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.
