Published on September 4, 2025
On Data Management
As a Data Professional with over two decades in the trenches of enterprise data, I can tell you that the current excitement around AI, while invigorating, is putting immense pressure on the very foundations of our work.
For years, we've been the stewards, the guardians, advocating for the unglamorous but absolutely critical disciplines outlined in the DAMA-DMBOK framework.
Frankly, you can't build a house on a sandy ground. And right now, too many organizations are trying to deploy advanced Generative AI models on "sandy" data. They're chasing the magic without doing the math.
My focus isn't on the flashy algorithm of the week. It's on:
- Data Governance: Who owns the data? Who has the right to access and modify it? How do we ensure compliance with GDPR, CCPA, and the next wave of regulations? An AI is only as ethical as the data governance that surrounds it.
- Data Quality: An LLM fed with inaccurate, incomplete, or duplicative data will only hallucinate with more confidence. We implement the frameworks—the data profiling, cleansing, and monitoring—that ensure the fuel for these AI engines is high-octane, not sludge.
- Master & Metadata Management: Without a 'golden record' for your customers or products, your AI will create chaos. Without a robust metadata catalog, you have no data lineage, no impact analysis, and no trust. You're flying blind.
So, while the world is talking about 'prompt engineering,' I'm ensuring the data ecosystem is secure, trustworthy, and audit-ready. Because when a multi-billion dollar decision is made based on an AI's recommendation, the board won't be asking about the model's architecture; they'll be asking us if the data it was trained on was accurate. And my job is to ensure the answer is an unequivocal 'yes'."