Information Quality: The Potential of Data and Analytics to Generate Knowledge


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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, data, analysis and utility that are the main building blocks of data analysis within any domain.
  • Presents a framework for integrating domain knowledge with data analysis.
  • Provides a combination of both methodological and practical aspects of data analysis.
  • Discusses issues surrounding the implementation and integration of InfoQ in both academic programmes and business / industrial projects.
  • Showcases numerous case studies in a variety of application areas such as education, healthcare, official statistics, risk management and marketing surveys.
  • Presents a review of software tools from the InfoQ perspective along with example datasets on an accompanying website.

 This book will be beneficial for researchers in academia and in industry, analysts, consultants, and agencies that collect and analyse data as well as undergraduate and postgraduate courses involving data analysis.

Foreword ix

About the authors xi

Preface xii

Quotes about the book xv

About the companion website xviii


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


6 Education 81

7 Customer surveys 109

8 Healthcare 134

9 Risk management 160

10 Official statistics 181


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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Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.