It all starts with a data warehouse


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

Read More About Data Warehousing in Healthcare

Why Do You Need a Data Warehouse?

New Gartner Report Covers Major Shift From EMRs to Data Warehousing and Analytics

Why Your Healthcare Business Intelligence Strategy Can’t Win without a Data Warehouse
Paul Horstmeier, Senior Vice President

Using Clinical Data Repositories Versus a Data Warehouse: Which Do You Need?
Tim Campbell, Vice President Technology

6 Surprising Benefits of Healthcare Data Warehouses: Getting More Than You Expected
Mike Doyle, Vice President

5 Myths You Won’t Need to Worry About When Adopting a Clinical Data Warehouse
Mike Doyle, Vice President

Why Are Data Warehousing Needs Unique in Healthcare?

5 Reasons Healthcare Data Is Unique from Other Industries
Dan LeSueur, Vice President Technology

Why Knowing When to Bind Your Healthcare Data is So Critical in Healthcare
Steve Barlow, Senior Vice President Operations and Co-Founder

What Are the Different Approaches for Data Warehousing in Healthcare?

The Late-Binding Data Warehouse Explained (white paper)
Dale Sanders, Senior Vice President, Strategy

What Is the Best Healthcare Data Warehouse Model? Comparing Enterprise Data Models, Independent Data Marts, and Late-Binding Solutions
Steve Barlow, Senior Vice President and Co-Founder

Comparing Star Schema vs. Late-Binding Approaches in Healthcare Data Warehousing
Steve Barlow, Senior Vice President and Co-Founder

Comparing EMR-Based Models vs. Late Binding Approaches in Healthcare Data Warehousing 
Eric Just, Vice President, Technology

Build vs. Buy a Healthcare Data Warehouse: An Honest Comparison of the Options
Mike Doyle, Vice President

Late-Binding Data Warehousing: An Update on the Fastest Growing Trend in Healthcare Analytics
a webinar by Dale Sanders, Senior Vice President (including slides and transcripts)

A Health Catalyst Overview: Building a Data Warehousing and Analytics Strategy (a webinar)
Eric Just (Vice President, Technology), and Mike Doyle (Vice President)

Key Considerations Once You Select a Data Warehouse Approach

6 Reasons Why Healthcare Data Warehouses Fail
Steve Barlow, Senior Vice President and Co-Founder

I Already Have a Data Warehouse. Can I Use Health Catalyst Applications With It?
Health Catalyst

Data Warehouse Tools: Faster Time-to-Value for Your Healthcare Data Warehouse
Doug Adamson, Chief Technology Officer

EDW Cloud Hosting: Is It Right for Your Health System?
Nate Arnold, Director, Infrastructure Systems

What Does a Data Warehouse Cost? How to Get a Return on Your Investment
Dan Burton, Chief Executive Officer


    Collect key Knowledge Center content to share with colleagues or yourself by dragging it here.