Information Quality (eBook)

The Potential of Data and Analytics to Generate Knowledge

Ron S. Kenett, Galit Shmueli (Autoren)

eBook Download: PDF

2016
John Wiley & Sons (Verlag)
978-1-118-89064-6 (ISBN)

Infos

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.

Ron S. Kenett, KPA Ltd. and University of Torino, Turin, Italy
Ron S. Kenett, Chairman and CEO of the KPA Group and KPA Ltd., Research Professor at the University of Turin, Italy, International Professor Associate at the Center for Research in Risk Engineering, NYU-Poly, New York, USA and Visiting Professor at the Faculty of Economics, University of Ljubljana, Slovenia. He has over 25 years of experience in restructuring and improving the competitive position of organizations by integrating statistical methods, process analysis, supporting technologies and modern human resource management systems. Ron Kenett is Editor in Chief of the Wiley Encyclopedia of Statistics in Quality and Reliability, a Fellow of the Royal Statistical Society, Senior Member of the American Society for Quality, Past President of the Israeli Statistical Association and Past President of ENBIS, the European Network for Business and Industrial Statistics and is the 2013 Greenfield Medalist of the Royal Statistical Society.

Galit Shmueli, Indian School of Business, India
Galit Shmueli is SRITNE Chaired Professor of Data Analytics and Associate Professor of Statistics & Information Systems at the Indian School of Business. She is best known for her research and teaching in business analytics, with a focus on statistical and data mining methods for contemporary data and applications in information systems and healthcare. Dr. Shmueli's research has been published in the statistics, management, information systems, and marketing literature. She authors over seventy journal articles, books, textbooks and book chapters, including the popular textbook Data Mining for Business Intelligence and Practical Time Series Forecasting. Dr. Shmueli is an award-winning teacher and speaker on data analytics. She has taught at Carnegie Mellon University, University of Maryland, the Israel Institute of Technology, Statistics.com and the Indian School of Business.

