Data Governance Community of Practice – 6 Sessions

January 20, 5:00pm, EST - 6:00pm, EST
February 17, 5:00pm, EST - 6:00pm, EST
March 17, 5:00pm, EDT - 6:00pm, EDT
April 21, 5:00pm, EDT - 6:00pm, EDT
May 19, 5:00pm, EDT - 6:00pm, EDT
June 16, 5:00pm, EDT - 6:00pm, EDT


One time Registration Price for all of the 6 Series


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6 CPHIMS Credit hours will be given at the conclusion of the series.




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Announcing the NEHIMSS Data Governance Community of Practice!

What is it?

A Community of Practice is defined as a group of people who share a passion about a subject, who learn to do it better by interacting regularly.  NEHIMSS is establishing a CoP on Data Governance, its best practices, and strategies to get it implemented or expanded at your organization

Who is it for?

The NEHIMSS DG CoP is for anyone who has a passion for the consistency, usability, accuracy, and quality of data, including:

  • Data Scientists
  • Data and Quality Analysts
  • Clinical Informaticists
  • Data Architects
  • Management & Clinical Leaders interested in getting better data
  • Interoperability & Integration Engineers

What are the goals of the CoP?

The main goal of the CoP is to share experiences with Data Governance, with the purpose of increasing the quality and usability of data in the Health Care and Life Sciences:

  • How does DG work to manage the data assets of your organization?
  • How can you measure the effect of not managing data on your organization?
  • How can you justify a DG program, or a new DG activity, in your organization

How will it be structured?

The CoP will initially meet in six sessions, starting January 20, on the third Wednesday of the month, from 5:00 PM to 6:00 PM Eastern time.

Each session will be structured similarly:

  • Introduction of the DG activity of the month
  • Value of the activity on your business
  • Strategies for initiating the activity in your organization
  • “War stories” from participants who are going through the activity already
Additional Materials: 



Ron M’Sadoques

Director of Enterprise Data Intelligence, Hartford Healthcare

As the Director of Enterprise Data Intelligence for Hartford Healthcare, Ron M’Sadoques is responsible for HHC’s Data Management program, including the Master Data Management system, and HHC’s Data Lake and Epic Caboodle Data Warehouse efforts.  Ron also is responsible for the ETL functions and procedures.


Ron started his career managing software developers on DEC VAX systems.  Over the next 35 years, he managed efforts on CRM and Office Automation systems.  Prior to his current role, Ron managed ERP support and development at HHC.




Session 1: Data Governance Overview

    • The difference between Data Management & Data Governance
      • Hint: Data Management deals with the structure and performance of the data.  Data Governance deals with accessibility and quality of the content of the data
    • What is data, and why should it be governed
      • An asset that is non-consumable, and retains value
    • A PROGRAM, not a PROJECT
      • You can stop it when you stop your HR program or Supply Chain program
      • Justify it with adverse events
    • If data is an asset, then the asset needs to be managed and maintained
      • Use the “Truck Fleet Analogy”
    • Who are the actors?
      • Data Owners, Data Stewards, Application Stewards, Analytic Stewards
    • What are the models?
      • Depends on the goal.
        • If you’re trying to keep your warehouse clean, consider a central model
        • If you want better data throughout the org, consider a distributed model
    • Introduce the 6 DG activities

Session 2: Data Domain Governance

    • What is a domain?
      • Many disciplines use the term “Domain”.  What does it mean to DG?
    • Why is Data Domain the first activity?
    • The Domain selection process
      • Find an easy one
      • Find a long-term one
      • Find others based upon enterprise strategies and important KPI’s
    • Find the Data Owners
      • This might be tricky.  A Data Owner needs respect and authority across the organization.
    • Find the Stewards
      • This might be easy.  They already exist in your organization, and are recognized in that capacity.  Their role is just not formalized
      • We’re asking people who spend X hours per week figuring out what happened to spend <X hours per week making sure nothing happens

Session 3: Data policy and Strategy

    • What is a Data Strategy?
      • A set of guidelines and patterns for decision making
    • Your Strategy needs to be flexible to changes in business need, and be able to take advantage of new technology
    • Data Strategy must follow Analytics Strategy
    • Access policies must follow
      • Balance the HIPAA “minimum needed” with the organization’s need for insight, and opportunity for monetization

Session 4: Metadata Management

    • Easy definition:  “The stuff you need to know to make the best use of your data”
    • The most esoteric concept to sell to Management
    • Critical to democratizing data
      • It’s the key to taking the “tribal knowledge” in your Analytics areas, and making it available to all
      • Highly participatory
    • You will get resistance from people who believe they are the only ones who know enough to access the data.
    • The three levels of Metadata
      • Dictionary, Catalog, Glossary
    • The magic of Data Lineage
      • I like the term “data pedigree” – do you really want to make decisions from “mongrel data”

Session 5: Master and Reference Data Management

    • The second most esoteric concept
    • Initially high investment, subsequent high payoff
    • Can greatly shorten time-to-insight
    • What is Master Data?
      • The 5 P’s – Patients (or People), Providers, Places, Payers, Procedures
      • Contrast Master Data in Healthcare versus other industries
    • What is Reference Data?
      • The obvious – ICD, CPT, SNOMED
      • The not-so-obvious – internal codes – encounter types, financial classes,

Session 6: Data Quality Management

    • Initially low investment; subsequent high payoff
    • Find the adverse events, and springboard off of those
    • Data profiling – where it all starts
      • How many duplicate keys do you have?  How many different gender codes do you have?
    • Move to more advanced quality analyses as you know more about the data
    • The types of data quality issues
      • Validity, Completeness, Accuracy