Selecting a Real-World Evidence Data Partner

How to evaluate real-world data and RWE vendors: data types, fitness-for-purpose questions, pricing models, and what recent consolidation means for buyers.

Published July 21, 2026

Real-World Evidence & Data

Selecting a real-world evidence (RWE) data partner comes down to three questions: does the vendor’s data actually capture your patient population and outcomes of interest, can the vendor demonstrate the provenance and quality of that data to the standard your use case requires, and does the engagement model — data license, analytics platform, or full-service study — match the capabilities of your internal team? Everything else in the evaluation, from pricing to publication support, flows from those three answers. Because no single dataset covers every therapeutic area, care setting, and outcome, most life-science buyers end up combining sources, which makes data linkage capabilities and vendor interoperability a core selection criterion rather than an afterthought.

Why the selection matters more than it used to

Real-world data has moved from a nice-to-have for publications into regulatory and commercial workflows. In July 2024, the FDA finalized its guidance on assessing electronic health records and medical claims data to support regulatory decision-making for drugs and biologics — part of the agency’s broader RWE program under the 21st Century Cures Act. That guidance puts explicit weight on data provenance, fitness-for-purpose assessment, and documentation of how a dataset was curated. If your RWE program may ever touch a regulatory submission, your data partner needs to be able to answer those questions in writing.

The commercial stakes are growing too. Grand View Research valued the global RWE solutions market at roughly $3.0 billion in 2025 and projects it to reach about $6.0 billion by 2033, a 9.1% compound annual growth rate, with services making up the majority of spend. A growing market means more vendors, more overlapping claims, and more work for buyers to separate genuine differentiation from repackaged data.

Know the segments before you shortlist

RWE vendors cluster into a few functional segments. Most established companies span more than one, but each has a center of gravity, and matching that center of gravity to your use case is the fastest way to build a sensible shortlist.

Segment	What they primarily sell	Representative vendors (alphabetical)
Broad claims and closed-payer data	Large administrative claims assets for epidemiology, treatment patterns, HEOR	HealthVerity, IQVIA, Komodo Health, Merative, Optum Life Sciences
EHR and specialty clinical data	Deep clinical detail, labs, notes, oncology and specialty registries	Flatiron Health, OM1, Ontada, Tempus AI, TriNetX, Truveta, Verana Health
Data linkage and tokenization	Privacy-preserving record linkage across datasets	Datavant, HealthVerity
Analytics platforms and study services	Software and methodology for regulatory-grade studies	Aetion, Atropos Health, ConcertAI, Cytel, Panalgo

Claims data gives you breadth — large patient counts, longitudinal coverage of encounters, prescriptions, and costs — but limited clinical depth. EHR-derived data gives you labs, staging, biomarkers, and physician documentation, but coverage is uneven across health systems and specialties. Registries and curated disease-specific cohorts sit in between: smaller, but with outcome variables that neither raw claims nor raw EHR data reliably capture. Most rigorous studies end up combining at least two of these, which is why tokenization and linkage infrastructure has become strategically central.

The fitness-for-purpose checklist

A structured evaluation should force each candidate vendor to answer the same set of questions. The categories below mirror what regulators and journal reviewers increasingly expect.

Data provenance and coverage

Where does the data originate (payers, providers, labs, pharmacies), and what contractual rights govern its use?
What share of your target patient population is plausibly captured, and how is that estimated?
How current is the data, and what is the refresh cadence and lag?
How are duplicate patients handled when sources overlap?

Quality and curation

What curation is applied between raw source data and the delivered asset, and is it documented well enough to describe in a study protocol?
How are key variables (diagnosis, line of therapy, mortality, disease severity) derived, and have those derivations been validated or published?
Can the vendor produce a data dictionary and known-limitations document before you sign?

Privacy and compliance

What de-identification methodology is used, and when was it last certified?
How does tokenization work if you later want to link the asset to other sources?
Who bears responsibility if a re-identification issue arises?

Engagement model and pricing

Is the offer a raw data license, a query platform subscription, or a full-service study? Prices and internal staffing needs differ by an order of magnitude across these models.
Are publication rights and derived-data rights included, or priced separately?
What happens to your access and any derived cohorts when the contract ends?

Run a paid pilot or feasibility count before committing to a multi-year license. A feasibility query that returns patient counts for your exact inclusion criteria tells you more than any sales deck, and reputable vendors treat feasibility work as a normal part of the sales cycle.

Consolidation is reshaping the shortlist

The vendor landscape a buyer evaluates today is not the one from two years ago, and it will keep moving during a typical contract term. Two verified 2025 examples illustrate the pattern. In May 2025, Datavant — the largest health-data tokenization network — announced its acquisition of Aetion, a regulatory-grade RWE analytics platform, and has since completed the deal, combining linkage infrastructure with study-execution software under one owner. And in February 2025, Tempus AI completed its $600 million acquisition of Ambry Genetics, extending a clinical-and-molecular data business further into hereditary testing.

For buyers, consolidation cuts both ways. Integrated stacks can reduce the number of contracts and data-transfer headaches; they can also reduce negotiating leverage and create pressure to buy bundled services you would otherwise source competitively. Two practical protections: negotiate change-of-control language that preserves your pricing and data rights if your vendor is acquired, and avoid designing your evidence strategy so that it only works with one vendor’s proprietary tokens or platform.

Matching vendor type to use case

There is no best RWE vendor — there is a best fit for a specific question. A few common pairings:

HEOR and payer evidence dossiers lean on large claims assets, where cost and utilization variables are strongest.
Regulatory-facing external control arms and label expansions need deeply curated clinical data with documented provenance, plus methodology support that can survive FDA scrutiny under the 2024 guidance.
Oncology evidence programs usually require specialty EHR-derived data, because staging, biomarkers, and progression are invisible in claims.
Rare disease programs often need linkage across several small sources, making tokenization capability the deciding factor.
Rapid internal analytics (treatment landscape, line-of-therapy shifts) favor query platforms your own analysts can operate over full-service studies.

Practical recommendation

Treat RWE partner selection as a two-stage process. First, define the two or three evidence questions that matter most over the next 24 months and write them down as draft study questions with inclusion criteria — not as a generic “we need RWD” requirement. Second, take that document to a shortlist of three to five vendors spanning at least two segments, and require a feasibility count, a data dictionary, and a limitations statement from each before discussing price. Buyers who anchor the process in concrete study questions consistently avoid the most expensive failure mode in this category: licensing an impressive dataset that cannot actually answer the question the organization needed answered.