Build vs. buy: R&D data infrastructure over five years
Whether to build or buy R&D data infrastructure depends less on first-release cost than on one question: who will own and maintain the scientific data model as the science changes? Build vs. Buy: R&D Data Infrastructure Over Five Years is a white paper that gives R&D and digital-transformation leaders a practical framework for comparing internal development, commercial platforms, and hybrid architectures.
The proposal is often hard to challenge: “We already have the data lake. We can build this ourselves.” The infrastructure exists, the team knows the tooling, and an initial application may look less expensive than a subscription.
But formulation and experiment data demand more than storage and a user interface. Recursive ingredient composition, method-specific properties, units, sample lineage, version history, instrument connections, and project-level access all need explicit modeling and ongoing ownership. When those requirements evolve, the work extends into historical records, integrations, analytics, and the evidence behind earlier decisions.
Should you build or buy R&D data infrastructure?
Build only when the conditions genuinely justify it, such as unusual science, mature platform engineering, strategic intellectual property, or sovereignty constraints. Outside those conditions, weigh the recurring work a build creates: instrument parsers, controlled vocabularies, access policies, audit trails, and schema migrations. Proposals that focus on the first release often leave these costs out.
A hybrid is also an option: a specialized R&D system of record, such as a connected R&D platform, linked to the enterprise lake for analytics, machine learning, and reporting.
Who this white paper is for
This white paper is for R&D leaders, digital-transformation leaders, and internal IT teams weighing a proposal to build scientific data infrastructure on an existing lake or lakehouse. It helps them compare that proposal fairly against a commercial platform. For the cost of the status quo, see our guide to what disconnected R&D systems cost.
For a one-page overview of how Uncountable approaches this, read the solution brief The Uncountable platform overview: from first experiment to finished product.
What's inside
Inside this white paper:
- The ownership question to resolve before comparing implementation costs or subscription fees
- What formulation R&D data requires, including recursive composition, sparse properties, units, methods, versioning, and traceability
- What modern lakehouse technology solves, and which scientific modeling responsibilities remain
- The maintenance commitments behind instrument parsers, controlled vocabularies, access policies, audit trails, and schema migrations
- A five-year total-cost-of-ownership structure that separates hard costs, productivity costs, and risk
- When unusual science, mature platform engineering, strategic intellectual property, or sovereignty constraints favor building
- Practical ways to reduce vendor lock-in through export rights, open formats, versioned APIs, and continuity provisions
- A weighted decision framework, eight questions for the internal IT team, and the minimum contents of a serious build proposal
Make the build-versus-buy decision on long-term ownership, scientific fit, operating capacity, and five-year cost. Read the white paper before you commit to an internal build.
FAQs
The central question in an R&D build-versus-buy decision is who will own and maintain the scientific data model after the initial implementation. That covers how formulations, ingredients, samples, methods, results, versions, and permissions are represented, and how those relationships change as research evolves. An internal build assigns that recurring obligation to the organization. A purchase can place platform maintenance with a supplier under defined contractual terms.
An enterprise data lake or lakehouse provides storage, processing, table management, and analytics, but it does not define the scientific relationships formulation and experiment workflows need. Teams still have to establish ingredient hierarchies, property definitions, method and unit vocabularies, sample lineage, entity-level versioning, comparability rules, and access boundaries. The real cost is creating and maintaining that domain layer, not the lakehouse itself.
Formulation R&D data is difficult to model because formulations can contain ingredients that are themselves formulations, creating multi-level composition. Samples may carry only a few of many possible properties, and each result needs its unit, method, conditions, instrument, and preparation context. Versioning also has scientific meaning: a recipe revision, a retest under a new method, and a corrected transcription are different events.
Internal build proposals centered on a user interface often overlook ongoing work on identity and access, audit trails, instrument connectivity, parser updates, unit and method vocabularies, sample and aliquot lineage, recovery, upgrades, and qualification evidence where applicable. They also understate the work when scientific definitions change, because a revised formulation hierarchy can affect live records, historical versions, downstream datasets, and dashboards.
Five-year total cost of ownership for R&D data infrastructure should separate hard costs, such as implementation, integrations, maintenance, infrastructure, and qualification, from productivity costs and risks, such as scientist data preparation, schema migration, key-person dependency, overruns, governance debt, and exit costs. The white paper provides this structure for teams to fill with local staffing rates, vendor terms, and realistic maintenance assumptions.
See the Platform Behind the Guide
