Want to know the best healthcare data warehouse for your organization? You’ll need to start first by modeling the data, because the data model used to build your healthcare enterprise data warehouse (EDW) will have a significant effect on both the time-to-value and the adaptability of your system going forward. Each of the models I describe below bind data at different times in the design process, some earlier, some later. As you’ll see, we believe that binding data later is better. The three approaches are 1) the enterprise data model, 2) the independent data model, and 3) the Health Catalyst Late-Binding™ approach.
Learn more about Bryan Hinton
Bryan Hinton joined Health Catalyst in 2012 and currently serves as the Chief Technology Officer. He has been instrumental in the development and integration of the Health Catalyst Data Operating System (DOS™)—Health Catalyst’s primary technology platform. Prior to joining Health Catalyst, Bryan spent 4 years with The Church of Jesus Christ of Latter-Day Saints and 7 years with Intel. While at the LDS Church, he led the .NET Development Center of Excellence and was responsible for setting the architectural guidance of all .NET projects across the company. As a senior software engineer at Intel, he was responsible for the development and implementation of Intel’s factory data warehouse product that was installed at Intel factories worldwide. Bryan carries a deep technical experience and understanding across all facets of software engineering with an emphasis on adopting software engineering practices as part of the data engineering process. He is passionate about building software and creating healthy engineering environments and teams. He believes deeply that curiosity and continuous learning are necessary skills to be fostered and encouraged for a successful team, and is an advocate for a healthy work-life balance. Bryan graduated from Brigham Young University in Provo, UT with a BS in Computer Science.
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The role of a data lake in healthcare analytics is essential in that it creates broad data access and usability across the enterprise. It has symbiotic relationships with an enterprise data warehouse and a data operating system.
To avoid turning the data lake into a black lagoon, it should feature four specific zones that optimize the analytics experience for multiple user groups:
Raw data zone.
Refined data zone.
Trusted data zone.
Each zone is defined by the level of trust in the resident data, the data structure and future purpose, and the user type.
Understanding and creating zones in a data lake behooves leadership and management responsible for maximizing the return on this considerable investment of human, technical, and financial resources.
Healthcare data is positioned for momentous growth as it approaches the parameters of big data. While more data can translate into more informed medical decisions, our ability to leverage this mounting knowledge is only as strong as our data strategy. Hadoop offers the capacity and versatility to meet growing data demands and turn information into actionable insight.
Specific use cases where Hadoop adds value data strategy include: