Principles of Strategic Data Science
Principles of Strategic Data Science
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Principles of Strategic Data Science

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Completed on 2019-03-08

About the Book

Principles of Strategic Data Science describes a framework to create value from data to help organisations meeting their objectives. This book is written for managers interested in strategic data science and data scientists interested in how to improve their contribution to their organisation.

The first chapter defines data science as a strategic and systematic approach to business analysis. Data science happens in the confluence of domain knowledge and mathematics, enabled by computer science.

Chapter two presents a model for good data science as being useful, sound and aesthetic. Useful data science adds value to an organisation and soundness relates to a systematic approach to analysis. Data science needs to be aesthetic to ensure that users understand the results.

The penultimate chapter discusses a strategic framework for maximising the value of data assets through an evolutionary approach from data management to prescriptive analysis. This evolution starts with good data management and continues through to using algorithms to make business decisions.

The final chapter of this book discusses various aspects of developing a data-driven organisation. Data science is not about lone geniuses but is undertaken by teams. Principles of Strategic Data Science closes with reflections on the limitations and ethics of data science.

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About the Author

Peter Prevos
Peter Prevos

Dr Peter Prevos is a civil engineer and social scientist who also dabbles in theatrical magic. Peter has almost three decades of experience as a water engineer and manager, working in Europe, Africa, Asia and Australia. He has worked on marine engineering, drinking water an sewage treatment projects. Throughout his career analysing data has been a central theme.

He also has a PhD in marketing and is the author of Customer Experience Management for Water Utilities. In his work, he aims to combine the social sciences with engineering to create value for customers. Peter occasionally lectures marketing for MBA students.

He is currently responsible for developing and implementing the data science strategy for a water utility in regional Australia. The objective of this strategy is to create value from data through useful, sound and aesthetic data science. His mission is to breed unicorn data scientists by motivating other water professionals to ditch their spreadsheets and learn how to write code.

Reader Testimonials

Michael Thomas
Michael Thomas

If you want to know about data science, Peter Prevos is your man. Not ‘just’ an engineer, Peter is equally conversant across the humanities and social sciences. His unique perspective is delivered with passion in his latest book; weaving a wonderful account of both the ‘science’ and the ‘art’ behind big data. A must read for those seeking a far richer and contextualised account of data-science.

Keshvinder Singh
Keshvinder Singh

For decision-makers

This is a great book for high level and conceptual discussion on data science. Good for decision-makers that rely on data for deciding company growth. How they can use the data and being vigilant with it.

Table of Contents

  • Preface
    • Preface to second edition
  • Acknowledgements
  • 1. What is Data Science?
    • 1.1 Data-Driven Organisation
    • 1.2 The Data Revolution
    • 1.3 The Elements of Data Science
    • 1.4 The Purpose of Data Science
  • 2. Good Data Science
    • 2.1 Data Science Trivium
    • 2.2 Useful Data Science
    • 2.3 Sound Data Science
    • 2.4 Aesthetic Data Science
    • 2.5 Best-Practice Data Science
  • 3. Strategic Data Science
    • 3.1 The Data Science Continuum
    • 3.2 Collect data
    • 3.3 Descriptive Statistics
    • 3.4 Diagnostics
    • 3.5 Predicting the Future
    • 3.6 Prescribe Action
    • 3.7 Towards a Data-Driven Organisation
  • 4. The Data-Driven Organisation
    • 4.1 People
    • 4.2 Systems
    • 4.3 Process
    • 4.4 The Limitations of Data Science
  • 5. References
  • Notes

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