Three best practices for data governance programs, according to Gartner
With data analytics playing such a huge role in the success of businesses today, strong data governance has become more vital than ever.
How can organizations ensure they are handling governance in the right way? Here are three suggested best practices, according to Andrew White, distinguished vice president and analyst at research firm Gartner.
First, enterprises should not confuse a data governance program with a focus on data. “Stop inventorying data, and start by prioritizing what data matters most to your organization,” White says. “Use prioritized business outcomes to identify [which] data drives what important outcomes.”
Second, companies need to understand that not all data is equal, and stop trying to govern all data equally. “Investing in a data catalog as a first step for a modern data and analysis governance program is not very helpful,” White says.
Companies should determine three things, White says: the least amount of data with the greatest business impact, and then govern this data well; the data that impacts parts of the business, and govern this less well; and the bulk of data used by the fewest number of people, departments, or processes, and govern this least well.
“This will help you focus your limited resources on the data that matters most,” White says.
The third best practice is to work out how business exceptions, processes, or decisions that are held hostage to bad data can be quickly routed to experts for resolution. “Those data issues will be solved, since they materially impact the success of that business task,” White says. “Focusing on data issues in a data warehouse or lake, far removed from an outcome, is not very practical.”