Hello! I’m Mark Horseman, and welcome to The Cool Kids Corner. This is my monthly check-in to share with you the people and ideas I encounter as a data evangelist with DATAVERSITY. This month we’re talking about Non-Invasive Data Governance (NIDG). If I haven’t already given it away, our featured Cool Kid is none other than Bob Seiner, principal of KIK Consulting & Educational Services and the provider and inventor of NIDG. With the rise of organizations working with, dealing with, and implementing AI, there’s no better time to talk about a Data Governance methodology that can help organizations better understand, manage, and formalize the rules of engagement around their data.
I’ve been a follower and practitioner of Non-Invasive Data Governance for a very long time now. What I appreciate most about the methodology is Bob’s mantra, “Everyone is a data steward – get over it.” What he means is that everyone has a relationship to data, and, to some degree, everyone is formally held accountable for that relationship.
When implementing NIDG frameworks myself, I often tell folks, “We’re here to help, not to change.” The framework is largely concerned with what it is people are doing already and formalizing those things. A data governance manager can get a long way with listening to folks and writing things down. After listening to folks and understanding the business relationship with data, certain things become very clear: what roles are done by whom within an organization, and to what extent individuals within an organization contribute to the production, use, and definition of data. In the past, I’ve been able to start up successful data governance programs just by documenting who’s who with respect to data and working with other stakeholder groups to share that information within the organization, by way of the common data matrix.
This leads us to the world’s current fascination with artificial intelligence, specifically the preponderance of large language models (LLMs) quickly becoming the fabric of the 2020s’ cultural zeitgeist. In data quality and data governance, we like the saying “GIGO” for “Garbage-In Garbage-Out,” but as a friend pointed out to me, we could now be saying “Good-Inputs Good-Outputs” as it relates to LLMs. What framework supplies the means to have “Good-Inputs”? You guessed it – our trusty old friend data governance. A clear understanding and formalization of the rules of engagement surrounding data at your organization are critical to any endeavor relating to AI.
Check out what Bob is up to:
Remember that you can meet and join Cool Kids like Bob at DATAVERSITY events:
Want to become one of the Cool Kids? All you need to do is share your ideas with the community! To be active in the community, come to DATAVERSITY webinars, participate in events, and network with like-minded colleagues.
Next month, we’ll be looking at driving data projects!