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title: Tableau data and content governance
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locale: en-gb
portal_url: https://docs.salesforce.com/tableau/next/en-gb/blueprint/bp_data_and_content_governance.md
canonical_url: https://help.tableau.com/v0.0/blueprint/en-gb/bp_data_and_content_governance.htm
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description: Blueprint guidance on defining centralized, delegated, and self-governing data and content models.
---
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# Tableau data and content governance

> [!TIP]
> 
> This content is part of Tableau Blueprint – a maturity framework allowing you to zoom in and improve how your organisation uses data to drive impact. To begin your journey, take our [assessment](https://www.tableau.com/en-gb/learn/webinars/tableaus-blueprint-self-assessment).

The data and content governance models worksheet, found in the [Tableau Blueprint Planner](https://mkt.tableau.com/TableauBlueprintPlanner.xlsx), walks you through the key considerations for defining centralised, delegated and self-governing models. This will help you document who is responsible for each area, and what the designated person or team should do. For more information about each area, see [Governance in Tableau](https://help.tableau.com/v0.0/blueprint/en-gb/bp_governance_in_tableau.htm) and [Tableau governance models](https://help.tableau.com/v0.0/blueprint/en-gb/bp_governance_models.htm).

## Data governance

Data source management: includes processes related to selection and distribution of data within your organisation.

- What are the key sources of data for a department or team?
- Who is the data steward or owner of the data?
- Will you connect live or extract the data?
- Should the data source be embedded or published?
- Do variants of a dataset exist? If so, can they be consolidated as an authoritative source?
- If multiple data sources are consolidated, does the single data source performance or utility suffer by attempting to fulfil too many use cases at once?
- What business questions need to be answered by the data source?
- What naming conventions are used for published data sources?
- Is there a need for multi-org connectors (CRM Analytics only)?
- Will you need to use output connectors (CRM Analytics)?
- Are you considering any ETL tools or API calls to push data from on-premises sources (CRM Analytics)?
- Will you need to create multiple local connections to your Salesforce org (CRM Analytics)?

Data quality: an assessment of data's fitness to serve its purpose in a given context.

- What processes exist for ensuring accuracy, completeness, reliability and relevance?
- What processes exist to scope upstream/downstream implications of adding or deleting fields from data sources?
- Have you developed a checklist to operationalise the process?
- Who needs to review data prior to it becoming shared and trusted?
- Is your process adaptable to business users and are they able to partner with data owners to report issues?

Enrichment & preparation: processes used to enhance, refine or prepare raw data for analysis

- Will data enrichment and preparation be centralised or self-service?
- What organisational roles perform data enrichment and preparation?
- What ETL tools and processes should be used to automate enrichment and/or preparation?
- What sources of data provide valuable context when combined with each other?
- How complex are the data sources to be combined?
- Will users be able to use Tableau Prep Builder and/or Tableau Desktop to combine datasets (or recipes in the case of CRM Analytics)?
- Have standardised join or blend fields been established by the DBA to enable users to enrich and prepare datasets?
- How will you enable self-service data preparation?
- How often should enrichment and preparation processes occur (hourly, daily, monthly) and how will you be notified of failures?
- Which approach are you going to use to combine datasets and/or preserve the dataset's grain level (CRM Analytics)?
- How will you implement feature engineering or hybrid data required for stories/models in predictions (CRM Analytics - Einstein Discovery)?

Data security: protective measures that are applied to prevent unauthorised access to data

- How do you classify different types of data according to its sensitivity?
- How does someone request access to data?
- Will you use a service account or database security to connect to data?
- What is the appropriate approach to secure data according to sensitivity classification?
- Does your data security meet legal, compliance and regulatory requirements?
- Are you planning to use sharing inheritance from Salesforce or security predicates for data row-level security (CRM Analytics)?
- Are you using Salesforce Shield and require reporting against masked fields or encrypted datasets (CRM Analytics)?
- Have you established asset level access for different Salesforce users/groups/roles (CRM Analytics)?
- Are there requirements for using summarised/aggregated datasets to mask detailed level analysis?

Metadata management: the end-to-end process for creating, controlling, enhancing, attributing, defining and managing a business-friendly semantic layer of data

- What is the process for curating data sources?
- Has the data source been sized to the analysis at hand?
- What is your organisational standard for naming conventions and field formatting?
- Does the Tableau data model (fields or dataset XMDs for CRM Analytics) meet all criteria for curation, including user-friendly naming conventions?
- Has the metadata checklist been defined, published and integrated into the validation, promotion and certification processes?
- Have you identified and enabled actionable fields at each dataset level (CRM Analytics)?

