Digital Future & Strategy

Two records can look different and still describe the same real-world entity. “International Business Machines Corporation” and “IBM” are an obvious example to a person. Enterprise data is rarely that easy. A supplier may appear under a legal name in one ERP, a trading name in a procurement platform, an abbreviated name in a regional system and a former name in historical transactions. Addresses may differ. Phone numbers may have changed. Registration identifiers may be missing. Corporate ownership may have changed after an acquisition. The opposite problem also occurs. Two companies can have very similar names while being completely different legal entities. This is why entity resolution is not simply a search for similar strings. The real question is not “Do these records look alike?” It is “Does the available evidence support the conclusion that these records represent the same real-world entity?” Traditional exact and fuzzy matching remain useful parts of that ...

“Self-healing master data” sounds as if an AI system can detect a bad record, understand what went wrong, repair it and continue operating without human involvement. Some parts of that vision are already technically possible. Others are not. A system can automatically reject an invalid country code. It can standardize an address using trusted reference data. It can derive a value from an approved rule. It can identify anomalous records, recommend data-quality rules and surface suspected duplicates. But should it automatically merge two major suppliers because an AI model believes they are the same company? Should it change a product classification when two business units disagree about the correct taxonomy? Should an AI system decide which customer record becomes authoritative when the evidence conflicts? Those are different types of decisions. Self-healing master data should not mean “AI fixes everything automatically.” A more useful definition is a controlled...

The Data Steward has traditionally been the human control point inside enterprise MDM. When duplicate records appear, a steward reviews them. When a change request violates a rule, a steward investigates it. When two business units disagree about an attribute, somebody eventually needs to understand the context and decide what happens next. AI is beginning to change this operating model. Not because every stewardship decision can suddenly be automated, but because much of the work surrounding the decision can now be detected, assembled, summarized, recommended and routed by software. The important question is no longer “Can AI replace the Data Steward?” It is “Which parts of stewardship should an agent perform, and where must human accountability remain?” I use the term Data Steward Agent for an AI-enabled operating role that assists with or executes selected stewardship activities inside defined policies, permissions and escalation rules. It should not be interpret...

An enterprise AI system can produce a technically sophisticated answer and still misunderstand the business entity behind the question. A procurement agent may know how to analyze supplier risk but fail to recognize that three supplier IDs belong to the same legal organization. A sales assistant may summarize customer activity but miss half of the relationship because local accounts are not connected to the global parent. A product-support agent may retrieve detailed specifications but mix an obsolete product with its current replacement. These are not necessarily model failures. They are often failures of identity, context, semantics or governance in the data surrounding the model. AI-Ready Data is not simply “clean data.” It is data whose quality, meaning, identity, relationships, freshness, access and governance are sufficient for a specific AI use case to operate reliably. This distinction is important. There is no universal quality score that makes an enterprise...