Every mention of health data and data analytics comes with it a reference to data silos that get in the way of effective use of data analytics tools. With so-called big data tools like Hadoop we have the ability to do analytics at scale and with streaming data, but what are the data warehouse strategies that are needed as the underlying architecture for this vision to become a reality?
Most HCOs have a large number of disparate data assets of both structured and unstructured data. Increasingly they will need to integrate clinical and financial data to drive insights on both clinical and operational aspects of the organization. For many decisions data analytics will need to be in as close to real-time as possible. As the volume of patient-generated data grows from sources such as social media, devices, and wearables, HCOs are feeling the heat to find database answers to scaling up their analytics. Data lakes are becoming a popular data warehouse approach to addressing these challenges.