13355nam a22005533i 4500 EBC1106491 MiAaPQ 20220908154026.0 m o d | cr cnu|||||||| 211129s2013 xx o ||||0 eng d 9780123977540 (electronic bk.) 9780123970336 (MiAaPQ)EBC1106491 (Au-PeEL)EBL1106491 (CaPaEBR)ebr10642635 (CaONFJC)MIL424568 (OCoLC)823722256 MiAaPQ eng rda pn MiAaPQ MiAaPQ QA76.9.D35 S43 2013 005.7068 Sebastian-Coleman, Laura. author Measuring Data Quality for Ongoing Improvement : A Data Quality Assessment Framework / Laura Sebastian-Coleman. San Francisco : Elsevier Science & Technology, 2013. ©2013. 1 online resource (404 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier The Morgan Kaufmann Series on Business Intelligence Ser. Front Cover -- Measuring Data Quality for Ongoing Improvement -- Copyright Page -- Contents -- 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 -- Definitions of Data Quality -- Expectations about Data -- Risks to Data -- The Criticality of Metadata and Explicit Knowledge -- The Business/Information Technology Divide -- Data Quality Strategy -- DQAF: the Data Quality Assessment Framework -- Overview of Measuring Data Quality for Ongoing Improvement -- Section One: Concepts and Definitions -- Section Two: DQAF Overview -- Section Three: Data Assessment Scenarios -- Section Four: Applying the DQAF to Data Requirements -- Section Five: Data Quality Strategy -- Section Six: the DQAF in Depth -- Intended Audience -- What Measuring Data Quality for Ongoing Improvement Does Not Do -- Why I Wrote Measuring Data Quality for Ongoing Improvement -- 1: Concepts and Definitions -- 1 Data -- Purpose -- Data -- Data as Representation -- The Implications of Data's Semiotic Function -- Semiotics and Data Quality -- Data as Facts -- Data as a Product -- Data as Input to Analyses -- Data and Expectations -- Information -- Concluding Thoughts -- 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 -- 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 -- 4 Data Quality and Measurement -- Purpose -- Data Quality -- Data Quality Dimensions -- Measurement -- Measurement as Data -- Data Quality Measurement and the Business/IT Divide -- Characteristics of Effective Measurements -- Measurements must be Comprehensible and Interpretable -- Measurements must be Reproducible -- Measurements must be Purposeful -- Data Quality Assessment -- Data Quality Dimensions, DQAF Measurement Types, Specific Data Quality Metrics -- Data Profiling -- Data Quality Issues and Data Issue Management -- Reasonability Checks -- Data Quality Thresholds -- Process Controls -- In-line Data Quality Measurement and Monitoring -- Concluding Thoughts -- 2: DQAF Concepts and Measurement Types -- 5 DQAF Concepts -- Purpose -- The Problem the DQAF Addresses -- Data Quality Expectations and Data Management -- The Scope of the DQAF -- DQAF Quality Dimensions -- Completeness -- Timeliness -- Validity -- Consistency -- Integrity -- The Question of Accuracy -- Defining DQAF Measurement Types -- Metadata Requirements -- Objects of Measurement and Assessment Categories -- Functions in Measurement: Collect, Calculate, Compare -- Concluding Thoughts -- 6 DQAF Measurement Types -- Purpose -- Consistency of the Data Model -- Ensuring the Correct Receipt of Data for Processing -- Inspecting the Condition of Data upon Receipt -- Assessing the Results of Data Processing -- Assessing the Validity of Data Content -- Assessing the Consistency of Data Content -- Comments on the Placement of In-line Measurements -- Periodic Measurement of Cross-table Content Integrity -- Assessing Overall Database Content. Assessing Controls and Measurements -- The Measurement Types: Consolidated Listing -- Concluding Thoughts -- 3: Data Assessment Scenarios -- Purpose -- Assessment Scenarios -- Metadata: Knowledge before Assessment -- 7 Initial Data Assessment -- Purpose -- Initial Assessment -- Input to Initial Assessments -- Data Expectations -- Data Profiling -- Column Property Profiling -- High-Frequency Values -- Low-Frequency Values -- Date Data -- Observations about Column Population -- The Presence of Invalid Values -- The Absence of Valid Values -- Columns with the Cardinality of One -- Columns with High Cardinality Where Low Cardinality is Expected -- Columns with Low Cardinality Where High Cardinality is Expected -- Columns with Less-Than-Complete Population -- Multiple Data Format Patterns in One Column -- Structure Profiling -- Incomplete Referential Relationships -- Missing or Unexpected Dependency Relationships and Rules -- Differences in Granularity and Precision -- Relationship Cardinality Different from Expectations -- Profiling an Existing Data Asset -- From Profiling to Assessment -- Deliverables from Initial Assessment -- Concluding Thoughts -- 8 Assessment in Data Quality Improvement Projects -- Purpose -- Data Quality Improvement Efforts -- Measurement in Improvement Projects -- 9 Ongoing Measurement -- Purpose -- The Case for Ongoing Measurement -- Example: Health Care Data -- Inputs for Ongoing Measurement -- Criticality and Risk -- Automation -- Controls -- Periodic Measurement -- Deliverables