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The DataOps Way to Commercial Pharma Data
2026
Launch faster, spend less, and trust the numbers. 25 chapters on why commercial pharma data fails the way it does, what a launch data platform has to deliver and when, and who should run it.
- Length
- 209 pages
- Price
- Free
About this book
The DataOps Way to Commercial Pharma Data collects ten years of writing on running the data operation behind a drug launch. It argues that commercial pharma data fails in specific, predictable ways, and that the fix is to run the commercial data operation as a factory: every table tested on every run, deployment through automation rather than tickets, and bad vendor files caught before the field sees them. Chapters are grouped into why commercial pharma data is different, the launch, who should run your commercial data, the process hub and the platform, AI and technology, and running it day to day. Case studies carry the customer's own numbers from Celgene, Karuna, and other launches.
Key points
- The DataOps Way to Commercial Pharma Data argues that commercial pharma data fails in specific, predictable ways: vendor feeds on slipping schedules, identifiers that look valid and are not, and reports that break while every underlying value stays correct.
- The first six to twelve months of a launch set the trajectory a brand rides for the rest of its life, so the launch data platform has to deliver the right reports on a fixed clock.
- The book treats the commercial data operation as a factory: every table tested on every run, deployment through automation rather than tickets, and bad vendor files caught before the field sees them.
- Case studies carry the customer's own numbers: seven engineers behind about $10 billion in US sales at Celgene, one and a half engineers behind the Cobenfy launch platform at Karuna, and three customers acquired for nearly $100 billion combined.
- 23 of its 25 chapters are published in full on datakitchen.io, linked from the table of contents on this page. The other two were rewritten for the book from pages on pharma.datakitchen.io.
- Written by Chris Bergh, Gil Benghiat, and Eric Estabrooks, who have worked with commercial pharma data for more than two decades. 209 pages, published September 2026, free.
Download Your Free Copy
Complete the form below to get The DataOps Way to Commercial Pharma Data.
What's inside
25 chapters, most of them published here first: the book's contribution is the selection and the order. Linked titles go straight to the full text, no form required.
Why commercial pharma data is different
The launch
Who should run your commercial data
- The $100 Billion Secret: Why Leading Pharma Companies Bring In a Dedicated Commercial Data Team
- The Age Of Outsourcing Your Commercial Pharma Data Team To India Is Over
- You Built the Thing. Now You’re Stuck Running It.
- We Have a Cadillac, We Need a Corolla: Cutting a Biotech’s Data Bill to Fund the Next Launch
- Cut the Data Run-Rate on the Product You Already Sell
- Why I Chose DataKitchen for DataOps
The process hub and the platform
AI and technology
Running it day to day
Why we wrote it
Commercial pharma data is not like other data. The feeds arrive from vendors you do not control, on schedules that slip, with identifiers that look valid and are not. A prescriber gets duplicated and the incentive comp run pays the wrong rep. A specialty pharmacy file lands half empty during the weeks that matter most. And all of it lands on a clock, because the first six to twelve months of a launch set the trajectory a brand rides for the rest of its life. You get one chance at the field’s trust.
The people asking for those numbers are demanding, and they should be. A brand lead who catches a bad number once stops trusting the dashboard for good. Commercial teams work with some of the most complicated datasets in any industry and know their market and their customers extremely well. What they are not, and should not have to be, is data engineers. They should never have to know why the IQVIA refresh disagrees with the claims file.
The three of us have worked with commercial pharma data for more than two decades. Seven of our engineers supported about $10 billion in US sales at Celgene, with ten to 12 analysts covering hundreds of datasets and no missed SLAs. One and a half engineers built and ran the launch data platform behind Cobenfy at Karuna, across 50 integrated datasets. Three of our customers have been acquired for nearly $100 billion combined. We did not get there with more people. We got there by treating the commercial data operation as a factory: one that tests every table on every run, deploys through automation rather than tickets, and catches a bad vendor file before the field ever sees it. That way of working is DataOps. It is the argument of this book.
The chapters were written over ten years and gathered in one place for the first time. They cover why commercial pharma data fails in the specific ways it does, what a launch data platform has to deliver and when, who should run your commercial data and what it should cost, the process hub and platform underneath, and what AI changes about all of it. Some are case studies with the customer’s own numbers. Some are arguments we expect you to disagree with. People who have run the standup, chased the vendor about the late file, and explained the restated number to the brand lead wrote every one of them.
Our tools are open source, our engagements are flat rate, and the code lives in your repository from the first sprint, so the platform is yours to keep or hand to whoever you name. We wrote this book for the same reason we work that way: the knowledge of how to run commercial pharma data well should not be locked inside a consulting contract.
Download the free 209-page book here:
Frequently Asked Questions
Common questions about The DataOps Way to Commercial Pharma Data
What is The DataOps Way to Commercial Pharma Data about?
It is a guide to running the data operation behind a commercial pharma launch and the brands that follow it. Across 209 pages it covers why commercial pharma data fails the way it does, what a launch data platform has to deliver and when, who should run your commercial data and what it should cost, the process hub and platform underneath, and what AI changes about all of it. Several chapters are case studies with the customer's own numbers.
Who should read it?
Heads of commercial data, analytics, and IT at small and mid-sized pharma companies, especially those launching a product in the next one to two years. The chapters assume you know your market and your prescribers and do not want to become a data engineer to get a trustworthy territory report.
Is The DataOps Way to Commercial Pharma Data free?
Yes. The full 209-page PDF costs nothing: fill in the form on this page and we send you a copy. 23 of the book's 25 chapters are also readable on this site with no form at all, and the table of contents above links to each of them.
Who wrote The DataOps Way to Commercial Pharma Data?
Chris Bergh, Gil Benghiat, and Eric Estabrooks. Chris and Gil are two of DataKitchen's three co-founders, and Eric leads the commercial pharma data engagements. Between them they have worked with commercial pharma data for more than two decades, including the launch data platforms behind Celgene, Karuna, and Acceleron.
What makes commercial pharma data different?
The feeds arrive from vendors you do not control, on schedules that slip, with identifiers that look valid and are not. Prescriptions, claims, specialty pharmacy dispenses, payer hierarchies, and field activity come from a dozen syndicated sources that disagree about what a date means. A duplicated prescriber pays the wrong rep; a half-empty specialty pharmacy file lands in the weeks that matter most. And all of it lands on a launch clock.
Do I need DataKitchen software or services to use the book?
No. The book covers a way of working, and the practices apply to whatever stack and team you already have. DataKitchen does build open-source tools that match those practices, DataOps TestGen and DataOps Observability, and runs commercial data operations for pharma customers at a flat rate with the code kept in the customer's own repository.
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