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Late-Binding™ Data Warehouse

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Late Binding Data Warehouse

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. Binding is the process of mapping the data in the EDW from source systems to standardized vocabularies (e.g., SNOMED, RxNorm) and business rules (e.g., length-of-stay definitions, ADT rules) so it can be brought together for analysis.

data binding in analytics

The Pitfalls of Early Binding

Traditional data warehouses try to model the perfect database from the outset, determining in advance every possible business rule and vocabulary set that will be needed.

This practice, called early binding, is a time-consuming, expensive undertaking. In healthcare, business rules and vocabularies change rapidly – and so do the use the cases that data linked across different source systems can serve. Mappings must be redone again and again as data models shift.

The Wisdom of Late-Binding

Health Catalyst’s Late-Binding architecture avoids the consequences of linking data with volatile business rules or vocabularies too early. By waiting to bind data until it’s time to solve an actual clinical or business problem, analysts:

  • Don’t have to make lasting decisions about a data model up front when they can’t see what’s coming down the road in two, three, or five years
  • Quickly adapt to new questions and use cases
  • Have the data they need to perform timely, relevant advanced analytics

 

Health Catalyst’s Late-Binding architecture has a proven track record of agility and adaptability to new rules, vocabularies, and data content that other designs have not matched.

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.

Health Catalyst’s Late-Binding architecture avoids wasted time and effort by waiting to bind data until a business case drives it, ensuring data retains its original, undiluted value.

Read more: Late-Binding Data Warehouse explained: A technical overview from Dale Sanders, Health Catalyst’s Executive VP of Software

 

The Six Binding Points

Knowing when and how tightly to bind data to rules and vocabularies is critical to the agility and success – or failure – of a data warehouse. Data in Health Catalyst’s Late-Binding Data Warehouse can be bound at six points:

Six points to bind data

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When data is extracted from source systems and loaded into the EDW, it undergoes almost no transformation. Aside from some minimal data conformance (for example, making sure the patient name field in one Source Mart is structured the same as the patient name field in another), the data is kept as raw as possible in a source system’s natural data models.

Minimal transformation also helps analysts easily track and catalog data lineage through a single enterprise metadata dictionary.

Health Catalyst’s Late-Binding Principles

The military, manufacturing, and healthcare organizations that have operated their data warehouses 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.

Read More About Data Warehousing in Healthcare

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New Gartner Report Covers Major Shift From EMRs to Data Warehousing and Analytics
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Why Your Healthcare Business Intelligence Strategy Can’t Win without a Data Warehouse
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Using Clinical Data Repositories Versus a Data Warehouse: Which Do You Need?
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6 Surprising Benefits of Healthcare Data Warehouses: Getting More Than You Expected
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5 Myths You Won’t Need to Worry About When Adopting a Clinical Data Warehouse
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Why Are Data Warehousing Needs Unique in Healthcare?

5 Reasons Healthcare Data Is Unique from Other Industries
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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
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Comparing Star Schema vs. Late-Binding Approaches in Healthcare Data Warehousing
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Comparing EMR-Based Models vs. Late Binding Approaches in Healthcare Data Warehousing 
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Build vs. Buy a Healthcare Data Warehouse: An Honest Comparison of the Options
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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

 

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