Tableau Data and Content Governance

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The Data and Content Governance Models worksheet, found in the Tableau Blueprint Planner, walks you through the key considerations for defining centralized, 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 and Tableau Governance Models.

Data Governance

Data Source Management: includes processes related to selection and distribution of data within your organization.

  • 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 fulfill 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 premise 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 operationalize 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 centralized or self-service?
  • What organizational 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 standardized 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 unauthorized 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 Inheritence 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 summarized/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 organizational 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 to remove stale Published Data Sources?
  • What is the process to cleanup 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 predictions 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 organizational (departments/teams), functional (topics), or hybrid approach?
  • Have sandbox and production projects been setup 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, tags, and 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 save 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)?


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

  • What is the minimum site role for Active Directory/LDAP group synchronization?
  • 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 planner 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 thecontent 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 visualizations, 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 behaviors (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 Utilization: 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 utilization (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 utilization (CRM Analytics)?
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