Data Science and Analytics Strategy: An Emergent Design Approach

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.

本書描述了如何使用「Emergent Design」在組織中建立數據科學和分析能力，這是一種演化式方法，可以增加成功結果的機會，同時最大限度地減少前期投資。根據作者和一些數據領導者的經驗，本書提供了關於數據技術、流程和治理結構的可行建議，讓讀者能夠根據組織的背景和需求做出適當的選擇。

本書結合了組織變革和數據科學流程的學術研究和經驗豐富的數據分析領導者的真實故事，重點關注建立數據能力的實際方面。除了詳細介紹能力、文化和技術選擇外，本書的一個獨特特點是對數據倫理和算法公平性等新興問題的處理。

《數據科學和分析策略：一種演化設計方法》是為那些希望在組織中建立數據科學和分析能力的專業人士以及希望擴展他們在數據領域知識並推進職業發展的人士而寫的。本指南提供了深入洞察數據科學和商業之間的交集，幫助專業人士了解如何幫助他們的組織從數據中獲益。最重要的是，讀者將學習如何避免最常見的陷阱，以適合目的的方式建立數據科學能力。

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.

Kailash Awati是一位數據和感知專業人士，對幫助組織應對複雜問題有濃厚興趣。他是悉尼科技大學聯繫智能中心的人本數據科學兼職研究員，也是一家政府機構的數據和洞察經理。在過去十年中，他使用本書中描述的原則在不同組織中建立了數據能力。除了在行業中的工作外，他還開發並教授了關於機器學習和不確定性決策的研究生課程。他是兩本關於組織中管理社會複雜問題的知名書籍的合著者:《異端者的最佳實踐指南》和《異端者的管理指南》。

Alexander Scriven是澳大利亞悉尼Atlassian的高級數據科學家，擁有初創公司、政府和企業的經驗，建立了分析能力並執行數據科學項目。他非常喜歡教學和指導，並建立並提供了機器學習和深度學習的碩士級大學課程以及Datacamp等在線平台上評價很高的課程。他的研究興趣在於將數據科學技術應用於新興行業挑戰。Alex非常喜歡將尖端技術與商業應用相結合。