standard permission sets provided for Data 360. The Salesforce admin assigns permission sets to users. We don’t recommend creating a custom permission set for Data 360 access, however you can combine the permissions sets with other Salesforce permission sets.
Customer Data Platform Standard Permission Sets in Data 360: A permission set defines the level of access and visibility that Salesforce users have to data and features in Data 360. Four standard permission sets are provided for orgs contracted under a Customer Data Platform license. The Salesforce admin assigns permission sets to users.
Custom Permission Sets in Data Cloud: To create a Data Cloud custom permission set, we recommend cloning an existing standard Data Cloud permission set. Some Data Cloud features can’t be accessed with permission sets created from scratch.
Grant Data Cloud One Access to Users: Users in companion orgs must be assigned the Data Cloud One User permission set to access Data Cloud One in their org. To view data in shared data spaces, users must be assigned permission sets that have the data spaces they need associated with them.
Impact of Custom Permissions on Policies: Custom permissions in Data 360 play a key role in determining how a policy is applied. You can assign them to users through custom permission sets or profiles to act as user attributes within the policy builder.
Use Data 360 Data in Tableau: Tableau enables metadata and data discovery of all objects in your Data 360 instance with Salesforce security mode.
Use Data Cloud APIs with Data Spaces: To use Salesforce Data Cloud APIs, you must be authenticated. Create an external client app to authenticate and request access to the Data 360 APIs.
Data Tagging and Classification in Data 360: Data tagging is the process of attaching labels or metadata to data assets such as objects, fields, or records to describe their content, sensitivity, or purpose. Tags help identify and organize data so it can be easily searched, filtered, and governed.
Using the Aggregate Node: The aggregate node performs a calculation on a set of values and returns a single value. For example, Average returns the average value of a numerical column. You can use dimensions to roll up the records to a higher granularity. Dimensions, which are typically textual, categorize and describe data by supplying qualitative data points. They are commonly used to filter and segment data based on specific characteristics, such as customer ID, product type, location, or date. Dimensions provide context for measurements, which allows for analysis across different aspects of a dataset.
Using the Case Node: The case node, similar to the CASE statement in SQL, analyzes data by comparing the attributes of conditions. The WHEN/THEN statements determine the outcome based on whether all or any of the specified conditions are true. If none of the WHEN conditions are met, the ELSE statement provides an alternative action. The Case node statement must be followed by at least one WHEN/THEN pair, which applies a designated function when the associated condition is true.
Using the Filter Node: The filter node uses the selected operator and value to keep or remove data based on whether any or all of the specified conditions are met.
Using the Join Node: The Join node combines data from multiple sources based on shared values. A column represents a category of information, such as an opportunity source or account name.
Using the Transform Node: Use a Transform node to manipulate data based on the transformations added to the node. To manipulate data at multiple stages of a data transform, add a separate Transform node at each stage.
Validate an Insight in Data 360: After creation, validate your calculated or streaming insight in Data Explorer to ensure that your data and formulas are accurate. You must validate an insight before you use it in a segment.
Verify and Reauthenticate a Salesforce CRM Connection: Verify your Salesforce CRM connection trust and re-authenticate if disruption was caused by a sandbox refresh, or if trust relationship between the home org CRM connection and Data 360 is broken. If your Salesforce org has a CRM connection to a sandbox org, and that sandbox has recently refreshed, the refresh process generates a new Org ID for the sandbox org that conflicts with the Org ID associated with the existing connection. Because Data 360 can’t establish trust with the refreshed sandbox, the connection to Data 360 is disrupted and requires reauthentication. When the connection is disrupted, data streams associated with it can encounter errors, including deployment failures for new streams and errors within the stream’s refresh history.
Data 360 Videos: Find short videos about Data 360 fucntionality..
View Audience Insights in Data Explorer: View audience insights across different activations for a given target in Data 360. You can view results up to the past 90 days.
