Piedmont is a not-for-profit healthcare provider consisting of 8 hospitals and 1674 beds based around the Atlanta Metro area. In 2016 alone they served over 2 million patients, with plans to increase its footprint further in 2018 with the addition of 3 more hospitals to the network. The business intelligence and data architecture teams are comprised of 24 people, who are responsible for compiling and disseminating the healthcare provider’s enormous amount of raw and highly sensitive data to its planning and analysis groups, and relevant hospital decision makers.
The Initial Situation
Healthcare providers store a colossal amount of data in the form of decades of patient information, gathered before the real birth of data analytics, and before the concept of “big data” even existed. Piedmont alone had over 22,000 fields to analyze gathered from around 30 different published data sources. Multiplied by the number of records available Piedmont had to extract value from over 555 billion data points.
Piedmont wanted to take their processing and understanding of its available data to the next level. Thanks to an aggressive growth strategy greatly increasing the size of the business, Piedmont ran into significant scalability problems with their old SQL Server system, resulting in mounting security, speed and cost issues when analyzing its data.
Piedmont outlined its requirements for a replacement with a set of guiding principles: