Information quality data analytics : the potential of data and analytics to generate knowledge / Ron Kenett, Galit Shmueli.

Format:

Book

Author/Creator:

Kenett, Ron, author.

Shmueli, Galit, 1971- author.

Series:

THEi Wiley ebooks.

THEi Wiley ebooks

Language:

English

Subjects (All):

Data mining.

Mathematical statistics.

Physical Description:

1 online resource (384 p.)

Edition:

1st ed.

Place of Publication:

Chichester, West Sussex, England : Wiley, 2017.

System Details:

Access using campus network via VPN at home (THEi Users Only).

Summary:

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 managementand 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.

Contents:

Intro

Title Page

Copyright Page

Contents

Foreword

About the authors

Preface

Quotes about the book

About the companion website

Part I The Information Quality Framework

Chapter 1 Introduction to information quality

1.1 Introduction

1.2 Components of InfoQ

1.3 Definition of information quality

1.4 Examples from online auction studies

1.5 InfoQ and study quality

1.6 Summary

References

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

2.1 Introduction

2.2 Data quality

2.3 Analysis quality

2.4 Quality of utility

2.5 Summary

Chapter 3 Dimensions of information quality and InfoQ assessment

3.1 Introduction

3.2 The eight dimensions of InfoQ

3.3 Assessing InfoQ

3.4 Example: InfoQ assessment of online auction experimental data

3.5 Summary

Chapter 4 InfoQ at the study design stage

4.1 Introduction

4.2 Primary versus secondary data and experiments versus observational data

4.3 Statistical design of experiments

4.4 Clinical trials and experiments with human subjects

4.5 Design of observational studies: Survey sampling

4.6 Computer experiments (simulations)

4.7 Multiobjective studies

4.8 Summary

Chapter 5 InfoQ at the postdata collection stage

5.1 Introduction

5.2 Postdata collection data

5.3 Data cleaning and preprocessing

5.4 Reweighting and bias adjustment

5.5 Meta-analysis

5.6 Retrospective experimental design analysis

5.7 Models that account for data "loss": Censoring and truncation

5.8 Summary

Part II Applications of InfoQ

Chapter 6 Education

6.1 Introduction

6.2 Test scores in schools

6.3 Value-added models for educational assessment

6.4 Assessing understanding of concepts

6.5 Summary.

Appendix: MERLO implementation for an introduction to statistics course

Chapter 7 Customer surveys

7.1 Introduction

7.2 Design of customer surveys

7.3 InfoQ components

7.4 Models for customer survey data analysis

7.5 InfoQ evaluation

7.6 Summary

Appendix: A posteriori InfoQ improvement for survey nonresponse selection bias

Chapter 8 Healthcare

8.1 Introduction

8.2 Institute of medicine reports

8.3 Sant'Anna di Pisa report on the Tuscany healthcare system

8.4 The haemodialysis case study

8.5 The Geriatric Medical Center case study

8.6 Report of cancer incidence cluster

8.7 Summary

Chapter 9 Risk management

9.1 Introduction

9.2 Financial engineering, risk management, and Taleb's quadrant

9.3 Risk management of OSS

9.4 Risk management of a telecommunication system supplier

9.5 Risk management in enterprise system implementation

9.6 Summary

Chapter 10 Official statistics

10.1 Introduction

10.2 Information quality and official statistics

10.3 Quality standards for official statistics

10.4 Standards for customer surveys

10.5 Integrating official statistics with administrative data for enhanced InfoQ

10.6 Summary

Part III Implementing InfoQ

Chapter 11 InfoQ and reproducible research

11.1 Introduction

11.2 Definitions of reproducibility, repeatability, and replicability

11.3 Reproducibility and repeatability in GR&amp

&amp

R

11.4 Reproducibility and repeatability in animal behavior studies

11.5 Replicability in genome‐wide association studies

11.6 Reproducibility, repeatability, and replicability: the InfoQ lens

11.7 Summary

Appendix: Gauge repeatability and reproducibility study design and analysis

References.

Chapter 12 InfoQ in review processes of scientific publications

12.1 Introduction

12.2 Current guidelines in applied journals

12.3 InfoQ guidelines for reviewers

12.4 Summary

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

13.1 Introduction

13.2 Experience from InfoQ integrations in existing courses

13.3 InfoQ as an integrating theme in analytics programs

13.4 Designing a new analytics course (or redesigning an existing course)

13.5 A one-day InfoQ workshop

13.6 Summary

Acknowledgements

Chapter 14 InfoQ support with R

14.1 Introduction

14.2 Examples of information quality with R

14.3 Components and dimensions of InfoQ and R

14.4 Summary

Chapter 15 InfoQ support with Minitab

15.1 Introduction

15.2 Components and dimensions of InfoQ and Minitab

15.3 Examples of InfoQ with Minitab

15.4 Summary

Chapter 16 InfoQ support with JMP

16.1 Introduction

16.2 Example 1: Controlling a film deposition process

16.3 Example 2: Predicting water quality in the Savannah River Basin

16.4 A JMP application to score the InfoQ dimensions

16.5 JMP capabilities and InfoQ

16.6 Summary

Index

EULA.

Notes:

Description based upon print version of record.

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9781118890653

1118890655

9781118890646

1118890647

9781118890622

1118890620

OCLC:

961063859