View Content Viewer User Engagement Insights in Data Explorer: Data 360 automatically creates an engagement telemetry data stream and a data lake object (DLO) to track user engagement with the Content Viewer. The Content Viewer is available only for harmonized content. You can view engagement data from the Data Explorer tab.
View Data Graph Properties and JSON Structure: Use a list view to see data graph properties, including the primary data model object (DMO), data graph status, and structure. Additionally, you can copy a data graph’s JSON structure from its record home page.
View Filter and Container Level Counts: Use the population counts displayed in the ribbon under each filter or container to evaluate their impact on your segment. Filter-level counts apply to direct attributes; container-level counts apply to related attributes (containers). Counts are available on both the Include and Exclude tabs.
View Harmonized Content Consistently in Data Explorer: The harmonized Unstructured Data Model Object (UDMO) record page, available through the Content Viewer in Data 360’s Data Explorer tab or in Service Cloud's Enterprise Knowledge component, provides you with a unified, in-app experience od all data interactions (structured knowledge and unstructured content). Through the Content Viewer, your harmonized content is presented in a standard, consistent format, regardless of its source.
View Tableau Dashboards: After defining visualizations on your Data 360 data in Tableau, you can view these dashboards by embedding or publishing them.
View Your Data in Data Explorer: In Data Explorer, you can view Data 360 data from your data lake objects (DLOs), data model objects (DMOs), calculated insight objects (CIOs), or data graphs. You can personalize your view by editing the columns and use filters to show only the most relevant data for the selected object.
Viewing Data Relationships with Unified Lineage: Unified lineage gives you a complete view of how your data objects are connected. Use it to understand data journeys, dependencies, and the downstream impact of changes such as applying tags or updating objects
Web and Mobile App Connector: Connect your web and mobile apps to Data Cloud to collect customer engagement data. This data helps you customize messages, create automations, or support AI projects. After it’s connected, set up data streams to ingest your data into Data Cloud, allowing you to access and query this data for various uses.
Working with Nodes in Visual Insight Builder: Visual Insight Builder uses a low-code, drag-and-drop interface to simplify the creation of streaming, calculated, and real-time insights. Visual components replace complex code, allowing you to develop metrics and expressions, regardless of technical expertise.
Advanced Configuration for Data Graphs: In Data 360, advanced configuration for data graphs allows you to customize the data graph beyond the default settings.
Clone a Data 360 Sandbox: Create a Data 360 sandbox by cloning an existing configured sandbox to save time and maintain environment consistency. Cloning saves time by automatically copying data model objects, relationships, identity resolution rules, and data stream definitions from the source sandbox to the clone.
Deploy Data Kit Components in Data 360: After you create and install a standard data kit in a managed package, you can deploy package components such as data streams, batch data transforms, calculated insights, and data graphs from Data 360 with Package Manager in your target org.
Uninstall a Deployed Data Kit in Data 360: Removing unused or outdated data kits keeps your org organized and efficient. To uninstall a data kit deployed from a managed package, first undeploy the deployed data kit components and their dependencies from all data spaces. Then, uninstall the managed package.
Update Database Configuration for Zero Copy Data Streams: When deploying a data kit that contains zero copy data streams, you can update the target database, schema, or table after deployment to match your target environment.
Data 360 Reports and Dashboards: Easily create standard reports on single or multiple related data model objects (DMOs) or Calculated Insights to determine which areas of your business to focus on. Or, build a custom report type on DMOs to get information on the most pertinent fields and records from Data 360. You can group, filter, and summarize records in the report, run it for near real-time information and share it with others. And, make business decisions faster by visualizing KPIs and trends from Data 360 reports in your dashboards.
Gain Insights on Data with CRM Analytics: Data 360 allows you to perform exploratory analysis on your data at different stages of its lifecycle using CRM Analytics. You can build contextual insights and create interactive views on a data lake object (DLO), data model object (DMO), or calculated insights object (CIO) without loading the data into CRM Analytics. You can filter the Data 360 data, drill deeper, and act on the data directly from the dashboards.
