Data Science and Analytics Strategy
An Emergent Design Approach
1st Edition
Published on 5. April 2023
230 pages
978-1-000-85945-4 (ISBN)
Description
This book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the authors provide actionable advice on data technologies, processes, and governance structures so that readers can make choices that are appropriate to their organisational contexts and requirements.
The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness.
Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls.
The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness.
Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls.
Reviews / Votes
"Not only does the discipline of data science need this book, it holds critical insights and lessons for other facets of enterprise IT too. For the first time, the critically important ideas of Emergent Design practice have been weaved into the hyper-rational world of data science in an accessible and practical way. Kailash Awati and Alex Scriven have written the first Data Science book of its kind - a must read for anyone interested in the governance of data and the complex problems that data and analytics seeks to help solve."-Paul Culmsee, Managing Partner, Seven Sigma Business Solutions
"If you are passionate about the successful implementation of Data Science and Analytics strategies, then put this book on your required reading list. You will learn why and how to define a direction by finding and framing problems that matter to people across the organisation."
-Zanne Van Wyk, Worldwide Education Industry Architect at Microsoft
"Data Science and Analytics Strategy covers a wide range of topics like building analytics and data science capability, building data driven culture in the organization and ethical aspects of practicing data science. It includes advice which are easy and very practical to use in real world scenarios. All in all, a great read for all those who want to setup analytics and data science practises within their organization. "
- Duhita Khadepau Director (Analytics and Data Science), Assignar
"A refreshingly practical approach to success in data science and machine learning. The value of Awati and Scriven's contribution to this field is that emergent design lends to data science a coherence that previously was missed in the chasm between the promise of new tech and the organisational change required to harness it. They've bridged that gap with a highly accessible read, weaving the wealth of their collective experience with the rigour of leading researchers, intellectuals and practitioners into a lively jaunt covering the full vocabulary of concepts for leaders (from deep learning to tech stack to GDPR) that will hold 'aha' moments for even the most seasoned data and analytics professionals and (hopefully!) spawn a new generation of strategic leadership and emergent practice in this space."
-Passiona Cottee, Associate Director, NSW Government.
"Succeeding with Data Science and Analytics is no easy ride, however this book gives the reader a range of ideas and actions to combat the challenges faced by professionals in this field. Finding a path to success requires new approaches and this book provides a refreshing perspective for practitioners to consider as they strive for success."
-Sandra Hogan, Co-founder Amperfii.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Product notice
Reflowable
Illustrations
6 Tables, black and white; 12 Line drawings, black and white; 12 Illustrations, black and white
ISBN-13
978-1-000-85945-4 (9781000859454)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
04/2023
1st Edition
Chapman & Hall/CRC
€158.40
Shipment within 10-20 days
Book
04/2023
1st Edition
Chapman & Hall/CRC
€61.30
Shipment within 10-20 days
Persons
Kailash Awati is a data and sensemaking professional with a deep interest in helping organisations tackle complex problems. He is an Adjunct Fellow in Human-Centred Data Science at the UTS Connected Intelligence Centre and a Data and Insights Manager at a government agency. Over the last decade, he has established data capabilities in diverse organisations using the principles described in this book. In addition to his work in industry, he has developed and taught postgraduate courses in machine learning and decision-making under uncertainty. He is the co-author of two well-regarded books on managing socially complex problems in organisations: The Heretic's Guide to Best Practices and The Heretic's Guide to Management.
Alexander Scriven is a senior data scientist at Atlassian in Sydney, Australia, and has experience across start-ups, government, and enterprise building analytical capacities and executing on data science projects. He greatly enjoys teaching and mentoring and has built and delivered both master's-level university courses in machine learning and deep learning and highly rated courses for online platforms such as Datacamp. His research interests are in applying data science techniques to novel industry challenges. Alex greatly enjoys bridging the gap between cutting-edge technology and business applications.
Alexander Scriven is a senior data scientist at Atlassian in Sydney, Australia, and has experience across start-ups, government, and enterprise building analytical capacities and executing on data science projects. He greatly enjoys teaching and mentoring and has built and delivered both master's-level university courses in machine learning and deep learning and highly rated courses for online platforms such as Datacamp. His research interests are in applying data science techniques to novel industry challenges. Alex greatly enjoys bridging the gap between cutting-edge technology and business applications.
Content
Foreword. Preface. Acknowledgements. Contributors. 1. Introduction. 2. What Is Data Science? 3. The Principles of Emergent Design. 4. Charting a Course. 5. Capability and Culture. 6. Technical Choices. 7. Doing Data Science: From Planning to Production. 8. Doing the Right Thing. 9. Coda. Index.
