You're mentoring a data governance professional. What's the most important thing to keep in mind?

As a data governance professional, the most important thing to keep in mind is that data governance is not just about policies, processes, and technologies; it ultimately drives business value and enables organizational success through effective management and data utilization. Here are some key points to consider: Business Alignment Stakeholder Engagement Holistic Approach Data Quality and Integrity Compliance and Risk Management Measurement and Monitoring Continuous Improvement Communication and Education Keeping these principles in mind and focusing on driving business value, engaging stakeholders, ensuring data quality and integrity, addressing compliance and risk management, measuring impact, fostering continuous improvement.

Stop talking about "governance" and start talking about "enablement". The best possible value you can derive is _more_ people using high quality data to make decisions and drive value. "Governance" sounds like policies that point toward "No". Enablement sounds like politice that point toward "yes".

Yes, align with the culture. Also harmonize roles used in other programs instead of creating entirely new ones, thus leveraging past learnings and expectations. Remember, data governance is about enablement and risk protection, not control. You may need to emphasize change management more than anticipated, particularly if it's the program's initial launch.

I tell my coachees to focus on the people aspect of data governance. Figure out what personas you need to influence and craft a one-page compelling, visual story of the need for change and what the program can offer in terms of realizing the core strategy for the business. There is simply no substitute for talking to stakeholders about the real problems they have, connecting them to the program strategy, and identifying a prioritized roadmap of initiatives that they can contribute to. Also, making it a fun social experience focused on learning and career progression doesn't hurt!

Spend a piece of each meeting defining what you _actually_ mean by "data governance". The term has gotten so freighted that often people aren't even talking about the same thing. Explain the narrowest possible option, get agreement on what the plan is, and drive toward that. If everyone has a different idea of "governance" then it will take a lot longer to get where you want to be.

The data tools would definitely help in data quality, analysis, classification and choosing a right tool as mentioned above is a challenge and require expertise, also the tool should be compatible with other softwares with less complexity as for as integration , accessibility etc.

One thing that I've seen frustrate those new to the field is balancing the need to immediately fix data quality issues versus finding the root cause. The governance process should note this so that it is apparent what should be done. Often it may be that you need to immediately fix the issue at hand, but then use the data governance process to flesh out the root cause with the help of a larger body and collaboration.

I appreciate the first sentence of this post, it helps me understand that when we implement productive initiatives, it often leads to more opportunities for productivity. As Jim Collins alluded to in his book "Good to Great", a good effort can get the flywheel of success moving and inspire others to innovate and work in a more inspired way. I remember when our team implemented a new chart of accounts to improve financial data management. Once it was done, not only did it become easier to produce financial reports, but we could also easily download financial data and create business intelligence. This BI was often combined with other data sources to make better decisions throughout the organization, resulting in real synergy.

A succssful Enterprise data governance initiative requires people, processes and tools. A RACI Matrix could be used to identify required stakeholders.