Enterprises keep discovering that model prowess is not the bottleneck; master data management is. Treat master data management as the operating system for AI and the path to scale opens up; treat it as housekeeping and the programme drifts from pilot to post-mortem.
Executive teams have moved past novelty and into accountability. The questions now are practical: can the outputs be trusted, can they be explained, and can this capability be run at a cost and risk profile the board will accept? Those questions do not begin with model choice; they start with the state of the data estate and the discipline wrapped around it.
The pattern behind many stalled initiatives is consistent. Organisations assemble large training sets from wherever data is easiest to access, stitch them into pipelines, and then wonder why accuracy wobbles, audits stall, and privacy teams raise alarms. The issue is not a lack of enthusiasm or intent; it is the absence of a durable foundation.
“Enterprises are coming out of the science-experiment phase and recognising that risk mitigation, regulatory pressure, data privacy and basic explainability are inseparable from value,” Craig Gravina, Chief Technology Officer at Semarchy, explains. “That recognition puts master data management back at the centre of the conversation. The early push to show quick results often bypassed the need for governed, consolidated data and clear lineage. Now that teams need to prove where training sets came from and how models were influenced, they rediscover capabilities MDM has delivered for years.”
The shift in tone matters because it reframes why programmes falter. Failure rarely reflects a model’s limits. Failure occurs when inputs are inconsistent, identities are duplicated, provenance is unclear, and no one can trace how a dataset was processed through a pipeline into a weight update. Those are not exotic problems. They are the predictable result of treating data readiness as optional. Once leaders accept that AI is an operating discipline rather than an app, the first investment becomes obvious.
What master data management delivers
Confusion about terms does not help. Many executives hear the term’ data management’ and picture a data lake or warehouse. Master data management is a different class of capability. At its core, it resolves entities, reconciles duplicates, and assembles a golden record for the subjects that matter most to the enterprise: customers, suppliers, products, locations, assets, and employees. That golden record is not a slogan; it is the referential truth that synchronises operational systems and supplies analytics and AI with consistent meaning.
“Master data management pulls together source data from disparate systems, applies matching and survivorship rules, and produces a governed set of golden records that downstream consumers can rely on,” Gravina continues. “The traditional outlet was analytics. The scope is now much wider. We see MDM as the engine that also exposes ‘data products’, curated datasets, governed APIs, and workflow applications packaged for specific uses. Those products become first-class artefacts for developers and data scientists, and they are the vehicles through which the organisation experiences the value.”
Treating master data as a product changes behaviours in valuable ways. Product management disciplines, stakeholder discovery, outcome definition, release planning, and service levels replace vague requests for more data. Ownership shifts from centralised gatekeepers to domain teams that understand the semantics of their entities and the realities of their processes. The centre still matters because common standards and shared platforms prevent fragmentation, but the work moves closer to where knowledge lives. The result is faster iteration without sacrificing coherence.
The compounding effect of architectural choices becomes clear when considering where data platforms reside, on-premises, within a cloud data warehouse, or distributed across regions, because those decisions set the centre of gravity for cost and latency. Placing mastered entities and governed products in the same environment as analytical and AI workloads reduces egress, improves performance, and simplifies privacy obligations. The aim is not centralisation for its own sake; it is proximity between trusted data and the systems that learn from it.
Quality beats quantity in AI pipelines
Volume alone does not rescue a weak corpus. Enterprises are reminded of this whenever a model reflects inconsistent customer identities, misclassified products, or outdated attributes that have survived unnoticed in a long tail of systems. The physics are familiar. More data increases the likelihood that a model will produce a proper interpolation; bad data raises the possibility that it will produce an incorrect interpolation with great confidence. When those mistakes create customer harm or regulatory concerns, confidence evaporates quickly.
“Garbage in, garbage out is not new, but AI makes the garbage harder to spot because outputs sound convincing,” Gravina adds. “The teams that win are the ones that understand the characteristics and sources of their data and can answer the specific questions their models need to learn. That means knowing where the training sets originated, how entities were mastered, what their lineage is, and how quality was assessed. Without that, you do not get better inference, you just get faster mistakes.”
Quality is not a single number; it is a bundle of attributes that depend on the use case. Completeness is vital when building a 360-degree customer view; timeliness dominates in fraud detection; precision of classification controls the usefulness of retrieval for large language models. Master data management provides the scaffolding for those attributes: entity resolution, survivorship policy, standardisation, enrichment, and stewardship workflows for exception handling. When those fundamentals improve, every downstream model benefits without additional heroics.
