You're juggling agile projects and data governance rules. How can you find the perfect balance?
Navigating the intersection of agile methodology and strict data governance can be challenging, but it's crucial for maintaining both innovation and compliance. Here's how to strike the right balance:
How do you balance agile projects with data governance in your organization? Share your strategies.
You're juggling agile projects and data governance rules. How can you find the perfect balance?
Navigating the intersection of agile methodology and strict data governance can be challenging, but it's crucial for maintaining both innovation and compliance. Here's how to strike the right balance:
How do you balance agile projects with data governance in your organization? Share your strategies.
-
these are my thoughts on putting a strategy on maintaining the balance- Embed governance into workflows: Define clear guidelines and include compliance in the Definition of Done. Automate processes: Use DataOps tools and CI/CD pipelines for data validation and compliance. Enable cross-functional teams: Appoint governance champions to align priorities with agile delivery. Adopt risk-based governance: Focus stricter rules on sensitive data while allowing flexibility for low-risk areas. Promote shared responsibility: Train teams to view governance as a collective goal. Use feedback loops: Refine governance practices through retrospectives and team input. Scale policies by maturity: Adjust governance to fit the project's stage and criticality.
-
With Data as product kicking in across industries, DG has become more embedded and agile to say. Stewards being part of squads enable DQ/Metadata and privacy/compliance aspects, this allows literacy and data culture to sink in. Moreover the product is ready to consume with all required guardrails.
-
By making the Data Governance Framework itself Agile. In fact, we call it EDG (Enterprise Data Governance) because besides having a Corporate approach, it is also Complete (Governance+Quality+Security&Privacy), Federated and AGILE.
-
Balance shouldn't be a challenge if the data governance team did their job properly. For example, the agile team creates new data, the process for documenting it should be straight forward. if the agile team creates new data, the process for documenting it should be straightforward. Agile projects need to move quickly. The problem they can face is that the quality of the application is substandard. Data governance should help the projects in the following ways: 1. High quality data, free from defects 2. Precise definitions to data elements so that the IT team doesn't have to guess at them 3. Valid values for data elements 4. Data lineage so the team knows the source of the data 5. Which analytics currently use the data
-
Apart from applying the control processes, infusing space is vital to achieving the organization's DG maturity level balance. Any organic growth requires not just time but space to progress. Allowing space for success and failure grew the strategy, innovation and compliance.
-
I believe that the data gov framework should align the DG Programs on What needs to be done with the PMO/Project management aspects with How to get it done. Data governance practices sometimes do tend to plan more to perform than more than necessary and bled into other camps. Data governance is Governance and not Management. Let the other practice areas do what they do best.
Rate this article
More relevant reading
-
Financial ManagementHere's how you can harness creativity to create groundbreaking financial products and services.
-
Program ManagementWhat are the best techniques for managing dependencies in a matrix organization?
-
Agile MethodologiesWhat techniques can you use to increase the business value of user stories?
-
Value Stream MappingHow do you communicate and visualize the value stream map to your agile sponsors and customers?