DGIQ 2013 Learned and Applied Concepts

Data Governance ConceptsDGIQ Conference - June 2013Presented By Angela Boyd for Lunch & Learn - November 13th, 2013

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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

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Introduction to Data Governance:

What is Data Governance? Familiar Example as a Demonstration

– Food Labels– Same Unit of Measurement– Same Attributes

Examples that Require Data Governance (DG):Issue Business Impact How DG can HelpInvalid 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

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

Change in Data Governance Focus:TODAY: Department specific/Siloed

within functional areas and departments

TOMORROW & FUTURE: An Enterprise

Focus/Organizational coordinated approach

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. 

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

DGIQ – June 2013: Presenters and AttendeesPeter Aiken – Data Management & 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)

Where is Data Governance Needed?

DGIQ – June 2013: Presenters and AttendeesDavid Loshin – IT/ Data Management/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

Beginning Data Governance Principles:

Critical Data ElementsIdentify enterprise metadata in use across the organizationClarify unambiguous definitions, formats, and semanticsFacilitate agreement to those definitions and semantics from

all stakeholdersAbsorb replicated reference sets into a single managed

repository

How to Apply Data Quality Knowledge:

Year 1: Key Impact of Our DG Program to Improve Measures

ProjectsOperating 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

Review of Data Governance:

Simple example – Food labelsData 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