There is also the matter of fit for purpose. A dataset that is adequate for an internal prototype may be unacceptable for a production system that faces customers or regulators. MDM platforms that badge sensitivity, attach usage constraints, and surface lineage enable program leads to determine whether data is suitable for an experiment, suitable for internal automation, or suitable for external services. That clarity avoids the all-too-common pattern where a prototype succeeds technically and then stalls for months in legal review because provenance cannot be proven.
Governance as an accelerator, not a brake
Governance suffers from an image problem. Too often, it is equated with committees and delays. In practice, good governance increases speed by reducing ambiguity and preventing rework. Clear labels, consistent taxonomies, role-based access, and policy-driven controls turn discovery from a hunt into a catalogue. When a data scientist can find a badged, fit-for-use dataset with lineage and usage constraints attached, experimentation accelerates because arguments about provenance and permission are settled up front.
“Proper governance means better accessibility,” Gravina explains. “When a data product carries the tags, classifications, and lineage that consumers need, self-service and self-discovery can happen. Teams spend less time debating definitions and more time building. The same controls that satisfy audit and privacy teams are the controls that let you scale safely.”
Explainability reinforces that point. Being able to trace a model’s inputs and training lineage is no longer optional in regulated sectors. It is also a pragmatic necessity when a model underperforms. Without lineage, diagnosis degenerates into guesswork. With lineage, teams can reason about whether an error originated in a source system, a transformation, a feature store, or a specific window of training data. That precision saves time and money precisely because governance was treated as design, not decoration.
Responsible decentralisation ties these strands together. Centralised programmes fail when they demand omniscience from a single team. Decentralised programmes fail when they fragment definitions and duplicate effort. The modern pattern avoids both extremes by pushing responsibility for data products to domain teams while enforcing shared standards for identity, metadata, lineage, and security. Those standards are not bureaucracy; they are the interfaces that let products interoperate.
“Decentralisation is powerful when the domains that understand their data are empowered to build, and when the platform gives them the guardrails to do it responsibly,” Gravina continues. “Data products are the way to package that value. A customer 360 is not a spreadsheet; it is a mastered entity view with APIs, workflows, and well-defined service levels. One of those service levels might be ‘AI-ready,’ which signals that the product meets quality, privacy, and lineage thresholds for model training.”
Privacy finally sits as the non-negotiable layer. Personally identifiable information often lies hidden in unstructured content, and sector-specific rules add complexity for financial services and healthcare. Data products that badge sensitivity and constrain access by default make compliance less a negotiation and more an attribute of the platform. That posture changes the conversation with legal, because risk controls are engineered into the path to production rather than stapled on at the end.
The reality of shadow AI then becomes manageable rather than existential. Departments will experiment with tools outside central oversight because the friction associated with trying something is low. The productive response is to make the safe path the easy path by ensuring that high-quality, mastered, privacy-aware data products are available to those teams and that usage is observable.
“The pace of AI adoption means individual domains will absorb capabilities independently of IT,” Gravina concludes. “The key is not to chase every tool. The key is to be confident that the data those teams use is consistent and governed. When that foundation exists, an enterprise can be more flexible about how departments apply AI without creating unmanageable risk.”
The practical implications for boards and programme leaders are straightforward, even if the work is not. Treat master data management as essential rather than optional. Identify the entities that drive value and pain, then build golden records for those domains and publish them as products with clear interfaces and service levels. Wrap them in governance that is lightweight for most users and rigorous for those who need sensitive access. Instrument the pipeline so that quality, cost, and usage are observable. Sequence pilots to prove that cycle time from experiment to service shrinks when teams start from mastered entities rather than ad hoc extracts. The result is not simply better models; it is a faster organisation.
A split will emerge as programmes mature. In one camp, teams continue to assemble ad hoc datasets, struggle to explain outcomes, and face growing pushback from risk and compliance. In the other, teams build on mastered entities and governed products, which enables them to prototype quickly, deploy with confidence, and scale without surprises. The gap will initially be one of talent and tooling. It will appear as cost and time shortly thereafter. Eventually, it will present itself as trustworthy because customers and regulators will learn which organisations can effectively explain themselves.
There is no AI story without a data story. Master data management may not be glamorous, but it is the difference between an experiment and an enterprise. Treat it as the foundation, and the programme has a chance to move from slide to system without a trail of caveats. Treat it as an afterthought, and the organisation will rediscover, expensively, why quality always beats quantity.
