DATAVERSITY

In every enterprise data platform I have built over the last several years, the conversation has eventually arrived at the same uncomfortable place: The dashboards look fine, the pipelines are running, but the AI built on top of them is confidently producing answers that are quietly wrong. A data analyst who opens a dashboard and sees a freshness score has dropped to 80% will pause, ask a question, maybe escalate to the pipeline owner.

Generative AI systems can produce full marketing campaigns, product images, social videos, brand copy, UI mockups, and localized variations of all of the above in seconds. But as adoption moves beyond experimentation and into production, an important distinction is emerging: There’s a difference between content that can be generated and content that can actually be used. The gap isn’t primarily a model problem. It’s a data problem.

Metaphors can be helpful in allowing us to apply known concepts to new things. But, the metaphors we use also structure our thinking, sometimes in limiting ways. As conversations around AI governance abound, how we think about “what AI is like” starts to directly relate to issues of governance. This month’s question asks… How do metaphors structure our thoughts on AI governance?

Data protection used to resemble mapmaking. Organizations could reliably document where information lived and how it moved. Customer records sat in a database, financial information remained inside approved systems, and intellectual property stayed within defined repositories. AI is making that map obsolete. Sensitive information now flows through copilots embedded in productivity suites, agentic workflows that span applications, and autonomous systems that can act on behalf of users.

AI is often positioned as a breakthrough layer that effortlessly unlocks enterprise insights. It is exposing something more fundamental; and that is that most organizations never properly built or cleaned up their data foundations to begin with. Nearly every enterprise is experimenting with AI, but experimentation is not transformation. If the underlying data is incomplete, inconsistent or poorly understood, AI does not solve the problem. It accelerates it.

Why Data Stewardship Matters Organizations across all industries are increasingly data-driven. From operational dashboards and advanced analytics to regulatory reporting and artificial intelligence initiatives, data plays a critical role in how modern businesses function and compete. At the same time, data environments have become increasingly complex.

There’s no shortage of momentum around AI agents right now. They’re moving quickly from proof-of-concept into production, taking on real responsibilities such as resolving service issues, generating insights, and even initiating actions across enterprise systems.

For July, the Book of the Month is The Deployed Data Scientist by Ankit Anand, Scott Burk, and Kinshuk Dutta. With all the AI talk over the last couple years, it’s nice to be reminded of the real work done by data scientists deploying machine learning and achieving results for their organization. This book is technical. The team that put this together pulls no punches, but if you’ve got a coding background, you’ll be able to pickup this book and turn yourself into a data scientist.

It seems like everybody’s talking about governance these days: data governance, the principles that are addressed in the DAMA-DMBOK [1]; BI governance, including many of the issues covered in my book Growing Business Intelligence [2]; and now AI governance, an emerging topic of increasing concern to data and BI professionals. What do they do? How are they similar? How are they different? And how do we actually implement them?

Show of hands: How many of you have ever heard corporate leadership say something like, “We are moving everything into the cloud and closing down our data center so we can scale faster and save money”? Maybe you heard the slightly constrained version, “We are moving all of our analytics into the cloud and getting rid of our on-premise data warehouse so we can scale faster and save money.” How’d that work out? Let’s focus right now on data and analytics, but the same ideas apply to other areas.