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Build a unified data foundation by making research data findable, consistently described, interoperable, traceable, and usable under appropriate governance—not by forcing every dataset into one database. Start with the scientific questions AI needs to answer, then choose metadata, standards, integration patterns, and access controls that fit those questions and the data behind them.
No single architecture is mandated by the primary sources covered here. The right design is a set of explicit choices about how data is described and connected while its origin, limitations, and permissions remain visible.
What does a unified data foundation mean?
A unified foundation gives researchers and analytical systems a dependable way to discover and work across relevant data, even when that data remains in different systems or under different owners. “Unified” describes how data can be understood and used together; it does not require every source to be copied into one central store.
For drug discovery AI, the foundation should help answer practical questions: What data exists? How was it generated? Which entities or measurements does it describe? What transformations has it undergone? Can it be combined with other sources for a defined analysis, and is that use permitted?
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These capabilities are related but distinct. A catalog can make a dataset discoverable without making its contents openly accessible. Interoperability can make two datasets easier to combine without making them scientifically equivalent. Governance determines who may use data, for what purposes, and under which conditions.
How should an organization begin?
Begin with the research questions and constraints, not a preferred platform or data model. The initial design work is to decide what needs to be connected and what “usable together” means for each intended use.
- Choose priority questions. Identify the decisions or analyses the foundation must support. Make each question specific enough to reveal the data required and the expected output.
- Map relevant sources and owners. For each question, record which systems or datasets may contribute, who is responsible for them, how they are generated, and which versions or time ranges matter.
- Record constraints before integration. Capture known quality limitations, permitted uses, access restrictions, and any obligations that affect sharing or processing. The controls needed depend on the organization’s data and obligations.
- Define success in scientific and operational terms. Decide how users will find the needed data, what level of consistency is necessary, how fresh integrated data must be, and how users will verify source and transformation history.
- Prioritize a bounded use case. Start with a question whose data sources, permissions, and expected outputs can be understood. Use it to test whether the design works before expanding to other questions and data classes.
What capabilities should the foundation provide?
A shared way to find and assess data
A catalog or portal should let users search across relevant sources and assess whether a dataset is suitable before they invest in an analysis. Useful metadata can describe the source, responsible owner, subject or entity coverage, collection or generation method, format, available time span, known limitations, and access conditions. The exact fields should reflect the data and intended questions rather than a universal checklist.
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Discovery is not the same as access. A catalog may show that a dataset exists and explain how to request it while the underlying data remains restricted. That distinction lets organizations improve visibility without implying that every dataset is public or automatically available to every researcher.
The NIH Common Fund Data Ecosystem (CFDE) is a public example of a portal intended to support FAIR-oriented discovery across dispersed Common Fund datasets. NIH describes the ecosystem as integrating data, resources, and knowledge across programs. It illustrates the value of a cross-dataset discovery point; it is not a prescribed blueprint for a pharmaceutical organization.
Consistent definitions and identifiers where they matter
Shared definitions, formats, identifiers, and exchange rules make data more predictable for systems and scientific tools. Apply them where they support a defined workflow; do not assume one standard fits every data type or that every regulatory standard applies to every research dataset.
Medicinal-product identity and related regulatory information are one area where the IDMP standards family is relevant. ISO/TS 21405:2026 describes an ontology framework intended to support semantic interoperability for medicinal-product identification using IDMP standards and FAIR principles. It explains a framework for representing concepts and relationships, but does not mandate a particular ontology implementation tool.
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Connections that retain context
Data can be connected through shared models, mappings between source schemas, APIs, federated access, or graph representations. Whichever approach is chosen, a connection should not erase where a value came from or what it means in its original context.
Knowledge graphs are one possible approach when questions depend on relationships among entities across sources. In a September 25, 2026 announcement, the U.S. National Science Foundation (NSF) described its Open Knowledge Network as connecting independent knowledge graphs through a shared technical fabric and supporting cross-graph questions. This shows that federation is a working infrastructure pattern; it does not establish that every drug-discovery environment needs a knowledge graph.
Traceability and governed use
For an AI workflow, users should be able to determine the source and context of inputs, the transformations applied, the data version used, and the permitted use. These records help teams interpret results, reproduce an analysis where feasible, and investigate issues with inputs or processing.
NSF describes the Open Knowledge Network as structured, persistent, verifiable, attributable, and governed. Those attributes support traceability as a design principle. They do not specify a complete access-control, privacy, consent, or audit scheme for a pharmaceutical organization; those controls need to be designed around its own data and obligations.
How do the main architecture choices differ?
