Here's how you can effectively learn from failure in data governance.

Thank you, @MaudSchlich. The best practice I have seen is this (change to the life cycle definition): 1. Person assigned to be manager is required to look through all retrospectives. 2. Person prepares a new project plan that specifically addresses every positive (how to maintain/continue it) & every negative (how to avoid or mitigate). Retrospectives actually become read. And this practice changes everything: A. Retrospectives begin to be fashioned in a way to be consumed, and B. Projects do not repeat previous findings. This transforms retro reports into a data base of experience that is actually relied upon for future project success. In the past I have read many retrospectives. They are almost always all the same! How embarrassing!

Embrace the Inevitable: Data governance initiatives are complex, and setbacks are a natural part of the process. The key is to view these failures as learning opportunities for improvement. Conduct a Post-Mortem: Gather your team to analyze what went wrong. Identify the root causes of the failure, whether it was unclear policies, inadequate communication, or technological shortcomings. Focus on Solutions, Not Blame: Don't dwell on assigning blame. Instead, brainstorm potential solutions to address the identified issues.

Organizations struggle face a number of challenges when implementing data governance. These challenges either prevent the DG program from gaining ground in the first place or derail the program at some point after takeoff. While common failure points are lack of DG awareness in the organization, lack of meaningful metrics to measure DG program progress, lack of executive support, it is important to do a retrospective on what went wrong and then proceed from there.

Don’t make it a bureaucracy and focus on solving tangible business problems. Make sure standards are value-added and not theoretical. Look to change data culture towards collaboration and not a top-down one-size-fits-all approach.

A thorough analysis of data governance failures is crucial. By identifying root causes without blame, organizations can improve policies, allocate resources better, and enhance stakeholder engagement. This approach turns setbacks into opportunities for strengthening data management practices.

Iterate, iterate quickly, don't get stuck with ideas. Share the learnings with all involved. Failures bring you one step closer to success.

A great principle in data governance implementations is continuous monitoring and improvement. It ensures that outcomes are measured against expectations continually. The data governance team can subsequently improve on areas of failure and underperformance uncovered through continuous monitoring.

In this buzz-wordy atmosphere we live in, it is important to establish a common language. Nomenclature or basic definition will go a long way in the common understanding of the goals and process.

Share Learnings: Don't let the knowledge gained from failures stay siloed. Share your learnings with other teams within the organization to foster a culture of continuous improvement in data governance.

Data Governance is change management. Its about people, data culture, maturity and Literacy There should be multiple awareness sessions, effective change management and culture assessment addition to training . Ensuring Leadership is onboarded for the initiative from the beginning. Data Governance comes with great responsibilities and accountability beyond the existing business process for the stakeholders who are embracing this journey

Data gov is a living document. Constantly adapting and evolving. Once upon a time a truth, is now a failure as it no longer fitting the current technical or business landscape.