Название: Data Science and Analytics Strategy: An Emergent Design Approach
Автор: Kailash Awati, Alexander Scriven
Издательство: CRC Press
Серия: Data Science Series
Год: 2023
Страниц: 231
Язык: английский
Формат: pdf (true)
Размер: 13.8 MB

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.

Before beginning any discussion of the what, how, and why of Data Science, it is necessary to set the scene for where it sits within traditional data- related functions. Data Science itself is not new; many old and well- established analytical techniques have been rebranded as Data Science or Machine Learning (ML) techniques. Be that as it may, there is a general perception that when problems become sufficiently complicated or difficult (both, indeed, quite subjective terms), it is appropriate to label what is being done as being advanced and thus worthy of being called Data Science. This is why we will avoid defining the term and instead discuss what data scientists do, where Data Science fits into the modern organisational landscape, and the elements that are needed in order to do Data Science. In this book we will use the term data analytics stack to describe both the functional and technical elements that are required for Data Science.

The bottom half of the figure deals with matters such as data ingestion (acquisition), storage, and access. These are foundational elements of the stack. The top half deals with the things one needs to do in order to extract business value from the data. This includes data analytics, (which refers to traditional data analysis work that involves data exploration, analysis, and reporting), business intelligence (BI), and Data Science. We’ll discuss data analytics in relation to BI and Data Science later in this chapter. For now, we’ll simply note that data analytics refers to a broad range of tools and techniques that help provide insights into organisational performance. We will defer a detailed discussion of the topmost element of the stack, MLOps (Machine Learning Operations), to Chapter 7 which will also include a more detailed outline of how Data Science projects can be undertaken in a considered and collaborative fashion. For now, we simply note that it refers to the things one needs to do in order to make the products that data scientists build available to the business in a reliable and repeatable way.

The present chapter is an overview of topics that we cover in much greater detail in the latter half of the book (Chapters 5– 8). Our primary aim here is to prompt readers to think about how/ where a data science capability might fit within their organisations. We’ll start from the bottom of the stack and work our way up (with the caveat noted above about MLOps). Although the chapter is written for those who are new to data strategy and building data capabilities, it may also serve as a review for those who are experienced in these areas.

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.








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