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    Late-Binding™ Data Warehouse

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    It all starts with a data warehouse

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    Most large healthcare organizations have hundreds of analytics vendors. Without bringing all of their data into an enterprise data warehouse (EDW), reliable and repeatable reporting and analysis is impossible.

    Health Catalyst’s Late-Binding™ Data Warehouse is a revolutionary architectural model for healthcare analytics. When an organization combines an EDW with the power of Late-Binding™, they quickly progress to registries and reporting, population health, and clinical and financial risk modeling.

    Late-Binding™ architecture

    Data must undergo massive transformations to fit into an enterprise data model.

    The pitfalls of early binding

    Early binding architectures – like those espoused by Bill Inmon, Ralph Kimball, and others – force early data bindings into proprietary enterprise data models. Time has proven early binding architectures to be inflexible, one-size-fits-all solutions, enforcing a compromised, least-common-denominator warehouse.

    The power of Late-Binding™

    Health Catalyst’s Late-Binding™ architecture avoids those inherent limitations. By delaying data binding until the proper time and context, data retains its original, undiluted value.

    Late-Binding™ Data Warehouse explained: A technical overview from a groundbreaker in healthcare analytics

    Health Catalyst’s Late-Binding™ principles

    Data warehouses in the military, manufacturing, and healthcare that have operated by these principles for more than 20 years continue to deliver an unparalleled track record for proven results.

    1. Minimize remodeling data in the data warehouse until the analytic use case requires it. Leverage the natural data models of the source systems by reflecting much of the same data modeling in the data warehouse.
    2. Delay binding to rules and vocabulary as long as possible until a clear use case requires it.
    3. Earlier binding is appropriate for business rules or vocabularies that change infrequently or that the organization wants to lock down for consistent analytics.
    4. Late binding in the visualization layer is appropriate for what-if scenario analysis.
    5. Retain a record of the changes to vocabulary and rule bindings in the data models of the data warehouse. This will provide a self-contained configuration control history that can be invaluable for conducting retrospective analysis that feeds forecasting and predictive analytics.

    Next: Key components of the Health Catalyst Late-Binding™ Data Warehouse


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