DATAVERSITY

Enterprises are implementing robust AI governance frameworks that build on existing data governance principles and meet the unique governance needs of agentic AI. AI governance begins with a complete AI inventory and thorough risk assessment to place all AI systems in the appropriate governance tier and to establish human ownership and approval paths.

For years, data governance had an image problem. Inside many organizations, it was associated with policies, approval committees, regulatory checklists, and long conversations about who owned which database column. Important work, certainly, but rarely the part of a technology strategy that attracted the most attention. Artificial intelligence is changing that.

I recently received two “thanks for attending” emails from two different events I registered for, but was not able to actually attend. Clearly, these are auto-generated emails sent to the entire registration list. These emails got me thinking about this question… What are the ethics of creating automated data? Automated thanks? No thanks. Why do we send thank-you notes?

The Real Purpose of Data Governance Organizations make thousands of decisions every day. Some keep operations running. Others allocate resources, set priorities, manage risk, demonstrate compliance, or commit the organization to a long-term course. All of them depend on data. Data becomes information. Information becomes knowledge. Knowledge informs judgment and decision-making. This chain shapes performance, resilience, risk management, and regulatory accountability.

I have attended the imaginary funerals for practically everything we do. Technologies change … Terminology changes … Acronyms multiply … Consultants (like moi!) discover new words for old ideas, and suddenly something we have relied on for decades is declared dead. Give it six months, and somebody will bring it back with a new name, a new acronym, and probably a higher price tag. It’s all in the data.

The Good AI is a TDAN column published every quarter on DATAVERSITY. AI is changing the economics of data delivery. Organizations should use that change to build stronger data foundations while demonstrating value much sooner. The Cycle We Keep Repeating For years, organizations have described data as a strategic asset, yet many still struggle to sustain investment in the foundation required to make it trustworthy and useful. The challenge is timing.

Overview For October, the Book of the Month is Enabling Data: A New Model for Data-Driven Success in Your Organization by David Castro-Gavino and Boyan Angelov. A lot of books we’ve featured recently focus on AI, so it’s refreshing to pick up a book that asks why so many data and AI efforts stall before they ever deliver value. Let’s dive right in. The book opens with a foreword from Tom Redman that sets a deliberately high bar. The authors take that challenge head on.

Enterprises are integrating AI into core workflows, introducing agents and looking for ways to scale the technology across the business. However, adoption is moving quickly, and regulation remains unsettled, creating a growing risk that organizations will move faster than they can understand, control, and govern what AI is doing. Regulation is an important part of that equation, and governments are still establishing rules around privacy, data protection, and responsible AI use.

Mind the Gap is a DATAVERSITY column published on the first Thursday of every month. An executive is in a meeting with the CEO. The executive shares a report. The CEO asks, “Do you trust this report?” The executive replies, “Yes.” To which the CEO responds, “Why?” How often is that question asked, and when it’s asked, how often is it answered confidently or honestly? Think about the hardest question your boss ever asked you.

Enterprises are deploying artificial intelligence (AI) agents faster than they are building the systems those agents depend on. Business leadership now expects visible AI capability within one or two quarters, a fraction of the 18 to 24 months a core enterprise resource planning (ERP) rollout once took. That compression stems from competitive pressure, despite potential shortcomings in technical readiness.