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Amgen: building and running an enterprise data warehouse platform

Amgen is one of the world’s leading biotechnology companies, developing medicines for patients with serious illnesses. Research at that scale generates data continuously, from many systems, in many shapes, and the ability to analyse across it rather than within each silo is a direct constraint on what the science can ask.

LOCATION

Thousand Oaks, California, United States

COMPANY SIZE

Fortune 500 Company

INDUSTRY

Healthcare and Life Sciences › Explore solutions for this industry

Enterprise data warehouse platform, built and supported

ThirdEye Data developed and supported Amgen’s data warehousing platform, running both on premises and in the cloud. That combination is common in regulated industries and awkward to engineer well, because a system split across two environments tends to acquire two sets of behaviour unless it is deliberately designed not to.

The pipeline work handled ingestion from many sources arriving in many formats, which is the ordinary condition of an enterprise that has been operating and acquiring systems for decades rather than an unusual complication. We also took on the performance work: tuning and optimising the queries the warehouse depends on, which is the difference between an analyst getting an answer in the course of their work and putting in a request and waiting.

The part of the engagement with the most lasting effect was not technical. Amgen’s data scientists worked in an established analytical environment, and moving them onto the new platform meant supporting people, not just migrating workloads. We did that support directly, because a platform that scientists route around because it does not fit how they work is an expensive way to achieve nothing.

A data warehouse platform running across on-premises and cloud environments, with the existing analytical community moved onto it.

Related solution: enterprise data warehouse engineering

Enterprise data warehouse work in a regulated industry carries constraints that do not apply elsewhere. Data cannot always move where it would be most convenient. Change has to be traceable. Systems that cannot be switched off have to keep running while their replacement is built alongside them. None of that is technically exotic and all of it shapes what can be built.

ThirdEye Data has been doing this work since 2011, across hybrid estates, large scale migrations and platforms supporting analytical communities numbering in the hundreds. We treat the human migration as part of the engagement rather than a handover, because the most common failure in a warehouse programme is not the platform but adoption: the new system is finished, it works, and people keep using the old one because nobody helped them move. We have delivered this for biotechnology, for healthcare revenue operations, and for consumer technology at several hundred terabytes.

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