Tracing the line: The MDR in Practice, between the Ideal and the Inherited

The Perfect MDR: Does It Exist?

In clinical data management, a metadata repository is, at its core, a managed store of standardized data concepts: their definitions, their relationships, their provenance, and the rules that govern how they are used. Ideally, that means capturing not just what a variable is called, but what it means, where it came from, how it connects to a standard, and how it has evolved over time.

For pharma companies with a sustained clinical pipeline, the need for having an MDR is well established. CDISC has long positioned metadata and standards as the foundation for data reuse and automation. Most pharma organizations, at some point in their data management maturity, arrive at the same conclusion. The MDR becomes the assumed infrastructure.

What it looks like in practice is considerably less uniform.

What any MDR needs to do

Hosting and serving clinical standards goes without saying; but additionally, a functional MDR needs to handle a specific set of aspects reliably. Identifiers that are stable and independent of any single system. A versioning mechanism that records not just what changed but the reason behind the change. Concept provenance that captures where a definition came from and how it evolved to its current form. Governance workflows that are part of the authoring process, not added on top of it. Metadata definitions that are equally readable by people and machines. And the ability to connect internal concepts to external standards without flattening the local specificity that makes them useful in the first place.

This is already a lot to ask of any repository. Most MDR implementations cover some of these adequately. Very few cover all of them with the same rigour. The reasons for that gap are worth examining. They run deeper than tooling choices or resourcing, and they tend to reinforce each other.

The weight of legacy

No two pharma organizations arrive at their MDR the same way. The most significant factor shaping what it looks like, and what it can realistically become, is the organization's accumulated history of data decisions. One small design decision at a time, and an organization can end up with a layered, entangled ecosystem where changing anything at the root carries downstream consequences no one can fully map anymore.

Is this a failure of foresight? Not necessarily. Designing complex information systems for longevity is an increasingly challenging task in the abstract. There’s, of course, a natural decay component that comes with the long-term continuous use of any system. But what makes the case of MDR particularly challenging is that, by definition, an MDR is a heavily-referenced upstream by the clinical trials execution engines.

In response to industry initiatives and new disruptive technology trends, pharma orgs are invited to rethink their standards and metadata management practices. Lucky are the smaller pharma and biotech companies that have a clean slate to start with but for the more established larger pharma the picture looks different.

Big pharma companies have already deployed significant resources into shaping their standardization strategies, technology stacks, and data governance processes. In many cases this means an ahead-of-the-curve position; but it also means more weight to carry when pivoting in response to external pressure: technology choices made under constraints that no longer exist and processes shaped around tools that have since been replaced. The predictable outcome is an MDR that keeps growing in scope and complexity, with shock-absorbing mechanisms built in out of necessity rather than design.

The legacy problem does not stop at the platform. The metadata content itself accumulates history and needs consistent grooming to explicitly capture the intent behind the data transformation, the mappings between formats and across standard versions, and provenance information. As data evolves, maintaining that lineage properly adds a layer of overhead that is necessary but easy to defer. How that content provenance should be managed is a subject worth its own treatment, and one explored in a previous article.

The creeping scope

One of the most consequential decisions in MDR design is also the most frequently avoided: what is the MDR actually for? The given answer is that it’s where the clinical data standards live. But what about the study metadata? And what about the protocol authoring environment? Each of these is a legitimate function but combining them in one system creates problems that tend to bite back on the longer run.

Some of the newer MDR initiatives in the industry are taking that path of accumulating scope in a consolidated MDR system. The argument for consolidation is usually framed around coherence: one system, one source of truth. But in practice, I would argue it is often as much a legacy constraint as a deliberate architectural choice: the path of least resistance when existing systems are already entangled and separation feels like a heavier investment than modularization.

What consolidation actually produces is a system that simultaneously inherits the governance requirements, change cycles, and user needs of several distinct functions. This is a combination that compounds in ways that are hard to predict early and expensive to untangle later.

Modularity and separation of concerns (keeping distinct functions in distinct, well-connected components) are first design principles and should be the north star when designing systems for longevity but they are also less intuitive to sell to leadership. Building a modular eco-system is perceived as more costly and requires confidence that the interfaces between modules will hold. That perception is not entirely wrong, though. A modular ecosystem is genuinely more complex to design upfront, and the interfaces between components require ongoing investment to keep clean and coherent but over time, it is what keeps each part of the system governable and the whole system evolvable.

