Introduction

A revolutionary change is taking place in society. Everybody, from small local companies to global enterprises, is starting to realize the potential in digitizing their data assets and becoming data driven. Regardless of industry, companies have embarked on a similar journey to explore how to drive new business value by utilizing analytics, machine learning (ML), and artificial intelligence (AI) techniques and introducing data science as a new discipline.

However, although utilizing these new technologies will help companies simplify their operations and drive down costs, nothing is simple about getting the strategic approach right for your data science investment. And, the later you join the ML/AI game, the more important it will be to get the strategy right from the start for your particular area of business. Hiring a couple of data scientists to play around with your data is easy enough to do - if you can find some of the few that are available - but the real heavy lifting comes when you try to understand how to utilize data science to create value throughout your business and put that understanding into an executable data science strategy. If you can do that, you are on the right path for success.

A recent survey by Deloitte of "aggressive adopters" of cognitive technologies found that 76 percent believe that they will "substantially transform" their companies within the next three years by using data and AI. IDC, a global marketing intelligence firm, predicts that by 2021, 75 percent of commercial enterprise apps will use AI, over 90 percent of consumers will interact with customer support bots; and over 50 percent of new industrial robots will leverage AI.

However, at the same time, there remains a very large gap between aspiration and reality. Gartner, yet another research and advisory company, claimed in 2017 that 85 percent of all big data projects fail; not only that, there still seems to be confusion around what the true key success factors are to succeed when it comes to data and AI investments. This book argues that a main key success factor is a great data science strategy.

The target audience for this book is anyone interested in making well-balanced strategic choices in the field of data science, no matter which aspect you're focusing on and at what level - from upper management all the way down to the individual members of a data science team. Strategic choices matter! And, this book is based on actual experiences arising from building this up from scratch in a global enterprise, incorporating learnings from successful choices as well as mistakes and miscalculations along the way.

So far, there seems to be little in-depth research or analysis on the topic of data science and AI strategies and little practical guidance as well. In fact, when researching for this book, I couldn't find another single book on the topic of data science strategy. However, several interesting articles and reports are available, like TDWI's report, "Seven Steps for Executing a Successful Data Science Strategy" (https://tdwi.org/research/2015/01/checklist-seven-steps-successful-data-science-strategy.aspx?tc=page0&m=1) or The Startup's "How To Create A Successful Artificial Intelligence Strategy" https://medium.com/swlh/how-to-create-a-successful-artificial-intelligence-strategy-44705c588e62). However, these articles primarily focus on easily consumable tips and tricks, while bringing up a few aspects of the challenges and considerations needed. There is an obvious lack of in-depth guidance which is not really accessible in an article format.

At the same time, the main reasons companies fail with their data science or AI investment is that either there was no data science strategy in place or the complexity of executing on the strategy wasn't understood. Although this enormous transformation is happening right here, right now, all around us, it seems that few people have grasped how data science will impose a fundamental shift in society - and therefore don't understand how to approach it. This book is based on more than ten years of experience spent driving different levels of strategic and practical transformation assignments in a global enterprise. As such, it will help you understand what is fundamentally important to consider and what you should avoid. (Trust me: There are many pitfalls and areas to get stuck in.) But if you want to be in the forefront with your business, you have neither the time nor the money to make mistakes. You really want a solid, end-to-end data science strategy that works for you at the level you need in order to bring your organization forward. The time is now! This is the book that everyone in data science should read.

About This Book

This book will help guide you through the different areas that need to be considered as part of your data science strategy. This includes managing the complexity in data science and avoiding common data challenges, making strategic choices related to the data itself (including how to capture it, transfer it, compute it, and keep it secure and legally compliant), but also how to build up efficient and successful data science teams.

Furthermore, it includes guidance on strategic infrastructure choices to enable a productive and innovative environment for the data science teams as well as how to acquire and balance data science competence and enable productive ways of working. It also includes how you can turn data into enhanced or new business opportunities, including data-driven business models for new data products and services, while also addressing ethical aspects related to data usage and commercialization.

My goal here is to give you relevant and concrete guidance in those areas that require strategic thinking as well as give some advice on what to include when making choices for both your data and AI investment as well as how best to come up with a useful and applicable data science strategy. Based on my own experience in this field, I'll argue for certain techniques or technology choices or even preferred ways of working, but I won't come down on one side or the other when it comes to any specific products or services. The most I'll do in that regard is point out that certain methods or technology choices are more appropriate for certain types of users rather than others.

Foolish Assumptions

Because this book assumes a basic level of understanding of what data science actually is, don't think of it as an introduction to data science, but rather as a tool for optimizing your analytics and/or ML/AI investment, regardless of whether that investment is for a small company or a global enterprise. It covers everything from practical advice to deep insights into how to define, focus, and make the right strategic choices in data science throughout. So, if you're looking to find a broad understanding of what data science is, which techniques and ML tools come recommended, and how to get started as a data scientist professional, I instead warmly recommend the book Data Science For Dummies, by Lillian Pierson (Wiley).

How This Book Is Organized

This book has six main parts. Part 1 outlines the major challenges that companies (small as well as large) face when investing in data science. Whereas Part 2 aims to create an understanding of the strategic choices in data science that you need to make, Part 3 guides you in successfully setting up and shaping your data science teams. In Part 4, you find out about important infrastructure considerations, managing models in development and production and how to relate to open source. In Part 5 you learn all about commercializing your data business and monetizing your data. And, and is the case with all For Dummies books, this book ends with The Part of Tens, with some practical tips, including what not to do when building your data science strategy and spelling out why you need to create a data science strategy to begin with.

Icons Used In This Book

I'll occasionally use a few special icons to focus attention on important items. Here's what you'll find:

This icon with the proverbial string around the finger reminds you about information that's worth recalling.

Expect to find something useful or helpful by way of suggestions, advice, or observations.

The Warning icon is meant to grab your attention so that can you steer clear of potholes, money pits, and other hazards.

This icon may be taken in one of two ways: Techies will zero in on the juicy and significant details that follow; others will happily skip ahead to the next paragraph.

Beyond The Book

This book is designed to help you explore different strategic options for your data science investment. It will guide you in your choices for your business, from data-driven business models to data choices and from team setup to infrastructure choices and a lot more. It will help you navigate the most common challenges and steer you toward the success factors.

However, this book is aimed at covering a very broad range of areas in data science strategy development, and is therefore not able to deep-dive into specific theories or techniques to the level you might be looking for after reading parts of this book.

In addition to what you're reading right now, this product comes with a free access-anywhere Cheat Sheet that offers a number of...