Should an emerging pharma build, hire, bring in a dedicated team, or use open source for commercial data?

Pick the model that leaves you owning the code and feed knowledge when people leave. Six fully loaded hires in Boston or the Bay Area cost $1.5 million to $2 million a year, after a six to nine month search. Hourly vendors keep the work. Open source still needs operators. A dedicated team in your repository lets you hire later.

By Chris Bergh Published October 20, 2026

Key points

Six fully loaded commercial data FTEs in Boston or the Bay Area run $1.5 million to $2 million a year, and the recruiting cycle runs six to nine months before anyone ships.
A general IT team can learn specialty pharmacy, claims, and syndicated data eventually, but the learning happens on your launch timeline.
An hourly model gives you one lever, the rate. Offshoring lowered the cost of every manual step in the pipeline and removed none of them.
Open source data quality software is free to run, and running it is still a job. DataOps TestGen's open source edition is one user, one connection, and one project.
Ownership is the test that separates the options: whether the warehouse sits in your cloud, the code in your repository, and the exit carries a fee.

You have five options, and the vendors pitching each will tell you the other four are wrong. The useful question is narrower: when the people change, who still has the code and the knowledge of your feeds?

Five models, compared

Model	Cost	Who owns the code	Domain knowledge	Continuity	Exit
Your IT department	Internal, but your requests queue behind every other department	You	Generalist; learns pharma feeds on your timeline	Shared with every other priority	Nothing to exit
Hire your own team	Six FTEs at $1.5M to $2M a year, plus six to nine months to recruit	You	Strong once hired	Rests on keeping specialists who are hard to keep	Nothing to exit
Hourly analytics or offshore vendor	Around $300 per analyst hour for consulting; offshore cut the rate, not the steps	Often the vendor	Varies by vendor	Tied to the contract	You get back what the contract says
Dedicated commercial data team	One to three engineers at a flat monthly rate	You, from the first sprint	Brought on day one	Tests and code in your repo outlast any engineer	No exit fee; hand over to anyone
Open source, run yourself	Software free; people still needed	You	Whatever your team has	Rests on whoever runs it	Nothing to exit

Sources: hiring and consulting figures from the cash-runway post; IT and domain knowledge from the $100 billion secret; offshore economics from the post on the three ages of commercial data engineering.

Cost is mostly people, so count them honestly

Every option except open source is a staffing decision. Six commercial data FTEs fully loaded in Boston or the Bay Area run $1.5 million to $2 million a year. A multi-year analytics consulting agreement runs higher than that, by the same post’s numbers. DataKitchen typically staffs one to three engineers, and at one rare-disease biotech, consolidating two hourly vendors into that model cut the annual cost of running commercial data by about 58%.

The hourly models carry a specific trap. A developer offshore cost a third of one in New Jersey, so teams bought hours in bulk. But the hours never went down, because offshoring lowered the wage on every manual step and removed none. Same assembly line, same defects, lower labor rate.

Domain knowledge is the cost nobody budgets

Specialty pharmacy files, payer hierarchies, NDC levels, vendor restatements. A generalist IT team can learn them eventually, but the learning happens on your launch timeline.

James Royster led an analytics function at Celgene for nearly seven years. His account of why he chose DataKitchen is about method as much as people: testing at every stage, automating any task done more than twice a year, and a team that did more analytics, faster, with fewer errors than the companies it benchmarked against.

Open source solves the license, not the job

Open source removes lock-in on the software. DataOps TestGen and DataOps Observability are both Apache 2.0. In a DataKitchen engagement the data quality software runs in your cloud, so no license expires when we leave.

It does not remove the work. The open source edition of TestGen is one user, one connection, and one project, and the commercial pharma identifier tests ship only in Enterprise. More to the point, building a pipeline is the small part. Running it never ends: the schema change, the late file, the expired credential.

Ownership decides the exit

The test that separates these models is what you hold on the day you want to change. With DataKitchen, the warehouse sits in your cloud account and the code and tests sit in your repository from the first sprint. The rate scales down on 30 days’ notice. Transfer goes to your team, Accenture, or your offshore center, with no exit fee. Eisai moved one platform to Accenture that way.

The models also mix. On the BMS CAR-T warehouse, 41 contributors from five companies, including Beghou and Accenture, have worked one codebase over seven years. That works only when tests are the contract.

One limit on DataKitchen before you choose: we do not do incentive compensation, territory alignment, call planning, or brand strategy, so those need covering elsewhere. And if you hire your own team later, we hand the platform to it. The in-market cost page shows how a takeover works when you already run something today.

Frequently asked questions

Short answers to the follow-up questions people ask next.

Should we just hire our own commercial data team?

Eventually, and DataKitchen hands the platform to that team when you do. Today the hiring cycle runs six to nine months before anyone ships, six fully loaded commercial data FTEs in Boston or the Bay Area cost $1.5 million to $2 million a year, and specialized commercial pharma data engineers are hard to recruit and harder to keep. Starting with a team that already knows the feeds buys back the recruiting window.

Can our IT department run commercial data?

It can, slowly. A general IT team serves every department, so a request for integrated claims data waits behind finance and regulatory work. The deeper gap is domain knowledge: specialty pharmacy files, medical claims, payer hierarchies, and syndicated restatements. A generalist team learns those eventually, and the learning happens on your launch timeline rather than before it.

Why not keep paying analytics consultants by the hour?

Because the incentive points the wrong way. Analytics consultants bill analyst hours at around $300 each, so more hours is a better year for them, and the work stays with them when the agreement ends. A multi-year analytics agreement runs higher than six in-house FTEs and transfers nothing back. A flat rate makes a leaner operation the vendor's gain as well.

Is open source software enough to run commercial pharma data?

The software, often. The operation, no. DataOps TestGen and DataOps Observability are Apache 2.0, so the tests and monitoring stay yours with or without a vendor. TestGen's open source edition is one user, one connection, and one project, and the commercial pharma identifier tests ship only in Enterprise. Somebody still has to triage failures and fix feeds every morning.

What happens when we want to take the platform back?

With DataKitchen, a normal day rather than an event. The warehouse runs in your cloud account and every line of code and every test lives in your repository from the first sprint, so there is nothing to extract. Transfer goes to your team, Accenture, or your offshore center, with no exit fee. Eisai moved one platform to Accenture that way.

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