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Author Bio

Bryan Hinton

Bryan Hinton joined Health Catalyst in February 2012. Prior to joining the Catalyst team, Bryan spent 6 years with Intel and 4 years with the The Church of Jesus Christ of Latter-Day Saints. While at Intel Bryan was on teams responsible for Intel's factory reporting systems and equipment maintenance prediction. At the LDS Church he led the .NET Development Center of Excellence and was responsible for the Application Lifecycle Management (ALM) processes and tools used for development at the Church. Bryan graduated from Brigham Young University with a degree in Computer Science.

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Steve Barlow
Bryan Hinton

Healthcare Data Warehouse Models Explained

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.

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Bryan Hinton

The Four Essential Zones of a Healthcare Data Lake

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.
Exploration 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.

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Bryan Hinton
Sean Stohl

Hadoop in Healthcare: Getting More from Analytics

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:

Archiving
Streaming
Machine learning

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