Navigating the standards layer

Architecture alone does not make an MDR work. Even the most artfully architected metadata repository will fall short if not backed by a data governance strategy and a clear vision for standards that stakeholders will actually relate to and adhere to.

Building and maintaining data standards is not a technical exercise. It is closer to an art. The people who do it well are navigating tensions that never fully resolve: global standard alignment against local study needs, governance against agility, stability against evolution, top-down design against bottom-up reality… Knowing which tension to lean into at a given moment requires both domain depth and a clear read of the organizational landscape.

The MDR is where those tensions land. Standards arrive carrying the compromises of the communities that produced them, into organizations that already have their own layers of conventions, extensions, and legacy interpretations. Keeping those layers coherent as both sides evolve, and maintaining visibility of where and why deviations exist, is ongoing commitment.

Without that governance commitment, the MDR becomes a passive store and the burden of technically maintaining it can even outweigh the strategic benefits.

And as if navigating that layer was not already demanding enough, the very ground on which it stands is being shaken by growing parts of the industry questioning whether sustained investment in standardization is still worth the effort. The argument is that AI models can compensate for what structured metadata would otherwise provide. That position is convenient for organizations that have already committed heavily to AI adoption and less convenient to challenge openly. The result is that the people responsible for driving standards initiatives are navigating the usual organizational tensions while also defending the premise of the work itself. That is a harder room to work in, and it has a direct bearing on what MDR initiatives can realistically achieve and at what pace.

The underlying model representation

​​Every MDR rests on a representational model, a choice about how concepts and their relationships are stored and processed. Given the high interconnectivity of clinical data concepts, and the fact that those connections need to span operational silos, a graph representation is generally more fitting than a relational one. But even within that consensus, there are two main approaches that are not interchangeable in terms of what they make possible: RDF knowledge graphs and property graphs. This may look like a narrow technical decision at the architecture stage but it turns out to carry significant long-term implications.

RDF knowledge graphs are built on a set of open W3C standards, including RDF, OWL, SHACL and SPARQL, that were designed specifically for representing and exchanging meaning across systems. In an RDF model, every concept and every relationship carries a globally resolvable identifier, which makes the graph inherently interoperable beyond the boundaries of any single system or organization. More importantly, OWL and SHACL enable writing ontologies with formal semantics: relationships between concepts carry logical constraints that an inference engine can use to infer new knowledge, validate consistency, and detect contradictions. For an MDR where provenance, concept lineage, and semantic alignment with external standards like CDISC are core requirements, those nuances of formal semantics and global identifier management are key.

Property graphs are flexible, handle complex relationships well, and are familiar to most data engineering teams. Combined with the right traversal algorithms, they are capable of deriving non-trivial insights from connected data. Where they are less suited is sharing meaning across system boundaries. Without a formal semantic layer, the meaning of a relationship is implicit, embedded in how the graph was built rather than in the model itself. That makes property graphs more vulnerable to inconsistencies and silent semantic drift: two systems can use the same structure to mean different things, with no mechanism to detect or resolve the divergence. There is also a practical consideration worth noting: the property graph implementations underpinning some of the newer MDR initiatives in the industry rely on a proprietary technology. For organizations considering adoption, that dependency sits awkwardly behind an open-source label.

In practice, the representational model often gets chosen for the wrong reasons: team familiarity, existing infrastructure, or the technology stack an industry initiative happened to be built on. The architecture ends up reflecting the legacy more than the defendable requirements. That is worth being deliberate about, because unlike other MDR design decisions, this one is particularly hard to revisit later.

Where this leaves us

The MDR question sits at the intersection of technology choices, organizational history, standards governance, and an industry debate about where metadata investment belongs in an AI-driven future.

None of those dimensions are settled. Most organizations are somewhere in the middle of figuring this out. They have made some good early choices and are living with some less good ones. How these unsettled dimensions shake out, for standards, for MDR investment, and for the industry's ability to make good on the AI promise in regulated environments, is still being written.