Modernizing operational architecture with big data: Creating and implementing a modern data strategy

The use of data throughout Cerner had taxed the company’s legacy operational data store, data warehouse, and enterprise reporting pipeline to the point where it would no longer scale to meet needs. Cerner’s internal operational data store once provided significant value but has since become a bottleneck to the architecture. The variety, velocity, and volume of data that users wanted to access far exceeded what the pipeline was designed to handle five years ago. The company has thrown hardware at it. It has optimized the software and the data model. It even stopped allowing access to it—and then allowed access to it again because its data is so valuable. Yet it still comes up at almost every system risk review meeting. The company could not just lift and shift the legacy ETL and databases to the cloud; it had to sort the use cases into functional patterns and technology alignment, leading to an overall modernized platform.

Jennifer Lim explains how Cerner modernized its corporate data platform with the use of a hybrid cloud architecture. Jennifer starts by outlining the problem statement she was given to resolve: help address the numerous resource utilization conflicts within the operational data store (ODS). Jennifer then outlines the modernization strategy created to solve the problem, namely creating the corporate data platform. She explains how the platform addresses the company’s ability to decouple its architecture through API management, scale with hybrid cloud data architectures, and incorporate open source technologies. She also shares some takeaways into the differences between what was described in industry articles and what she found in real life, as expectation management is an often-missed piece of setting an achievable strategy.