Recently, I was “awarded” the task of creating a data governance framework and process for our organization. We had many pieces in place, data management, protection, security, organization, and even a certain standardization level; however, we had several committees ruling over each area independently without an overarching and cohesive program.

To that end, I decided to create several posts relaying what I’ve learned about data governance, starting with what is it, where do I start, and what tools do I need.

Over the next few weeks, I’ll touch on all aspects of our journey into data governance. The goal is to provide you with enough knowledge to ask the right questions and begin the journey to better corporate data governance.

For now, I’ll touch on one of the first questions I asked myself, what is the difference between data governance and data management? I discovered, there are as many answers to this question as “answerers.” Below is the definition which became my starting point.

Data management is mostly an IT practice to organize and control your data resources so that it is accessible, reliable, and timely whenever users call on it. It encompasses the entire lifecycle of a data asset, including protection, availability storage, and many other aspects.

Data governance is more of a business strategy. Its purpose is to provide definite answers to how a company can prioritize data’s financial benefits while mitigating the business risks of insufficient or flawed data. Essentially we built a partnership between the IT and business teams to develop and provide proper guidelines, rules, framework, and, well, yes, “governance,” which is put into action by our IT team.

Having learned from experience, I try to find the best possible foundation on which to build for all my any projects. In data governance, I found the best foundation was data intelligence. I the next post, I’ll dive into my research one using intelligence in your governance framework.