Commercial Pharma Analytics
Our Data Engineers embed with your team to deliver a Commercial Data & Analytics Platform you control — from product launch through ongoing commercial operations.
$100 Billion in Customer Acquisitions
Three DataKitchen pharma customers secured exits totaling $100 billion — powered by our commercial data platform.
Our Data Engineers work as part of your team to deliver a Commercial Data & Analytics Platform that you control. Make the changes you need to keep up with your customers’ demands. Our team integrates varied data sources into a single, cross-channel Agile Data Warehouse that supports your product launches and the ongoing growth of commercial products with high-quality, on-demand analytics.
During a product launch, brand and sales management must rapidly alter their tactics and strategies to meet forecasts. We created an integrated Commercial Data & Analytics Platform under the brand and sales team's control. This enabled the team to:
Managed Market teams spend millions on data, systems, software, and ad hoc consulting. Our Commercial Data & Analytics Platform fully integrated all relevant data to answer key questions such as:
Life science organizations must drive specialty therapy revenue by identifying new prescriber opportunities, optimizing treatment paths, and flagging approval roadblocks or improper use scenarios. We created an integrated Specialty Pharma Provider (SPP) Commercial Data & Analytics Platform under the Specialty Pharma team's control. This enabled the team to:
Digital channels have disrupted the industry. Non-personal promotion (NPP) data sources have exploded with insight into every aspect of your customer, their interaction channel, and their journey. Pharma companies must leverage this insight to understand customers and drive meaningful engagement. We created an NPP data Commercial Data & Analytics Platform under the analytic team's control. This enabled the Marketing team to:
The people who scope the work deliver it, with decades of experience in data analytics, software engineering, and DataOps transformation.
Talk to our team about how DataKitchen services can accelerate your DataOps transformation.
Common questions about Commercial Pharma Analytics
You did not create most of it. Specialty pharmacy dispense feeds, claims, syndicated market data, 3PL reports, and patient services records arrive from vendors on their schedule and in their format. The picture of a prescriber or account is assembled from several sources, so failures live in the relationships between feeds.
Partial files that look complete, vendor restatements that read as growth, and territory realignments that make a rep's numbers drop overnight. None of these trip a null check, because the data that arrived is perfectly valid and simply incomplete, restated, or remapped.
Because the identifiers fail silently. NPI, NDC, DEA, ICD, HCPCS, and place-of-service codes are the join keys of the entire ecosystem, and a wrong-format NDC still looks like an NDC. The join quietly drops rows and every downstream number comes in low with nothing erroring.
Faster than a traditional program, because the approach starts by measuring the feeds you already receive rather than by designing a target model first. That produces working coverage and a defensible finding list early, which is what keeps a launch timeline realistic.
Yes, and launch is the common case. Launch analytics depend on vendor feeds that are new, changing, and unforgiving of error, with field credibility on the line from week one. Automated coverage matters most exactly when the team is smallest and the scrutiny is highest.
Yes. The tooling is Apache 2.0 and runs behind your firewall, with tests executing as SQL inside your own warehouse so no rows cross the wire. For pharma security reviews, that is usually the question that decides whether an evaluation continues.
Eric Estabrooks, Co-Founder and VP of Services, and Chris Bergh, CEO, lead this work, and the engineers are DataKitchen staff rather than subcontractors. The track record is specific: the commercial analytics platform behind the Otezla launch at Celgene ran more than 1,000 dashboards on hundreds of data sets, covered by over 20,000 automated tests, and absorbed more than 100 schema and data changes a week. Three DataKitchen customers, Celgene, Karuna and Acceleron, were later acquired for $100 billion combined.
