What is Data Management? Continuing the Discussion

🔍 What Is Data Management? Continuing the Conversation
📅 Join us for this month’s webinar series

Last month’s webinar on the Challenges Facing the DMBOK and the existential question of “What is Data Management?” sparked far more engagement than I anticipated. I’m grateful to everyone who reached out with thoughtful perspectives—your contributions are helping shape a deeper, more nuanced conversation.

It’s a tough topic. Our experiences shape how we interpret the DMBOK, and often, when something “hasn’t worked,” it’s because the value of certain Knowledge Areas isn’t clearly understood or communicated.

Some recurring questions from the community include:
🔄 Where do we start on the DAMA Wheel?

• 1 o’clock to 12?
• Data Architecture to Data Quality?
• Does Data Quality precede Data Governance?

🎯 Why is Data Governance at the center?
🔗 How do Knowledge Areas like Data Quality relate to others?
And then there’s the evolution of the DMBOK itself—new chapters like Big Data & Data Science, DMMA, and Organizational Change Management. Valuable? Absolutely. But are they core to Data Management, or do they belong to adjacent disciplines?
To move forward, we need guardrails—a shared framework to help us define what is and isn’t a Data Management discipline. Some ideas that have emerged:

1. What types of data are we managing?

Structured, Semi-Structured, Unstructured, Tacit Knowledge?
2. What is the data lifecycle?

Plan → Dispose? Or Create → Store → Dispose?
3. Which parts of the DIKW Pyramid are in scope?

All? Just Raw Data? Something else?

🎨 Call for Contributions
As part of this ongoing dialogue, I’m inviting submissions and visualizations of a revised DAMA Wheel that reflects your perspective on these questions. Please include the guardrails you used to define your version—what makes a Knowledge Area part of Data Management in your view?

Let’s continue the conversation and work toward a more unified understanding of our field.

💬 In the meantime, I’d love to hear your thoughts:
What do you think defines a core Data Management discipline?