Data Governance Maturity Model

Where are you now?

No matter where you are in the data governance stages, Jende can get you where you want to be. Planning and execution start with understanding where you are now. Jende can work with you to determine your starting point, end goal, and anchor points along the way.

Security Ropes AdobeStock_247996426

No Automation or Central Processes

Multiple Accounts to Manage
Low Data Reliability
Fragmented Security Policies

Defined Processes and Emerging Centralization

Fewer Accounts to Manage
Manual Data Quality Checks
Better Data Integration

Mature Processes and Data Management

Faster Data Discovery
Automated Quality
Row-Level Permissions
Whole-Institution Reporting

Advanced Security and Predictive Analytics

High Data Reliability
Automated Data Classification
Scalable and Responsive

What does it look like?

  • Separate processes and systems for data sets and features.
  • Manual approval, access, and data validation processes.
  • Multiple log ins to manage.
  • Changes require significant effort and staffing.

What challenges do you have?

  • Data discovery and validation takes a long time.
  • It is hard to make changes.
  • It is difficult to determine data quality.
  • Users and admins have to learn many different processes.
  • Protecting data is difficult, you cannot quickly see who is accessing information.
Stage 1 - Medium

What does it look like?

  • Systems and processes are better integrated.
  • Data access requests are automated.
  • Data can be accessed more quickly with fewer log ins to manage.
  • Data quality checks are well defined, but manual.

What challenges do you have?

  • Processes too customized and hard to manage.
  • Access and data validation is still slow.
Stage 2-1

What does it look like?

  • System data is better integrated for faster data discovery and data usage.
  • You have fewer UIs, but you still have multiple points of access.
  • Data security is mature with row-level permissions.
  • Automated data quality checks identify issues.

What challenges do you have?

  • Difficult to scale to support new data sets.
  • Quality checks revolve around issues, no predictive analytics. 
Stage 3-1

What does it look like?

  • Advanced permissions-based data access control.
  • Fast access to whole institution dataset.
  • Automated data discovery and classification.
  • High data reliability.
  • High ease of access approval and data validation.
  • Fully automated quality checks with predictive analytics.
  • Centralized data quality monitoring (99% of Issues Diagnosed).

What challenges do you have?

  • Requires advanced system administration skills.
  • Costly for initial implementation.
Stage 4-1