There is no best-in-class stack established by the cited sources. Compare architecture options against the actual questions, data classes, ownership boundaries, access constraints, latency needs, and governance model.
| Decision | Option A | Option B | What to weigh |
|---|---|---|---|
| Where data is accessed | Centralized storage: data is copied or consolidated under more direct operational control. | Federated access: data remains distributed and is accessed across systems. | Centralization can simplify some operations but increases duplication and requires decisions about copies and updates. Federation can respect distributed ownership but makes cross-system queries and consistent operations more complex. |
| How data is described | Shared schema: sources are represented in a common structure. | Schema mappings: source structures are retained and translated for specific uses. | A shared schema can support consistency for common analyses; mappings can accommodate heterogeneous sources but add translation and maintenance work. |
| How entities and relationships are represented | Relational or tabular models: structured records and fields. | Knowledge graphs: entities and their relationships are represented explicitly. | Tabular structures suit many straightforward processing tasks. Graphs may help with questions that traverse relationships across sources, but they are not automatically necessary or preferable. |
| How updates move | Batch pipelines: data is ingested or refreshed on a schedule. | Event- or API-based integration: changes can be exchanged as they occur or requested through interfaces. | Batching can make repeatable ingestion simpler; more immediate integration can improve freshness but adds operational complexity. |
| Who operates the infrastructure | Open or shared infrastructure: the organization has more direct control over the components it adopts. | Commercial managed services: a provider operates some infrastructure or service components. | Assess operational capacity, control, portability, and dependence on a provider. The appropriate balance depends on the organization; the sources do not establish a preferred vendor or service. |
What do public examples show—and what do they not show?
Public initiatives demonstrate approaches to cross-dataset discovery and connection, but their scale and goals should not be treated as a forecast for a drug-discovery project.
- FDA data standards: The U.S. Food and Drug Administration (FDA) defines data standards as rules for structuring, defining, formatting, or exchanging data between systems. It says standard, uniform study data lets FDA scientists explore questions by combining data from multiple studies. FDA’s current CDER Data Standards Program page, accessed October 7, 2026, reports more than 300,000 submissions each year, amounting to millions of pieces of data. That is a description of FDA’s workload, not a measure of a pharmaceutical discovery dataset.
- NSF Open Knowledge Network: NSF’s September 25, 2026 announcement reports 43 interconnected knowledge graphs and tens of billions of connected facts, participation from more than 12 federal agencies and over 90 cross-sector partnerships, and an initial prototype effort of $26.7 million involving 18 research teams. The announcement describes the program launched in 2023. These figures describe that public infrastructure and program, not the expected scale, cost, or result of a drug-discovery implementation.
NSF Assistant Director for Technology, Innovation and Partnerships Erwin Gianchandani described the network as “public infrastructure that agencies, researchers, and the public can use to answer questions that cross the boundaries between fields.” Its relevance here is as an example of connected, federated knowledge—not as a ready-made pharmaceutical architecture.
How should regulatory standards fit into the design?
Treat regulatory submission readiness as a bounded use case within the broader data foundation. FDA’s CDER Data Standards Program covers defined standards and requirements for regulatory submissions, including study data and product information. FDA also notes that some standards are required while others are not, and directs users to its catalog for supported and required standards and future timelines. Check the applicable requirements for the submission and data in question rather than applying every FDA standard indiscriminately to discovery data.
FDA’s final guidance, Data Standards for Drug and Biological Product Submissions Containing Real-World Data, issued in December 2023, is specifically scoped to standards for submissions containing real-world data. It is useful for understanding a regulatory interoperability need; it is not a general architecture guide for all preclinical, assay, imaging, omics, or literature data.
Separating these scopes helps teams avoid two costly mistakes: treating submission standards as a universal research data model, or designing research data flows without accounting for applicable submission requirements when a regulatory use is intended.
Quick Recap
What is a practical way to roll out the foundation?
- Write the use-case specification. For the first priority question, list the needed sources, responsible owners, access conditions, expected outputs, and known data limitations.
- Define a minimum metadata and provenance record. Agree what users need to know to find, assess, interpret, and trace each contributed dataset. Keep the record extensible rather than assuming all sources can provide identical detail.
- Select standards and identifiers selectively. Map existing definitions and formats, identify where inconsistency blocks the use case, and adopt shared rules for those points. Confirm regulatory requirements separately when submission use is in scope.
- Choose a connection pattern for the constraints. Decide whether the use case needs copied and consolidated data, federated queries, schema mappings, a shared model, or graph relationships. Document why the choice fits its latency, ownership, operational, and governance needs.
- Test with real users and representative data. Check that people can locate the right inputs, understand their limitations, gain access through the intended process, and trace transformations into outputs. Include failure cases such as missing metadata, incompatible identifiers, stale copies, or denied permissions.
- Expand by demonstrated need. Add more sources and use cases only when the foundation can preserve definitions, context, provenance, and appropriate access as it grows. Revisit the architecture when new questions or constraints change the tradeoffs.
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