from Ongoing Measurement -- In-Line versus Periodic Measurement -- Concluding Thoughts -- 4: Applying the DQAF to Data Requirements -- 10 Requirements, Risk, Criticality -- Purpose -- Business Requirements -- Data Quality Requirements and Expected Data Characteristics -- Data Quality Requirements and Risks to Data -- Factors Influencing Data Criticality. Specifying Data Quality Metrics -- Subscriber Birth Date Example -- Analysis Summary -- Additional Examples -- Concluding Thoughts -- 11 Asking Questions -- Purpose -- Asking Questions -- Understanding the Project -- Learning about Source Systems -- Source Goals and Source Data Consumers -- Source Data Processing -- Individual Data Attributes and Rules -- Your Data Consumers' Requirements -- The Condition of the Data -- The Data Model, Transformation Rules, and System Design -- Measurement Specification Process -- Concluding Thoughts -- 5: A Strategic Approach to Data Quality -- 12 Data Quality Strategy -- Purpose -- The Concept of Strategy -- Systems Strategy, Data Strategy, and Data Quality Strategy -- Data Quality Strategy and Data Governance -- Decision Points in the Information Life Cycle -- General Considerations for Data Quality Strategy -- Concluding Thoughts -- 13 Directives for Data Quality Strategy -- Purpose -- Directive 1: Obtain Management Commitment to Data Quality -- Assessing Management Commitment -- Directive 2: Treat Data as an Asset -- Characterizing an Organization's Data Assets -- Directive 3: Apply Resources to Focus on Quality -- Assessing Readiness to Commission a Data Quality Team -- Directive 4: Build Explicit Knowledge of Data -- Assessing the Condition of Explicit Knowledge and Knowledge Sharing -- Directive 5: Treat Data as a Product of Processes that can be Measured and Improved -- Assessing Organizational Understanding of Data as a Product -- Directive 6: Recognize Quality is Defined by Data Consumers -- Assessing How Data Consumers Define Data Quality -- Directive 7: Address the Root Causes of Data Problems -- Assessing Organizational Ability to Address Root Causes -- Directive 8: Measure Data Quality, Monitor Critical Data -- Assessing Organizational Readiness for Ongoing Measurement and Monitoring. Directive 9: Hold Data Producers Accountable for the Quality of their Data (and Knowledge about that Data) -- Assessing Options for Accountability -- Directive 10: Provide Data Consumers with the Knowledge they Require for Data Use -- Directive 11: Data Needs and Uses will Evolve-Plan for Evolution -- Developing a Plan for Evolution -- Directive 12: Data Quality Goes beyond the Data-Build a Culture Focused on Quality -- Building a Culture Focused on Data Quality -- Concluding Thoughts: Using the Current State Assessment -- 6: The DQAF in Depth -- Functions for Measurement: Collect, Calculate, Compare -- Features of the DQAF Measurement Logical Data Model -- Facets of the DQAF Measurement Types -- 14 Functions of Measurement: Collection, Calculation, Comparison -- Purpose -- Functions in Measurement: Collect, Calculate, Compare -- Collecting Raw Measurement Data -- Calculating Measurement Data -- Comparing Measurements to Past History -- Statistics -- Measures of Central Tendency -- Measures of Variability -- The Control Chart: A Primary Tool for Statistical Process Control -- The DQAF and Statistical Process Control -- Concluding Thoughts -- 15 Features of the DQAF Measurement Logical Model -- Purpose -- Metric Definition and Measurement Result Tables -- Common Key Fields -- Optional Fields -- Denominator Fields -- Automated Thresholds -- Manual Thresholds -- Emergency Thresholds -- Manual or Emergency Thresholds and Results Tables -- Additional System Requirements -- Support Requirements -- Concluding Thoughts -- 16 Facets of the DQAF Measurement Types -- Purpose -- Facets of the DQAF -- Organization of the Chapter -- Measurement Type #1: Dataset Completeness-Sufficiency of Metadata and Reference Data -- Definition -- Business Concerns -- Measurement Methodology -- Programming -- Support Processes and Skills. Measurement Type #2: Consistent Formatting in One Field. 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. 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2021. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Data structures (Computer science). Online databases 145136 Quality control. Electronic books. Print version: Sebastian-Coleman, Laura Measuring Data Quality for Ongoing Improvement San Francisco : Elsevier Science & Technology,c2013 9780123970336 ProQuest (Firm) The Morgan Kaufmann Series on Business Intelligence Ser. https://login.ezproxy.pucpr.edu/login?url=https://ebookcentral.proquest.com/lib/pucpr-ebooks/detail.action?docID=1106491 Acceso Remoto https://ebookcentral.proquest.com/lib/pucpr-ebooks/detail.action?docID=1106491 Acceso en el Campus PUCPR lcc 7 296899 296899 0 0 ddc 0 0 10 10 45 2021-12-07 0 005.7068 EBC1106491 2021-12-07 00:00:00 2021-12-07 7 0 0 lcc 0 0 12 12 45 2022-09-08 0 QA76.9 .D35 S43 2013 50000003289187 2022-09-08 00:00:00 Ebook c. 1 2022-09-08 7