---
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title: Using Unstructured Data in Data 360
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locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_unstructured_data_connect_overview.md
canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_unstructured_data_connect_overview.htm&release=264.0.0&type=5
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content_type: documentation
content_hash: 1a5529c144054f579597cc7d10bb407fe0dfbb55ebfcbd46fa9d9f7df2e94e51
description: 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.
products:
  - Data 360
release: "264"
---
# 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.

Unstructured data comes in many forms. Consider these common examples.

 
| Type of Data | Examples |
| --- | --- |
| Text documents | Case notes, reports, internal memos, handwritten notes, invoices, business cards, and emails that contain detailed descriptions or conversations |
| Multimedia files | Audio files from customer service calls, podcasts, video recordings from training sessions, tutorials, meetings, images from product reviews, infographics, scanned documents, and medical imaging |
| Social media content | Comments, posts, or messages containing customer feedback, opinions, or sentiments |
| Web content | Blogs, articles, and forum posts that discuss products, services, or customer experiences |
| Chat logs | Conversations from live chat support, often containing nuanced questions, complaints, and preferences |
| Sensor and IoT data | Data from wearable devices or machinery that doesn't follow a standard format, such as raw accelerometer data |
| Customer feedback | Survey responses, feedback forms, or reviews written in free text |

Data 360 supports several connectors and file formats for unstructured data. See [Unstructured Data Connectors](https://help.salesforce.com/s/articleView?id=data.c360_a_unstructured_data_connect.htm&release=264.0.0&type=5) and [Data 360 Limits and Guidelines](https://help.salesforce.com/s/articleView?id=data.c360_a_limits_and_guidelines.htm&release=264.0.0&type=5 "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.").

Using unstructured data in your AI, automation, and analytics workflows can help you make informed decisions and improve customer experiences.

## Agentforce Agents

Data 360 provides customer data to make agents more contextually aware and knowledgeable. For example, when a customer contacts an Agentforce Service Agent, Data 360 can add unstructured data, such as information from past emails, support tickets, product photos, or voicemails, to help the agent to better understand the customer's perspective. Data 360 hybrid search can then find the appropriate knowledge articles to resolve the issue. When a service case is resolved, Data 360 can guide the agent with next steps, such as automating follow-up emails or preparing detailed chat summaries.

Here's an example of an agent in action.

## Generative AI

Retrieval augmented generation (RAG) improves AI responses by grounding them in unstructured data, making them more relevant and accurate. To learn more, see [Using Retrieval Augmented Generation](https://help.salesforce.com/s/articleView?id=data.c360_a_rag_overview.htm&release=264.0.0&type=5 "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.") and [Create a Vector Search Index with Advanced Setup](https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_create_vector_index_config.htm&release=264.0.0&type=5 "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.").

In Service Cloud, agents can use hidden data in knowledge articles to answer customer questions more accurately. By working with Data 360, agents can look at unstructured data, like emails, support tickets, and chat logs, to get more information and write better responses, including suggestions for fixing problems before they happen.

In Sales Cloud, agents can use unstructured data, including emails, call transcripts, and notes, to generate detailed and contextually relevant meeting briefings and summaries. These insights improve new emails with personalized content and customer-specific recommendations, improving the customer experience and sales team effectiveness.

## Automation

Agents can automate the identification of similar records, such as Cases, based on their descriptions to flag duplicates and speed up resolution. Additionally, agents can classify records by finding and using semantically similar ones, improving efficiency and accuracy.

## Analytics

Agents can use Tableau to gain insights from unstructured data, such as topic classification. Check out the example in [Enhancing Data Analysis with Vector Search in Tableau](https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_vector_search_analytics.htm&release=264.0.0&type=5 "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.").

## Team-based Examples

Consider these use cases for how to use unstructured data across the various functions.

    
| Team | AI | Automation | Analytics | Search |
| --- | --- | --- | --- | --- |
| Sales | Draft responses to RFPs using previous proposals, contracts, and documentation. | Analyze email conversations to automate lead scoring. | Analyze recorded sales calls to spot patterns in competitive insights or feature interests. | Prep sales calls with insights from thousands of PDFs and emails. |
| Service | Estimate repairs through photo analysis. | Proactively send repair recommendations by identifying patterns in agent assets. | Analyze chat and product logs to identify common recurring service problems. | Get context-based knowledge article recommendations. |
| Marketing | Find subject lines that have worked on similar email templates. | Automate campaign follow-up based on specific product interests found in support chats. | Spot trends based on customer feedback on websites. | Search feedback from surveys to create more responsive campaigns. |

#### See Also

- [_Trailhead_: Unstructured Data in Data 360](https://trailhead.salesforce.com/content/learn/projects/unstructured-data-in-data-cloud.html)
- [_Salesforce Help_: Use Search for AI, Automation, and Analytics](https://help.salesforce.com/s/articleView?id=data.c360_a_search_index_ground_ai.htm&release=264.0.0&type=5 "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.")
- [_Salesforce Help_: Design and Implement Agents](https://help.salesforce.com/s/articleView?id=ai.copilot_intro.htm&language=en_US)