Description

The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You’ll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You’ll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies.

Key Features

    • Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challenges
    • Enables discussions between business and IT with a non-technical vocabulary for data quality measurement
    • Describes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation

    Readership

    Data quality engineers, managers and analysts, application program managers and developers, data stewards, data managers and analysts, compliance analysts, Business intelligence professionals, Database designers and administrators, Business and IT managers

    Table of Contents

    Dedication

    Acknowledgments

    Foreword

    Author Biography

    Introduction: Measuring Data Quality for Ongoing Improvement

    Data Quality Measurement: the Problem we are Trying to Solve

    Recurring Challenges in the Context of Data Quality

    DQAF: the Data Quality Assessment Framework

    Overview of Measuring Data Quality for Ongoing Improvement

    Intended Audience

    What Measuring Data Quality for Ongoing Improvement Does Not Do

    Why I Wrote Measuring Data Quality for Ongoing Improvement

    Section 1. Concepts and Definitions

    Chapter 1. Data

    Purpose

    Data

    Data as Representation

    Data as Facts

    Data as a Product

    Data as Input to Analyses

    Data and Expectations

    Information

    Concluding Thoughts

    Chapter 2. Data, People, and Systems

    Purpose

    Enterprise or Organization

    IT and the Business

    Data Producers

    Data Consumers

    Data Brokers

    Data Stewards and Data Stewardship

    Data Owners

    Data Ownership and Data Governance

    IT, the Business, and Data Owners, Redux

    Data Quality Program Team

    Stakeholder

    Systems and System Design

    Concluding Thoughts

    Chapter 3. Data Management, Models, and Metadata

    Purpose

    Data Management

    Database, Data Warehouse, Data Asset, Dataset

    Source System, Target System, System of Record

    Data Models

    Types of Data Models

    Physical Characteristics of Data

    Metadata

    Metadata as Explicit Knowledge

    Data Chain and Information Life Cycle

    Data Lineage and Data Provenance

    Concluding Thoughts

    Chapter 4. Data Quality and Measurement

    Purpose

    Data Quality

    Data Quality Dimensions

    Measurement

    Measurement as Data

    Data Quality Measurement and the Business/IT Div

    Details

    No. of pages:
    376
    Language:
    English
    Copyright:
    © 2013
    Published:
    Imprint:
    Morgan Kaufmann
    Print ISBN:
    9780123970336
    Electronic ISBN:
    9780123977540

    About the editor

    Laura Sebastian-Coleman

    Laura Sebastian-Coleman, a data quality architect at Optum Insight, has worked on data quality in large health care data warehouses since 2003. Optum Insight specializes in improving the performance of the health system by providing analytics, technology and consulting services. Laura has implemented data quality metrics and reporting, launched and facilitated Optum Insight’s Data Quality Community, contributed to data consumer training programs, and has led efforts to establish data standards and manage metadata. In 2009, she led a group of analysts from Optum and UnitedHealth Group in developing the original Data Quality Assessment Framework (DQAF) which is the basis for Measuring Data Quality for Ongoing Improvement. An active professional, Laura has delivered papers at MIT’s Information Quality Conferences and at conferences sponsored by the International Association for Information and Data Quality (IAIDQ) and the Data Governance Organization (DGO). From 2009-2010, she served as IAIDQ’s Director of Member Services. Before joining Optum Insight, she spent eight years in internal communications and information technology roles in the commercial insurance industry. She holds the IQCP (Information Quality Certified Professional) designation from IAIDQ, a Certificate in Information Quality from MIT, a B.A. in English and History from Franklin & Marshall College, and Ph.D. in English Literature from the University of Rochester (NY).

    Affiliations and Expertise

    Laura Sebastian-Coleman, a data quality architect at Optum Insight.

    Reviews

    "This book provides a very well-structured introduction to the fundamental issue of data quality, making it a very useful tool for managers, practitioners, analysts, software developers, and systems engineers. It also helps explain what data quality management entails and provides practical approaches aimed at actual implementation. I positively recommend reading it…"--ComputingReviews.com, January 30, 2014
    "The framework she describes is a set of 48 generic measurement types based on five dimensions of data quality: completeness, timeliness, validity, consistency, and integrity. The material is for people who are charged with improving, monitoring, or ensuring data quality."--Reference and Research Book News, August 2013
    "If you are intent on improving the quality of the data at your organization you would do well to read Measuring Data Quality for Ongoing Improvement and adopt the DQAF offered up in this fine book."--Data and Technology Today blog, July 2, 2013