Linked Real-World Data: How to Tell if Your Data Partner is Research-Ready

The real-world data (RWD) landscape is changing. As claims, EHR records, clinical notes, labs, and pharmacy data increasingly come together to establish longitudinal patient journeys, the best practices for data governance to protect data and patients are also evolving.

Life sciences teams need a data partner that can stand up to regulatory scrutiny in addition to delivering data depth. Having the right data is no longer enough; data partners must also showcase governance rigor through de-identification, data use agreements, access controls, auditability, and more.

In recognition of Cybersecurity Month, this blog explores:

• How governance changes the more that datasets are linked together
• Tips to gauge a potential vendor’s governance strengths
• What makes a research-ready partner stand out

How governance changes for linked real-world data

As different data sources are linked together, the linked real-world data develops a risk profile of its own. Although each input individually may have cleared a privacy review, the combined view has the potential to reveal more than any single source would. That connected profile is necessary to inform studies, but it requires higher levels of governance – and the strategy cannot be static. The risk profile for linked real-world data is a constantly moving target. It changes every time that a source is added, retired, or used in a new way.

A research-ready partner governs the linked dataset as a whole and keeps doing so from ingestion and tokenization through linkage, storage, delivery, and ongoing monitoring. Strong partners vet the quality of each source before linking it, and track lineage so that every record can be traced to its origin. They also apply semantic normalization so data from different systems is consistent as it’s linked.

In addition to increased linking, applying AI to linked real-world data raises the stakes even higher. Models and agents depend on the same clean, traceable data that researchers do. The argument for clean data is even stronger for AI use, though, as there’s more risk for models to scale whatever flaws they find. If built on poor-quality data, a model might seem sophisticated at first, but could just be confidently wrong.

For life sciences teams, one way to improve governance is to retain ownership of how you analyze and act your data, but don’t try to own all the processes and machinery surrounding it. A capable partner can keep researchers focused on the research, while seasoned governance and analytics experts maintain the data informing a particular objective.

Pressure-testing a data partner’s governance

All vendors can agree that data privacy is paramount, and most can share how their governance works to a certain extent.

To uncover what sets potential partners apart though, these are the questions to ask:

On linkage and privacy

• How were records matched? Ask whether matching was deterministic or probabilistic, what match rates looked like across sources, and how unmatched records were handled. Pay particular attention to how outliers are managed, as that’s a strong tell to an organization’s overall data practices.
• Can you share a link-level risk assessment? Expert determination should evaluate the combined risk profile across the full data life cycle, in addition to each individual source.
• Where did each source come from? Provenance, and the vendor’s rights to use every source, matter most when a study is headed for regulatory submission or publication. Primary source data is ideal whenever possible to obtain.

On controls and certifications

• What do your certifications cover? SOC 2 and HITRUST scope varies widely between vendors, so ask how your particular partner handles these.
• Are your guardrails written in policy or enforced by the system? Look for monitored role-based access, permitted-use terms enforced technically, and logs that trace each action to a specific person or AI agent.
• How do you prepare for a security incident? Ask about encryption in transit and at rest, continuity testing, recovery time, and how the vendor would communicate with customers.

The clearest warning sign is governance designed only for today. So, make sure to also ask potential partners where their governance programs are headed. If a partner can’t answer a forward-looking question, it may be a sign that their governance is reacting to changes in the RWD landscape rather than preparing for them.

Additionally, ask how a guardrail that protects data today will adapt when the data or the use case changes. If the answer depends on someone remembering to update it, treat that as a gap. Guardrails must evolve as the data evolves, and a manual step is an outdated approach.

Choosing a partner that keeps pace with RWD demands

As life sciences teams look ahead, one key principle to determining the best data partner is to find organizations who design linkage depth and patient privacy hand in hand. A partner that meets this standard is one who builds governance into every stage of the data life cycle, runs on secure infrastructure, and adapts as linked real-world data does.

Inovalon brings that discipline to the industry’s largest primary source dataset, giving life sciences teams real-world evidence they can stand behind. Explore our Real-World Data™.

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