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Data Lake vs. Data Warehouse: Which is Right for Healthcare?

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The data lake style of a data warehouse architecture is a flexible alternative to a traditional data warehouse. It allows for unstructured data. When a warehousing approach requires that the data be in a structured format, there are constraints on the analyses that can be performed because not all of the data can be structured early. The data lake concept is very similar to our Late-Binding approach in that data lakes are our source marts. We increase the efficiency and effectiveness of these through: 1. Metadata, 2. Source Mart Designer, and 3. Subject Area Mart Designer.

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Data Lake vs. Data Warehouse: Which is Right for Healthcare?

  1. 1. Data Lake vs. Data Warehouse: Which is Right for Healthcare? ―Jarod Crapo
  2. 2. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Introduction to the Data Lake In 2010, James Dixon introduced the concept of the Data Lake, and his idea has gained traction ever since. Dixon’s Data Lake is a style of data warehouse architecture, which he describes as follows: If you think of a datamart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.”
  3. 3. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Introduction to the Data Lake Dixon conceived of this architecture as a flexible alternative to traditional data warehouses that keep data in a very structured format. Structure puts constraints on the analyses that can be performed and may not accommodate future needs. The Data Lake concept allows for unstructured data—and more flexibility to answer new questions.
  4. 4. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. The Late-binding EDW and the Data Lake At Health Catalyst, we see a lot of value in this idea of the Data Lake. In fact, Dixon’s Data Lake concept is very similar to our Late-Binding™ enterprise data warehouse (EDW) architecture. What Dixon calls a Data Lake, we call a source mart. We bring data from our source systems into these source marts keeping data as raw as possible, relying on the natural data models of the source systems.
  5. 5. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Benefits of a Flexible, Late-binding EDW Here are three key benefits that make this the best architecture for healthcare EDWs: 1. A user only has to go to the source systems one time. 2. A flexible architecture enables analysts to respond to any and all future needs. 3. Users can scale the size of an EDW easily with this architecture using traditional Microsoft database tools.
  6. 6. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. How to Drink the Data Lake’s Water In a discussion of a Data Lake (or any kind of data warehouse architecture), the key question is this: How are users going to use the water? Health Catalyst offers three applica- tions to increase the efficiency and effectiveness of the Data Lake: 1. Metadata 2. Source Mart Designer 3. Subject Area Mart Designer
  7. 7. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. How to Drink the Data Lake’s Water Healthcare systems leaders can use the data warehouse (lake) to improve clinical quality or to drive operational efficiency. It can be used to manage an accountable care organization. Find an analytics partner with proven experience driving improvement and a flexible EDW architecture, and slake the thirst of physicians.
  8. 8. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Data Lake in Health Catalyst Social Media
  9. 9. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. More about this topic Early- or Late-binding Approaches to Healthcare Data Warehousing: Which Is Better for You? Mike Doyle, Vice President Data Warehouse Tools: Faster Time-to-Value Doug Adamson, Chief Technology Officer Six Reasons Why Healthcare Data Warehouses Fail Steve Barlow, Co-founder and Senior Vice President Client Operations I Already Have a Data Warehouse. What Applications Can I Use? A Guide to Using Health Catalyst Tools Within Your Existing Data Warehouse Environment Data Warehouse Trends: What to Know Before Setting Up Your Data Warehouse Dale Sanders, Senior Vice President of Strategy Link to original article for a more in-depth discussion. Data Lake vs. Data Warehouse: Which is Right for Healthcare?
  10. 10. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. For more information:
  11. 11. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Jared Crapo joined Health Catalyst in February 2013 as a Vice President. Prior to coming to Catalyst, he worked for Medicity as the Chief of Staff to the CEO. During his tenure at Medicity, he was also the Director of Product Management and the Director of Product Strategy. Jared co-founded Allviant, a spin-out of Medicity, that created consumer health management tools. In his early career, he developed physician accounting systems and health claims payment systems.

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