A pharma company is drowning in data, and yet every month someone still hand-assembles slides from a handful of spreadsheets. Sales data sits in one system, market data in another, trial and registry data in a third, and nobody owns a single, trustworthy picture. For a head of data, analytics or the commercial function, the problem is not a lack of data but that the pharma data warehouse and the reporting cannot keep pace with decisions.
In our experience, the hard part is not pulling data into one place but keeping it consistent, compliant and analysis-ready for years. We do not pretend to know your registries better than you do. We do something different: we strengthen the team with people who build warehouses, reports and analytics every day and can step into the realities of a regulated industry.
Why is pharma data so scattered and underused?
Because it was created in different places, at different times, for different purposes:
- sales and CRM live apart from market and reimbursement data,
- market data bought externally comes with its own formats and dictionaries,
- trial, registry and real-world clinical data land in entirely different systems,
- each country or affiliate counts the same thing slightly differently.
The result is that one metric has three values, depending on who calculated it. Until there is a shared data layer, analytics stays an extreme sport for a few people with Excel.
What is a pharma data warehouse and how do we build it?
A warehouse is a single source of truth: the place where data from many systems is cleaned, unified and ready for reporting. We build it in layers:
- source integration (CRM, ERP, market data, partner files) via ELT/ETL pipelines,
- a data model that reconciles dictionaries, products, geographies and time,
- a big data layer where volumes outgrow a classic database,
- a semantic layer with metrics computed once and used everywhere.
Our data engineers wire these pieces together so that new sources can be added without rewriting the whole thing.
How do we design reporting that doesn’t end up in Excel?
A report has value when people trust it and don’t have to fix it by hand. So we lean on:
- reports generated automatically from the warehouse, not from copied spreadsheets,
- one definition per metric, agreed with the business,
- versioning and auditability, so you can trace where a number came from,
- self-service for teams that want to dig deeper on their own.
Instead of multiplying files, we design a flow where there is one number and everyone knows its origin.
What do pharma sales and market dashboards look like?
Pharma sales analytics begins where the static report ends. We build dashboards that surface:
- sales and market share broken down by product, region and channel,
- momentum vs the market and competitors, not just your own figures,
- the effectiveness of commercial activity and field-force work,
- alerts and deviations that flag what needs a response on their own.
We embed them where the team already works, often on platforms like Salesforce or Creatio, so a decision-maker sees the market picture without switching tools.
How do we support real-world data (RWD) analysis?
Real-world data is data from outside clinical trials: registries, practice, actual medicine use. Its value is huge, but the data tends to be incomplete and hard to join. Here we support the team on the engineering and analytics side:
- we clean and link RWD sources into coherent, analysable datasets,
- we build pipelines that prepare data for analyses and models,
- we serve it to analysts and data scientists in a ready-to-use form,
- we co-create the logic with your experts, because they own the clinical context.
We do not replace your epidemiologists or biostatisticians. We give them a solid data layer to work on.
How do we approach safety and signal reporting?
Pharmacovigilance (drug-safety surveillance, signal and adverse-event reporting) is a specialised, heavily regulated domain. We treat it honestly: we confirm the scope and feasibility of support individually, and the details always remain to be verified by your safety and compliance experts.
Where we can genuinely help, we help on the data and engineering side:
- integration and quality of the data feeding safety processes,
- automation of flows and reporting around existing systems,
- auditability and data lineage required in a regulated environment.
The substantive decisions and regulatory compliance stay on your side. We strengthen the technical layer.
How do we handle data quality and compliance?
In pharma, data without quality and compliance is useless and sometimes risky. So we weave it into the foundation rather than bolting it on at the end:
- validation and quality checks at every stage of the pipeline,
- data governance: data owners, dictionaries, definitions,
- access control and an audit trail for regulatory requirements,
- repeatable, documented processes instead of manual fixes.
Which skills do we provide for a pharma data project?
We shape the team around the specific work, not “just in case”. Most often we staff the roles of:
- a data engineer building the warehouse, pipelines and integrations,
- a BI specialist designing reports, dashboards and the metrics layer,
- a big data specialist where volumes and RWD call for it,
- a data analyst / architect tying business requirements to the data model.
These are skills we grow on data and analytics projects, including for pharma clients. We combine them with our experience in commercial technologies (Salesforce, Creatio) and big data.
Where does the AI layer fit in all this?
We add AI optionally and only on top of a solid data layer, never instead of it. It genuinely helps where you need to:
- forecast sales or demand from patterns,
- segment the market or detect anomalies in the data,
- speed up work with text (reports, documentation) using GenAI/RAG tools.
Our AI and data competence is real (within the RITS Group), but we stay sober about it: clean data first, model second.
How do we do it at RITS?
We do it as a nearshore partner from Poland, in a team-augmentation or dedicated-team model:
- we staff the roles of data engineer, BI and big data to fit the project,
- we verify skills before the start and shape the team around the scope,
- we work in a close time zone, so the team joins your rhythm fast,
- in regulated areas like pharmacovigilance, we confirm scope individually.
We don’t promise a ready case study where we don’t have one. We supply the people and skills who build warehouses, reports and analytics, and plug into your processes.
Where to start building pharma data analytics?
Start from the single most important decision that lacks good data today (e.g. a product’s market share or the effectiveness of commercial activity). That defines which sources to connect first, how to design the warehouse and which roles to bring in. We covered the differences between engagement models in our article on IT engagement models.
Want to turn pharma data into reporting and analytics you can rely on? Let’s talk - we’ll match a team and an architecture to your data.