1. DataGovernanceConcepts
DGIQConference- June2013
Presented By Angela Boyd
for
Lunch & Learn - November 13th, 2013

2. Today’s Agenda:
 What is Data Governance?
 Example of Data Governance in action
 Issues that need Data Governance
 Industry definitions
 Who did we meet?
 Industry Leaders & Companies applying DG principles
 What are the next steps?
 Formation of Data Governance Office and Work Groups
 Review and Time for Questions & Answers

3. Introduction to Data Governance:
 What is Data Governance?
 Familiar Example as a Demonstration
– Food Labels
– Same Unit of Measurement
– Same Attributes

4. ExamplesthatRequireDataGovernance(DG):
Issue Business Impact How DG can Help
Invalid statistics sent to government
• Hospitals changed dept. numbers
used to calculate metrics
• Inaccurate regulatory reporting
• Time spent reconciling
• Increased expense (vendor charged for
changes)
• Establish clear data owners
• Build processes to ensure data
accuracy & increase data quality
Incorrect and Incomplete OB data in legal
medical record
• Potential for incorrect or redundant
patient care
• Inaccurate regulatory reporting
• Time spent reconciling
• Establish clear data owners
• Standardize data flow processes
• Profile data as part of a data quality
program.
30+ extracts with overlapping EHR data
• Team creates new extract for each
data request
• Potential for incorrect assessment of
patient care
• Increased data security
• Increased support costs
Reduced performance of operational
database
• Establish clear data owners
• Identify source of truth and owners
for data
• Standardize access to data, which
includes extracts & enterprise data
stores

5. Data Governance Defined:
 Governance is not command/control…it is about raising
awareness and presenting issues for cross-functional
assessment and decisions.
Michael Atkin, Managing Director, Enterprise Data Management
 Data Governance is a practical business solution to data and
information management challenges within an
organization…it is about Information Asset Management
that derives business performance.
James C. Orr – Author of Data Governance for the Executive

6. Change in Data Governance Focus:
TODAY:
Department specific/Siloed within
functional areas and departments
TOMORROW & FUTURE:
An Enterprise Focus/Organizational
coordinated approach

7. Data Management Policy Example:
Clinical Metadata Management
Policy Statement : All data flowing into and out of Enterprise Clinical
Operational Data Engine (ECODE) should be documented using the
approved templates defined by the Data Management Work Group.
Reason for Policy: To ensure transparency into the data lifecycle and
consistency in how the data is interpreted.
Policy Details and Related Documents: The approved template will include,
but is not limited to all business rules, transformations, message
specifications, source definitions, and target definitions. The documentation
should be stored in a central repository, so that it is easily accessible by all
vested parties. All necessary documentation should be checked into the
central repository prior to release to production and shall be subject to
audit. Documentation shall be reviewed on an annual or biennial basis.

8. Initial Data Governance Objectives:
Components Outcomes
• Establish a Data Governance Office
• Establish the Executive Data Governance Collaborative
• Form working teams
Governance
• Define first set of key data elements across each major
functional area
• Establish enterprise data architecture and core policies
for the architecture including data flow and access
Information
Stewardship
• Create standardized documentation for first set of key
data elements
Information
Documentation
• Establish data quality monitors (reports) for first set of
key data elementsData Quality Program
• Establish criteria for data capture and extract capabilities
for future technology purchases
• Collaborate with EHR Standardization Initiatives
Technology
Procurement
Improvement

9. DGIQ – June 2013: PresentersandAttendees
PeterAiken– DataManagement&Governance
30 Years Experience:
Associate Professor of IS at Virginia
Commonwealth University
President of International Data
Management Association
Authored 8 books
Founding Director of Data Blueprint
(consulting firm)

10. Where is Data Governance Needed?

11. DGIQ – June 2013: PresentersandAttendees
DavidLoshin– IT/DataManagement/Quality
30 Years Experience:
President, Knowledge Integrity, Inc,
(consulting firm)
Authored 10 books
Featured columnist at b-eye-
network.com, tdan.com, information-
management.com

12. Beginning Data Governance Principles:
Critical Data Elements
Identify enterprise metadata in use across the organization
Clarify unambiguous definitions, formats, and semantics
Facilitate agreement to those definitions and semantics from
all stakeholders
Absorb replicated reference sets into a single managed
repository

13. How to Apply Data Quality Knowledge:

14. Year 1: Key Impact of Our DG
Program to Improve Measures
Projects
Operating Room Data
Integration
Supply Chain Analytics Others
Major Objective Integrate OR data from
all hospitals
Integrate separate Supply
Chain ‘test lab’ (sandbox)
warehouse with the EDW
Proactively limit and/or
course correct data
governance issues
Primary Data
Governance
Achievement
Standardize OR data
elements. e.g., OR
procedure codes; body
site; and implant
definitions.
Implement quality
program and security
policies/procedures for
sharing data
Successfully change
behaviors, decisions,
and technologies
Timeframe Q12014 Q42014 Immediate & Ongoing

15. Review of Data Governance:
Simple example – Food labels
Data Management issue to solve:
 Standardizing measure specification and documentation
 Standardizing documentation of data flows
 Beginning a Data Quality Program
 Formation of Data Governance Office
 Leadership support
 Enterprise involvement
 Everyone will be involved at some point
 Specific knowledge needed to solve problems
 Question and Answer Time