In pharma and biotech, value no longer sits in a single result. It sits in whether you can join data from many studies, trials and labs into one analysable picture. For an R&D director or a data lead, the problem is rarely a lack of data. The problem is that clinical and research data is scattered across dozens of systems, formats and teams, and every analysis starts with the tedious work of gluing it back together.
In our experience, the hard part is not storing the data but building a repeatable integration in a regulated environment, with quality and an audit trail intact. This is an area where we provide the competence rather than an off-the-shelf product: we staff the project with data engineering, bioinformatics and big data specialists who have already worked under the rigours of pharma and biotech.
Why is clinical and research data so scattered and hard to work with?
Because it is created in many places, at different times and to different standards:
- EDC and CTMS systems collect trial data in their own schemas,
- labs and instruments produce results in device formats, not an analyst’s format,
- research teams keep data in spreadsheets, notebooks and local databases,
- external partners (CROs, contract labs) deliver data in yet other structures.
The result is that the same variable gets described several ways, and joining the data takes longer than the analysis itself. Integration therefore starts by settling what the data means, not by copying files.
How do you capture data from studies and clinical trials?
We design data capture so that errors do not enter at the source, because that is where they cost the most later:
- validation on entry, instead of cleaning data after the fact,
- standardisation to recognised models (e.g. CDISC/SDTM) already at the collection stage,
- automated pulls from EDC, devices and lab systems through APIs,
- metadata and sample context recorded together with the result, not attached by hand.
This way data flows in already in a shape you can join and analyse, not a shape you first have to rescue.
How do you integrate data from many sources into one coherent set?
Joining the sources is the heart of the whole problem. We build an integration layer that turns the chaos into a single model:
- ETL/ELT pipelines that extract, transform and load data from every source,
- a common data model and dictionaries that unify what variables mean,
- a central repository (a warehouse or data lake) as a single source of truth,
- versioning and data lineage, so it is clear where each value came from.
This foundation lets you add new sources without rewriting the whole thing, and gives researchers data they can trust.
How do you organise the exchange of laboratory data?
The lab is a world of its own formats and instruments, so we treat it as a dedicated stream:
- integrations with LIMS and sample-management systems,
- parsing of instrument output into a standardised form,
- mapping of identifiers for samples, patients and batches across systems,
- completeness checks, so no result is lost along the way.
We push for automated exchange where file exports and email used to rule, because that is exactly where errors and delays tend to appear.
How do you prepare cohorts and datasets for analysis?
Analysts and biostatisticians need data ready to work with, not a raw dump. So we build the analytical layer:
- definable cohorts built from inclusion and exclusion criteria,
- analysis-ready datasets in a fixed, repeatable shape,
- pseudonymisation and anonymisation where data protection requires it,
- dataset documentation, so everyone knows exactly what it contains and how it was made.
The point is to shorten the path from a research question to a dataset to hours, not weeks.
How do you build pipelines for statistical results?
Reproducibility of analyses is a requirement in a regulated environment, not a convenience. So we automate the path from data to result:
- automated pipelines in R and Python, run on fixed datasets,
- versioned analysis code, so a result can be reproduced months later,
- reports and tables generated from the data, not assembled by hand,
- compute environments that scale for large datasets and models.
Such a pipeline makes recomputing after a data correction a matter of rerunning the process, not another week of work.
How do you ensure data quality and compliance in a regulated environment?
In pharma and biotech, quality and compliance are not an add-on but the condition for a result to count. We build them into the pipeline itself:
- an audit trail for every data change (who, when, what),
- quality controls (validations, consistency rules) run automatically,
- access and role control, aligned with patient data protection,
- compliance with requirements such as GxP, GDPR or 21 CFR Part 11 built into the process, not bolted on at the end.
This way the data is not only joined, but also defensible under audit.
Which skills do we provide for a pharma data project?
We shape the team around the specifics of working with clinical and research data:
- data engineers building pipelines, warehouses and integrations,
- bioinformaticians who understand biological data and its context,
- big data specialists handling large, complex datasets,
- R/Python analysts and developers co-building the results layer.
Our big data and bioinformatics teams have already worked in a regulated pharma and biotech environment, so they join with a ready grasp of its rigours, rather than learning them on the way.
Where does the AI layer fit, if you choose it later?
AI adds value only once the data is already in order, so we treat it as an optional layer on a mature foundation:
- predictive models on standardised, joined datasets,
- search and structuring of text data (e.g. research documentation),
- analyst support in exploring large datasets.
We do not start with AI. We start with data you can trust, because without it every model simply repeats the same gaps.
How do we do it at RITS?
We do it as a nearshore partner from Poland, with real experience in data projects and in regulated environments:
- we staff the project with a data engineering, bioinformatics and big data team matched to the spec,
- we verify skills before the start, rather than hoping they fit along the way,
- we tie capture, integration and analysis into one maintainable whole,
- we work in a close time zone, so the team joins your rhythm without a long ramp-up.
Most often we do this as team augmentation or a dedicated team, because building a data platform is ongoing work, not a one-off rollout. We covered the differences between engagement models in our article on IT engagement models.
Where to start with capturing and integrating clinical data?
Start from the single most painful data join (for example, linking lab results to trial data) and from the question of what analytical dataset should come out of it. That defines the scope of the integration, the data model and the roles you need. From there, the data platform grows deliberately, rather than by gluing on one more export.
Capturing and integrating clinical or research data? Let’s talk - we’ll match a team and an architecture to your data.