Monitoring & management: process used to measure successful job execution

- Are schedules available for the times needed for extract refreshes?
- How is raw data ingestion monitored from source systems? Did the jobs complete successfully?
- Are there duplicate sources of data?
- When are extract refreshes scheduled to run? How long do extracts run? Did the refresh succeed or fail?
- Who should receive job execution alerts and notifications?
- Are subscription schedules available after extract refreshes have occurred?
- Are data sources being used? By whom? How does this compare with the expected audience size?
- What is the process for removing stale published data sources?
- What is the process to clean up unused datasets, including those in private apps (CRM Analytics)?
- Is orchestration (scheduling) needed for syncs, dataflows and recipes (CRM Analytics)?
- Who is responsible for monitoring deployed prediction data alerts (CRM Analytics - Einstein Discovery)?
- Who is responsible for refreshing models, including the datasets themselves (CRM Analytics - Einstein Discovery)?

## Content governance

Content management: processes used to keep workbooks and data sources fresh and relevant

- Will workbooks and data sources be shared across the company?
- Will sites be used to isolate sensitive content or departments?
- Will projects use an organisational (departments/teams), functional (topics) or hybrid approach?
- Have sandbox and production projects been set up to support ad-hoc and validated content?
- Are content naming conventions used?
- Are authors publishing multiple copies of the same workbook with different filters selected?
- Does content have a description and tags? Does it comply with visual styles?
- Do you have a load time expectation and an exception procedure in place?
- Is there a process to reassign content ownership?
- How are you going to manage apps and publish analytics assets (CRM Analytics)?
- Will you restrict users to saving their analytics assets to their private app only (CRM Analytics)?
- Are there any deployed analytics apps from templates or integrated service providers that need to be managed (CRM Analytics)?

Authorisation: process of defining permissions model enable access to data and content

- What is the minimum site role for Active Directory/LDAP group synchronisation?
- Have you set all permissions for the All users group in the Default project to None?
- Are any explicit restrictions (Deny permissions) needed on the All users group to propagate to every user account?
- Have you created groups that correspond to a set of authoring and viewing capabilities for each project?
- Have you reviewed effective permissions on select users to test your permissions model?
- Have you locked permissions at the parent project to maintain security throughout the project hierarchy?
- Have service account usernames/passwords been established for published data sources?
- Are you provisioning users into the Salesforce org that has CRM Analytics enabled (CRM Analytics)?
- How are you managing and assigning permission sets related to CRM Analytics PSLs and permissions (CRM Analytics)?
- Have you planned permissions for apps and mapped them to users/roles/groups from the Salesforce org (CRM Analytics)?
- How are you securing access to connectors with account login information to data sources (CRM Analytics)?
- Have you enabled read access for the Analytics Integration User profile for the required custom fields and objects (CRM Analytics)?

Content validation: process used to verify that content is correct

- Who is involved in the validation process?
- Is the content accurate, complete, reliable, relevant and recent?
- Does new content replace existing content?
- Are the underlying data and calculations correct?
- Does the content reflect corporate branding?
- Does the content have a logical layout?
- For data visualisations, are all axes and numbers formatted correctly?
- Do dashboards load within the acceptable performance time?
- Do filters and dashboard actions behave on the targeted views?
- Does the dashboard remain useful in edge case behaviours (filtered to all, none, one value, etc.)?
- Who is tuning models and verifying model metrics (CRM Analytics - Einstein Discovery)?

Content promotion: process used to bring content from sandbox project to production project

- Who is involved in the promotion process?
- Do content-promoting roles have a checklist of criteria to evaluate?
- Have you clearly delineated between certified content and ad-hoc content by projects?
- Is the process agile, to support iterations and innovation?
- Do you have workflows to address both direct and restricted sources of data and workbooks?
- What migration methods will you use for deploying analytics assets from sandbox to production (Tabelau CRM)?
- Will you conduct a dark launch for predictions? Will you deploy predictions to specific users in stages (CRM Analytics)?

Content certification: process used to verify that content has been vetted and can be trusted in operational state

- Who is responsible for designating certified content?
- Have all criteria for achieving certification status been met?
- Are all fields completed: about, certification notes, tags?
- In the case of stories and models, who is certifying the model metrics to be deployed as predictions (CRM Analytics)?

Content utilisation: processes used to measure user engagement

- How much traffic goes to each view?
- What is the definition of stale content? How often is stale content purged?
- How much indirect utilisation (alerts & subscriptions) occurs?
- Are subscriptions delivered on time?
- Does the actual audience size match with expectations?
- Does content follow a weekly, monthly, quarterly trend?
- What is the frequency of login or days since last login by user cohort?
- What is the distribution of workbook and data source size?
- Are you planning to use the adoption analytics app to monitor utilisation (CRM Analytics)?