Connecting the Dots
Using Master Data as the Truth Layer for Enterprise GenAI
Even Anthropic hit the brick wall. Here’s how to get past it.
Every data leader wants a GenAI solution the business trusts without a human checking every answer. The technology is ready. The trust isn’t.
Anthropic learned this firsthand. Automating their internal analytics with AI agents, they hit a wall of confident, articulate, wrong answers, all traced back to data that was never resolved to a single trusted version. What they built to fix it looks a lot like master data management, even though they never called it that.
In this report, Malcolm Hawker (Chief Data Officer at Profisee and former Gartner analyst) shows why trust in enterprise GenAI comes down to meaning and truth. A semantic layer can tell you what an “active customer” is and still be wrong about how many you have. Master data supplies the other half, and this report tells you which use cases actually need it before you spend a dollar.
Download the report to get:
- A simple test for when a use case truly depends on master data, and when it doesn’t
- The three-stage grounding model (Crawl, Walk, Run) for matching data trust to the risk a solution carries
- How master data and semantic layers divide the labor of meaning, and why one without the other produces confident, plausible, wrong answers
Frequently Asked Questions
Does generative AI need master data management?
Not always. It depends on the use case. Generative AI needs master data management when a correct answer relies on shared facts about business entities like customers, suppliers, products, or accounts that must be trusted across multiple systems. If the task is self-contained, such as summarizing a single document, it likely doesn’t need MDM at all.
How do I know if a GenAI use case needs master data?
Use a simple test: does producing a correct answer or action depend on facts about entities like customers, suppliers, and accounts that have to be shared and trusted across more than one system or process? If yes, the use case depends on master data. If it’s self-contained, like summarizing a single uploaded document, it likely doesn’t need MDM.
Why does generative AI give confident but wrong answers?
Generative AI produces confident, plausible, wrong answers when it has fluent language but no grounding in trusted data. A large language model can assemble a convincing statement about a customer or account with no guarantee the underlying facts are correct or current. Master data supplies the trusted facts and a semantic layer supplies their meaning. Without both, accuracy is left to chance.
