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5 Biggest mistakes in Data Governance Program Execution & how to avoid them
Those who followed my Data Governance 101 6-part series, ask me a multitude of scope questions about how to set up their data governance programs.
Several times, I get startling questions, sometimes naive, sometimes overthinking the application of various industry tools and methodologies.
Given the lack of quality practitioners in the industry (Financial Services, or otherwise), it is not surprising to hear those, I figured I will share some of my insights.
I won’t regurgitate the foundations from my past series, but rather I would focus on major program-level mistakes that are tough to “take back” if you happen to be leading one of these large programs or a stakeholder.
In this era where the tug of war is between data privacy and data monetization (product view), it is critical to underpin the data management activities with proper data governance.
From a nomenclature point of view, I would like to highlight the often misused and interchanged words, data management vs data governance. This is a decent summary.
What is data governance, anyway? It might actually be worth reading what other consortiums defined it as, a quick glossary here by Mr. Firican.
