Description

Book Synopsis

Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis

Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance at many stages of the data analytics journey, from the pre-data collection stage to the post-data collection and post-analysis stages. It is also critical to various stakeholders: data collection agencies, analysts, data scientists, and management.

This book:

  • Explains how to integrate the notions of goal, dat

    Table of Contents

    Foreword ix

    About the authors xi

    Preface xii

    Quotes about the book xv

    About the companion website xviii

    PART I THE INFORMATION QUALITY FRAMEWORK 1

    1 Introduction to information quality 3

    2 Quality of goal, data quality, and analysis quality 18

    3 Dimensions of information quality and InfoQ assessment 31

    4 InfoQ at the study design stage 53

    5 InfoQ at the postdata collection stage 67

    PART II APPLICATIONS OF InfoQ 79

    6 Education 81

    7 Customer surveys 109

    8 Healthcare 134

    9 Risk management 160

    10 Official statistics 181

    PART III IMPLEMENTING InfoQ 219

    11 InfoQ and reproducible research 221

    12 InfoQ in review processes of scientific publications 234

    13 Integrating InfoQ into data science analytics programs, research methods courses, and more 252

    14 InfoQ support with R 265

    15 InfoQ support with Minitab 295

    16 InfoQ support with JMP 324

    Index 351

Information Quality

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    A Hardback by Ron S. Kenett, Galit Shmueli

    £73.10

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      Book details

      Publisher John Wiley & Sons Inc
      Published 9 December 2016
      ISBN-13 9781118874448
      978-1118874448
      ISBN-10 1118874447
      Also in:
      Data capture and analysis

      Description

      Book Synopsis

      Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis

      Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance at many stages of the data analytics journey, from the pre-data collection stage to the post-data collection and post-analysis stages. It is also critical to various stakeholders: data collection agencies, analysts, data scientists, and management.

      This book:

      • Explains how to integrate the notions of goal, dat

        Table of Contents

        Foreword ix

        About the authors xi

        Preface xii

        Quotes about the book xv

        About the companion website xviii

        PART I THE INFORMATION QUALITY FRAMEWORK 1

        1 Introduction to information quality 3

        2 Quality of goal, data quality, and analysis quality 18

        3 Dimensions of information quality and InfoQ assessment 31

        4 InfoQ at the study design stage 53

        5 InfoQ at the postdata collection stage 67

        PART II APPLICATIONS OF InfoQ 79

        6 Education 81

        7 Customer surveys 109

        8 Healthcare 134

        9 Risk management 160

        10 Official statistics 181

        PART III IMPLEMENTING InfoQ 219

        11 InfoQ and reproducible research 221

        12 InfoQ in review processes of scientific publications 234

        13 Integrating InfoQ into data science analytics programs, research methods courses, and more 252

        14 InfoQ support with R 265

        15 InfoQ support with Minitab 295

        16 InfoQ support with JMP 324

        Index 351

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