How to Expand Data Governance with Successful Citizen Data Science

How do you get business leaders to participate in data governance programs? Many business leaders still believe that it's IT's issue to fix all the problems when there are data issues.

I provide an answer to this dilemma in a recent episode of 5 Minutes with NYIke (embedded at the end of this post), where I discuss how combining citizen data science with data governance can be a digital transformation force multiplier. I also share two other practices that are part of centers of excellence in citizen data science and governance programs.

Citizen Data Science and Data Governance drive Digital Transformation - Isaac Sacolick

Here's a hint: Most of the approach leverages agile data practices and some basic governance. Much like I recommend setting guidelines on prioritizing technical debt in agile software development, I recommend prioritizing addressing some amount of data debt in every agile data sprint.

Addressing Data Debt and Estabilishing Data Governance

And let's face it, many of the issues data governance aims to address are a form of data debt. Establishing data catalogs, adding data definitions, addressing data quality, identifying confidential/personal data, setting data policies, establishing dataops service level objectives, deduping data, and creating master data sources - why did it require big data, predictive analytics, machine learning, artificial intelligence, and AI for organizations to wake up and prioritize proactive data governance?

Sadly, a big spark in data governance only came from regulation, and it's disappointing that most data governance is now in place solely to address regulation and compliance.

Which is why getting executives involved in data science and governance is critical. They don't understand poor data quality until they see it - but when they do, be prepared to explain the role of data owners and data stewards in plain business language.

Enjoy the episode, and reach out to me if you have questions!


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