A Book Review: Executing Data Quality Projects Danette McGilvray’s Ten Steps to Quality Data

Presented by Angela Boyd2013

Agenda for Data Quality Book Review:

• Introduction – Stated Purpose and Uses• Methodology Summary• Information Quality Defined• Framework for Information Quality (FIQ)

• Additional Highlights in Handout to Review

• 10 Steps to Data Quality• Additional Highlights in Handout to Review

• Usage Examples for 10 Step Process• Group Discussion and Questions• Appendixes - DGO Objectives for Data Quality Program

Introduction:

• Danette McGilvray’s stated purpose for the book:• Providing a systematic approach for improving and

creating data and information quality within any business• Uses:

o Information quality-focused projects (i.e.. database assessments, data quality business impact)

o Guidebook for people responsible for daily data qualityo To integrate data quality activities into other projects (i.e..

Enterprise Resource Planning migration, building a data warehouse or application development and implementation)

o As foundation to create or standardize data quality activities into project life cycle

Methodologies:

• Conceptual Framework – Framework for Information Quality (FIQ), including

concepts

• The Ten Steps – Processes for implementing Framework concept

o Data Quality Dimensions• Aspects or features of information used for defining,

measuring, and managing datao Business Impact Techniques

• Qualitative and quantitative techniques for analyzing the impact of data quality issues

Key Concepts and Definitions

Information Quality is the degree to which information and data can be a trusted source for any and/or all required uses. It is having:the right set of correct informationat the right timein the right placefor the right peopleSo that data may be used:to make decisionsto run the businessto serve customersto achieve company goals.

Framework for Information Quality• 7 Components – Framework Needed for Diagnosis, Planning and

Communication (Framework helps with Steps 1-4)

10 Steps to Data Quality

Usage Examples for 10 Step Process:

Discussion Time:

• Group Discussion and Questions• (Appendixes cover the DGO stated objectives

and goals for Data Quality Program)• Data Quality Program Next Steps?

Appendixes

• BJC Data Quality Program Stated Objective (Slide from Presentation to ITWG)

• BJC Data Governance Office 2014 Goals and Objectives (referenced from drafted DGO Charter)

• DGO Priority Projects (referenced from drafted DGO Charter)

First Year 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

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2014 – DGO Goals and Objectives:

Area FocusGovernance Establish program infrastructure, leveraging learning’s and

benchmarking against UPMC, Mayo Clinic, Cedars-Sinai, and other leading healthcare organizations.

Establish communication planInformation Stewardship 

Define data definitions Establish data owners and responsibilities Establish core policies for data flow and data access

Information Documentation Create standardized documentation for dataData Quality Program Establish a means of measuring the quality of defined data

Utilize governance structure to communicate and discuss data quality issues

Technology Procurement Improvement

Establish criteria for data capture and extract capabilities for future technology purchases; help ensure that future technology solutions have the necessary open architecture to support enterprise data access needs.

Collaborate with EHR standardization initiatives as they relate to data access and data flow.

The Data Governance Program will focus on building competencies around five foundational components. In 2014, the competencies will be used to address specific priority projects, both to inform the development of practical applications of the competencies and to achieve meaningful results.

2014 Priority Projects

Priority projects for 2014 (Objectives include Data Quality Program):

• OR Data Analytics• Supply Chain Data Analytics (See Charter appendix for details on these projects).

Key Impact (Program will align with key project initiatives to):

• Ensure standard, accessible data documentation exists for the 2 priority projects • Measure quality of data elements defined as critical by the supply chain

analytics team and other key stakeholders; report quality metrics for these fields at each Data Governance Office meeting.

• Baseline and reduce the number of one-off extracts from the SIS OR system.• Baseline and increase the number of user accesses of the normalized OR stores.• Measure the adoption of data governance policies