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Resources

Reading, listening, and watching from the pharma practice.

Real launches, real platforms, real numbers. Case studies, white papers, webinars, and writing — most pull from the main DataKitchen site so they stay co-located with the broader product and methodology content.

blogs

Blogs

The $100 Billion Secret

Blog

Why leading pharma companies hand commercial data to a specialized outside team: focus, speed, and transparency instead of a queue behind every other department.

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How DataOps is Transforming Commercial Pharma Analytics

Blog

The first 6–12 months of a launch determine its lifetime revenue. The Otezla story: 7 engineers, 10–12 analysts, hundreds of datasets, tens of thousands of tests.

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Implementing a Pharma Data Mesh using DataOps

Blog

How DataOps automation makes data-mesh architectures viable in commercial pharma — not as a slogan, but as something engineering teams can actually ship and operate.

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The Equation for AI Success: DT + Dx + Ctx = 10×

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Where AI lands in commercial pharma analytics. Data transformation, data exposure, and context: the three terms that turn an AI investment into measurable team productivity.

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DataOps FITT: Data Testing for 10× Data Engineering Productivity with AI

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A practical look at how AI-assisted test generation reshapes commercial pharma data engineering — with a pharma example walked end-to-end.

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Reinvent Marketing Automation with DataKitchen DataOps Automation

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A non-personal-promotion story. How an integrated commercial data platform reset the team's relationship with marketing campaign data, channel ROI, and budget decisions.

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Why I Chose DataKitchen

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A practitioner's view from inside a commercial pharma analytics team. What "transparent, transferable, specialized" actually feels like day-to-day.

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Congratulations to the Karuna Team on Their Acquisition

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A note marking the Karuna → BMS acquisition, with reflection on what made the underlying commercial data work so durable through the transition.

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Track record

The launches behind the reading list.

Which pharma launches has DataKitchen worked on?

Karuna Therapeutics for the Cobenfy launch, Celgene for Otezla and Revlimid, and Acceleron. BMS acquired Karuna for $14 billion and Celgene for $74 billion. Merck acquired Acceleron for $11 billion. The platform stayed with the customer through every one of those transactions.

What results can you point to?

An average of 1.5 engineers built the whole Cobenfy launch platform at Karuna over two years, covering 50 integrated datasets. At Celgene, seven of our engineers supported $10 billion in US brand sales, and only 18 percent of engineering time went to data loading rather than new analyst work. One pharma cut data costs by two-thirds when we replaced its setup.

What error rate should we expect?

Low enough that the field stops asking. A commercial insights leader at Celgene reduced errors to about one per quarter and held that for years. The Otezla launch team shipped hundreds of schema and dataset changes a week with negligible error rates, because tests run at every stage rather than at the end.

Does this hold up in acquisition due diligence?

It has, three times. Due diligence scrutinizes whether the commercial story is accurate, auditable, and defensible, and a reconciliation trail on every feed is what turns "trust me" into "here is the check that passed." Our customers' platforms survived every exit and stayed with the customer each time.

Where should I start reading?

Two posts cover the argument end to end. The $100 Billion Secret explains why pharma companies hand commercial data to a specialized outside team instead of a general IT queue. The cash-runway post works the numbers for a small pharma launching in 12 to 24 months.
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Want a deeper conversation?

The writing only tells part of the story. Half an hour with our team will get you specifics on your stack, your launch window, and what the first sprint would look like.