Inhalt

Title Page 5
Copyright Page 6
Contents 9
Foreword 11
About the authors 13
Preface 14
Quotes about the book 17
About the companion website 20
Part I The Information Quality Framework 21
Chapter 1 Introduction to information quality 23
1.1 Introduction 23
1.2 Components of InfoQ 24
1.3 Definition of information quality 27
1.4 Examples from online auction studies 27
1.5 InfoQ and study quality 36
1.6 Summary 36
References 37
Chapter 2 Quality of goal, data quality, and analysis quality 38
2.1 Introduction 38
2.2 Data quality 40
2.3 Analysis quality 44
2.4 Quality of utility 45
2.5 Summary 49
References 49
Chapter 3 Dimensions of information quality and InfoQ assessment 51
3.1 Introduction 51
3.2 The eight dimensions of InfoQ 56
3.3 Assessing InfoQ 64
3.4 Example: InfoQ assessment of online auction experimental data 66
3.5 Summary 70
References 70
Chapter 4 InfoQ at the study design stage 73
4.1 Introduction 73
4.2 Primary versus secondary data and experiments versus observational data 74
4.3 Statistical design of experiments 76
4.4 Clinical trials and experiments with human subjects 80
4.5 Design of observational studies: Survey sampling 82
4.6 Computer experiments (simulations) 83
4.7 Multiobjective studies 84
4.8 Summary 84
References 85
Chapter 5 InfoQ at the postdata collection stage 87
5.1 Introduction 87
5.2 Postdata collection data 88
5.3 Data cleaning and preprocessing 89
5.4 Reweighting and bias adjustment 91
5.5 Meta-analysis 92
5.6 Retrospective experimental design analysis 93
5.7 Models that account for data “loss”: Censoring and truncation 94
5.8 Summary 96
References 97
Part II Applications of InfoQ 99
Chapter 6 Education 101
6.1 Introduction 101
6.2 Test scores in schools 102
6.3 Value-added models for educational assessment 109
6.4 Assessing understanding of concepts 116
6.5 Summary 125
Appendix: MERLO implementation for an introduction to statistics course 125
References 127
Chapter 7 Customer surveys 129
7.1 Introduction 129
7.2 Design of customer surveys 130
7.3 InfoQ components 134
7.4 Models for customer survey data analysis 135
7.5 InfoQ evaluation 142
7.6 Summary 148
Appendix: A posteriori InfoQ improvement for survey nonresponse selection bias 148
References 151
Chapter 8 Healthcare 154
8.1 Introduction 154
8.2 Institute of medicine reports 155
8.3 Sant’Anna di Pisa report on the Tuscany healthcare system 157
8.4 The haemodialysis case study 159
8.5 The Geriatric Medical Center case study 167
8.6 Report of cancer incidence cluster 173
8.7 Summary 178
References 178
Chapter 9 Risk management 180
9.1 Introduction 180
9.2 Financial engineering, risk management, and Taleb’s quadrant 182
9.3 Risk management of OSS 184
9.4 Risk management of a telecommunication system supplier 189
9.5 Risk management in enterprise system implementation 192
9.6 Summary 198
References 199
Chapter 10 Official statistics 201
10.1 Introduction 201
10.2 Information quality and official statistics 202
10.3 Quality standards for official statistics 207
10.4 Standards for customer surveys 214
10.5 Integrating official statistics with administrative data for enhanced InfoQ 216
10.6 Summary 235
References 236
Part III Implementing InfoQ 239
Chapter 11 InfoQ and reproducible research 241
11.1 Introduction 241
11.2 Definitions of reproducibility, repeatability, and replicability 243
11.3 Reproducibility and repeatability in GR& &
11.4 Reproducibility and repeatability in animal behavior studies 245
11.5 Replicability in genome?wide association studies 246
11.6 Reproducibility, repeatability, and replicability: the InfoQ lens 246
11.7 Summary 249
Appendix: Gauge repeatability and reproducibility study design and analysis 249
References 251
Chapter 12 InfoQ in review processes of scientific publications 254
12.1 Introduction 254
12.2 Current guidelines in applied journals 257
12.3 InfoQ guidelines for reviewers 258
12.4 Summary 264
References 270
Chapter 13 Integrating InfoQ into data science analytics programs, research methods courses, and more 272
13.1 Introduction 272
13.2 Experience from InfoQ integrations in existing courses 275
13.3 InfoQ as an integrating theme in analytics programs 279
13.4 Designing a new analytics course (or redesigning an existing course) 280
13.5 A one-day InfoQ workshop 282
13.6 Summary 283
Acknowledgements 284
References 284
Chapter 14 InfoQ support with R 285
14.1 Introduction 285
14.2 Examples of information quality with R 291
14.3 Components and dimensions of InfoQ and R 311
14.4 Summary 312
References 313
Chapter 15 InfoQ support with Minitab 315
15.1 Introduction 315
15.2 Components and dimensions of InfoQ and Minitab 320
15.3 Examples of InfoQ with Minitab 333
15.4 Summary 342
References 343
Chapter 16 InfoQ support with JMP 344
16.1 Introduction 344
16.2 Example 1: Controlling a film deposition process 347
16.3 Example 2: Predicting water quality in the Savannah River Basin 357
16.4 A JMP application to score the InfoQ dimensions 366
16.5 JMP capabilities and InfoQ 368
16.6 Summary 369
References 370
Index 371
EULA 384

Produkt-Details

• Erscheint lt. Verlag: 13.10.2016
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• Schlagworte: Analytic tools • Computer Science • Data Analysis • Data Mining & Knowledge Discovery • Data Mining u. Knowledge Discovery • Dataset • Datenanalyse • Design • Finanz- u. Wirtschaftsstatistik • Framework • General • Given • goal • infoq • Informatik • jmp index • MINITAB • postdata • quality • relevant • stage • statisticians • Statistics • Statistics for Finance, Business & Economics • Statistik • Study • Work
• ISBN-10: 1-118-89064-7 / 1118890647
• ISBN-13: 978-1-118-89064-6 / 9781118890646