Here's how you can effectively respond to failure in data governance.

Before you can even acknowledge a failure you should understand how you would detect for failures. What are the monitors or indicators that you establish up front to inform? Some of the common ones are based on things like DAMA Data Quality Dimensions, however others flow from things like security posture, RACI matrices for data artefacts and policies and procedures that you might implement to be compliant and respect of laws and standards that relate to your data.

I think this is a good step, but I actually do not think it is the first step. Before being able to acknowledge that something is an error, you have to establish what at least some kind of minimal standards or accepted practices are. Once you have clarified these, and they could be very straightforward in terms of protecting the access to PII data, you can then establish what is an error. Once you have established what good looks like, now it becomes much more straightforward to establish what would constitute an error. It would also accelerate all of the next steps in this framework because you have established some initial agreement around best practices.

Firstly, assess what went wrong without blaming individuals. Next, communicate openly about the failure to learn from it. Then, revise the governance processes to prevent similar issues in the future. Finally, continuously monitor and adapt your data governance strategies to maintain effectiveness. Remember, failures are opportunities for improvement!

Learn from Mistakes: Every failure offers valuable lessons. Take the time to analyze what went wrong, identify the root causes, and understand how you can prevent similar issues in the future. Documenting these lessons learned can help inform future decision-making processes.

Causes can be varied, as suggested, they could be because of an inadequately socialized DG program; inadequate accountabilities; a lack of rigour in reporting and 'enforcement' if enforcement is not a thing - then it has to be consequences associated with poor DG program subscription. Another cause can be lack of executive sponsorship. Generally the clues to failing DG programs are tied to data loss events, but they can also be tied to unanticipated or unwanted outcomes associated with inadequately controlled data or poor quality data. The main thing to consider is that DG is as much cultural as it is policy, procedure, controls and technology. Is the business culture aligned with the mission of the DG program?

Revising data governance policies is crucial for effectively responding to failures. In my experience, it's important to use failures as opportunities to identify gaps, involve stakeholders in the revision process, leverage automation, and commit to regular reviews. By taking a proactive approach and customizing policies to align with evolving business needs, I've been able to turn governance failures into valuable learning experiences that ultimately strengthened the overall program. The key is to remain agile, collaborative, and committed to continuous improvement.

1) Inform relevant stakeholders and explain how you’re addressing the issue to regain trust. 2) Review Policies and Procedures: Identify gaps or ambiguities. Strengthen access controls, monitoring, and validation. 3) Re-evaluate risks associated with data governance and update plans based on lessons learned.

You may need to do a heap more than change or adjust policies. You may need to do a DG program reset or relaunch. Make sure you have executive sponsorship if you need to do more than a few policy tweaks.

What isn't monitored or measured cannot be reasonably commented on. You're also not monitoring compliance as much as you're monitoring adherence. You're looking at behaviours in the data ecosystem that are misaligned or inconsistent with the dg policies and procedures that you have. Sometimes these deviations are because they P&P is no longer relevant or never was.

Here are steps to navigate effectively: 1) Investigate: Understand why the failure occurred. Was it a process issue, lack of training, or misalignment? Feedback Loop: Gather insights from stakeholders, data users, and stewards. 2) Continuous Improvement: Treat failures as learning opportunities. Document lessons to prevent recurrence. Adjust processes, policies, or tools based on feedback.