Who should use the Data Governance workflow?
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Data
Practical execution plan for data governance with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A living governance program with measurable improvements in data quality, security, and compliance each quarter
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A living governance program with measurable improvements in data quality, security, and compliance each quarter
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Atlan to a signed governance charter with defined scope, policies, and accountable roles. Then, you pass the output to Atlan to a complete, searchable data catalog with classification labels and lineage for all critical assets. Then, you pass the output to Soda AI to automated data quality dashboards with <24-hour average remediation time for critical issues. Then, you pass the output to OpenMined to zero unauthorized access incidents and 100% compliance with data security policies in audit reports. Then, you pass the output to Egnyte to 100% of data assets have documented retention policies and automated lifecycle enforcement. Finally, Tableau AI is used to a living governance program with measurable improvements in data quality, security, and compliance each quarter.
Define Data Governance Scope and Objectives
A signed governance charter with defined scope, policies, and accountable roles
Inventory and Classify Data Assets
A complete, searchable data catalog with classification labels and lineage for all critical assets
Implement Data Quality Monitoring and Remediation
Automated data quality dashboards with <24-hour average remediation time for critical issues
Enforce Access Controls and Data Security Policies
Zero unauthorized access incidents and 100% compliance with data security policies in audit reports
Establish Data Retention and Archival Processes
100% of data assets have documented retention policies and automated lifecycle enforcement
Monitor, Audit, and Continuously Improve Governance
A living governance program with measurable improvements in data quality, security, and compliance each quarter
Start by identifying the business context, regulatory requirements (e.g., GDPR, HIPAA), and key data assets. Engage stakeholders to agree on governance goals, such as data quality, security, or compliance. Document the scope in a charter that outlines roles, responsibilities, and success metrics.
Why Atlan: Atlan provides comprehensive data discovery, cataloging, and governance capabilities, making it ideal for defining governance scope and managing policies and roles.
Conduct a comprehensive data discovery across databases, data lakes, file shares, and cloud storage. Use automated scanning tools to catalog metadata, lineage, and sensitivity. Classify each asset (e.g., public, internal, confidential, restricted) based on the policies defined in Step 1.
Why Atlan: Atlan excels in data discovery, cataloging, and governance, directly supporting inventory and classification of data assets with lineage tracking.
Define data quality dimensions (accuracy, completeness, consistency, timeliness, uniqueness) and set measurable KPIs. Deploy automated checks that run on ingestion or scheduled intervals. Create a remediation workflow for flagged issues, with ownership assigned to data stewards.
Why Soda AI: Soda AI offers data quality monitoring, anomaly detection, and data contract enforcement, directly addressing rule execution and monitoring needs.
Translate governance policies into technical controls using role-based access control (RBAC) and attribute-based access control (ABAC). Implement data masking or encryption for sensitive fields. Regularly audit access logs and revoke unused permissions.
Why OpenMined: OpenMined enables private model training and secure multi-party data analysis, aligning with access control and privacy-preserving policy enforcement.
Define retention schedules based on legal, regulatory, and business requirements. Automate archival or deletion of data that exceeds retention periods. Maintain an immutable audit log of all retention actions.
Why Egnyte: Egnyte provides secure file sharing and automated compliance monitoring, directly supporting data retention and archival processes.
Set up continuous monitoring dashboards for governance KPIs (e.g., policy violations, data quality scores, access anomalies). Conduct quarterly audits and stakeholder reviews. Use feedback to update policies, rules, and automation.
Why Tableau AI: Tableau AI provides data analysis, visualization, and predictive modeling, ideal for creating governance dashboards and monitoring.
§ Before you start
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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