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This text book focuses on what could be the most important challenge for firms to boost long-term productivity and competitiveness: digital strategy. It seeks to provide readers with a solid knowledge of the most relevant issues and concepts, that will be relevant to MBA students in real-world settings. The book discusses theoretical concepts relating to digital strategy, while also using hands-on data analysis in R software to illustrate some fundamental features and pitfalls of working with real-world data. The book starts by clarifying the meaning of relevant concepts (digitization vs digitalization; Machine learning, Artificial Intelligence), presents three leading models of digital transformation, and explains how digitalization has far-reaching implications for how organizations need to be structured. Then the book discusses the skills of a data scientist, and how digital transformation leads to new concerns surrounding ethics. Other themes include data quality, data pre-processing, data visualization, as well as the distinction between prediction and causal inference. Many of these themes are illustrated using R examples, that familiarize the reader with data analysis, using these hands-on experiences to uniquely illustrate some important themes surrounding statistical analysis, and to let readers see for themselves how some popular statistical and data science techniques actually work.
List of contents
Contents.- Preface.- Chapter 1 Introduction and definitions.- Chapter 2 Digital Transformation of Organizations.- Chapter 3 Big Data technologies and Architecture.- Chapter 4 the Data Science process.- Chapter 5 Ethics of Data Science and AI.- Chapter 6 Working with Data.- Chapter 7 The User Experience UEX.- Chapter 8 Data Visualization.- Chapter 9 Descriptions statistical associations.- Chapter 10 Prediction.- Chapter 11 Text as data.- Chapter 12 Causal inference.- Chapter 13 Conclusion.- References.
About the author
Alex Coad is a Professor at Waseda Business School (Waseda University, Tokyo, Japan), and his research focuses mainly in the areas of firm growth, firm performance, entrepreneurship, and innovation policy. Alex has taught an MBA course on “Data Science for Management” at Waseda every year since 2020. Alex has published over 100 articles in international peer-reviewed journals. According to Google Scholar, Alex has over 17'000 citations and an H-index over 50. Alex is an Editor at the journals 'Research Policy' (on the Financial Times Top 50 list of journals for Business Schools) and 'Small Business Economics', and is an Associate Editor at 'Industrial and Corporate Change'. Previously Alex obtained a PhD from Université Paris 1 Panthéon-Sorbonne and the Sant'Anna School, Pisa, Italy, and held academic positions at the Max Planck Institute (Jena, DE), Aalborg University (Denmark), SPRU (Univ. Sussex, UK), and CENTRUM Graduate Business School (Lima, Peru), and also being an Economic Analyst at the European Commission (IRI group, JRC-IPTS, Sevilla). In December 2016, Alex received the 2016 Nelson Prize at University of California Berkeley. In 2024, Alex co-authored (along with Anders Bornhäll, Sven-Olov Daunfeldt, and Alex McKelvie) the Open Access book entitled “Scale-ups and High-Growth Firms: Theory, Definitions, and Measurement”, also published by Springer.
Summary
This text book focuses on what could be the most important challenge for firms to boost long-term productivity and competitiveness: digital strategy. It seeks to provide readers with a solid knowledge of the most relevant issues and concepts, that will be relevant to MBA students in real-world settings. The book discusses theoretical concepts relating to digital strategy, while also using hands-on data analysis in R software to illustrate some fundamental features and pitfalls of working with real-world data. The book starts by clarifying the meaning of relevant concepts (digitization vs digitalization; Machine learning, Artificial Intelligence), presents three leading models of digital transformation, and explains how digitalization has far-reaching implications for how organizations need to be structured. Then the book discusses the skills of a data scientist, and how digital transformation leads to new concerns surrounding ethics. Other themes include data quality, data pre-processing, data visualization, as well as the distinction between prediction and causal inference. Many of these themes are illustrated using R examples, that familiarize the reader with data analysis, using these hands-on experiences to uniquely illustrate some important themes surrounding statistical analysis, and to let readers see for themselves how some popular statistical and data science techniques actually work.
