How can we ensure our company has mature data governance processes in place?

To make sure your data governance is up to scratch, you need a well-rounded plan that includes a smart strategy, the right tech, and a company culture that's all about data. You've got to get everyone onboard, making sure they know their jobs when it comes to handling data, and use some solid systems and tech to keep everything under control. Plus, it's not a one-time thing – it’s more like a road trip where you keep tuning things up along the way. You've got to stay in sync with what your organization wants and the rules you need to follow, while also making sure people are taking this data stuff seriously at every step of the ladder.

Start with value. Until data is treated like an asset, data governance doesn't connect to value. It's important to implement a data valuation method and assign value to datasets. Then data security makes sense because the investment in that aspect of data governance protects the business's data assets. Data quality makes sense because each curated dataset increases the business's value and opportunities. You can break down each pillar of data governance in the same way. Once you can define data as an asset, you can define data governance in terms of value and implement processes that deliver value.

My belief is that maturity is a goal post. I do not think an organization should strive to be on the highest level of maturity since the goal post keeps moving every time when a new AI tech shows up in the Data Management space. However, Data Governance has to be aligned with business unit and organizational level objectives. In addition, core functions of DG such as Catalog and Data Quality has to be in place as a default. In addition, data observability is the key to success.