On an individual level, the following assumptions are made in this book about readers who will most likely gain the most from the information in this book:
You’re in charge of an organization or department and you want to be decision driven instead of data driven so that every decision is productive and profitable.
You’re trying to accelerate your career plans and you want to shine by making important decisions so that the best possible outcome is realized.
You are applying, or you are planning to apply, AI or machine learning at your organization, and you need to know how to make projects succeed in terms of measurable business impacts.
Your company is already working with data-driven methods and falling well short of your organization’s goals and expectations. You want to enhance or replace your previous work with new methods, tips, and tricks for improving its implementation, and you want a guide on how to make it work and perform consistently well over time.
You don’t need to have any specific skills for this book — you only have to be curious and intent on making good decisions — every time.
What You Don’t Have to Read
It’s worth your time to read the entire book. You can find important tips everywhere in it. Even if you can use only a few of its suggestions, the time and money you invest will be worth it. I guarantee that you’ll be able to use more than just a few elements of this information in your private life, your career, and your organization — regardless of your job role or your experience in decision-making. Some of the text in this book appears in a gray box, in order to highlight background information. You don’t absolutely need this info, but it’s always helpful.
How This Book Is Organized
This book is organized into six distinct parts, as described in this section. The design is intended to help you break free of any brain ruts and consider new ways of thinking about making decisions based on a variety of perspectives.
Part 1: Getting Started with Decision Intelligence
This section gives you an overview of the principles and methods in the decision intelligence framework. You can find out why being decision driven outperforms being data driven. You can also learn how to create the necessary conditions for decision intelligence projects to succeed at your organization, how to plan a project, and how to reinvent what it means to have an actionable outcome.
Part 2: Reaching the Best Possible Decision
The first phase of the decision intelligence process is all about making the decision from which you build the steps and then choosing the tools and data to realize the result of that decision in the real world. Shaping the decision, mapping a path, and choosing the right tools are essential to creating the best possible outcome. At the conclusion of deciding the impact you seek lies the beginning of the questions to be answered.
Part 3: Establishing Reality Checks
In the decision intelligence framework, you need to start with a decision, but that decision must be rooted in reality, and it must be attainable. In other words, this isn’t the place for pipedreams, even if profoundly creative disruption is your goal. To keep things grounded, you simply have to take the measure of job roles and team skill diversification, play to both human and machine strengths, ensure that decisions you intend to automate at large scales actually work at scale, among other reality checks. You can’t manage — or make a reality — that which you can’t measure. Be sure to measure the important things and skip the unimportant to ensure your decision (as well as its expected impact) is solid.
Part 4: Proposing a New Directive
Decision intelligence has many uses and is heavily based on ideas tied directly to favorable outcomes. As such, it plays a significant role in the Idea Economy, in impacts on entire industries, and in building competitive advantage for organizations, governments, and economies. In short, disruption is the point, change is constant, and you can use decision intelligence to command or at least direct both.
Last but not least, the use of decision intelligence can also quickly build and accelerate career paths and turn decision masters into highly influential power brokers. All of these grand rewards come with varying degrees of risks, however.
Part 5: The Part of Tens
No For Dummies book exists without The Part of Tens. In this part, you can read about ten (or so) steps to set up a smart decision and ten (or so) pitfalls to avoid in implementing decision intelligence projects.
Icons Used in This Book
Now and then, you find symbols in in the margins of this book. Their purpose is to make you aware of important information, as described here.
Beyond the Book
In addition to the text you’re reading right now, this publication comes with a free, access-anywhere Cheat Sheet that offers a number of tips, techniques, and resources related to data science. To view this Cheat Sheet, visit www.dummies.com and type decision intelligence for dummies cheat sheet in the Search box.
Where to Go from Here
You can start immediately by choosing one of these two strategies:
Read the book straight through, from cover to cover.
Find individual chapters that you want to read first. (Each chapter covers an entire subject area so that you can read and understand it independently of the other chapters.) If you have no experience with decision intelligence yet, I recommend starting with Chapter 1, which offers a crash course introduction to the concept.
My advice to you: Be aware that decision intelligence, though it has a firm definition, is used more loosely by several groups. For example, people working in AI most typically use it to mean putting the decision first in programming automation or training machine learning to make better automated decisions at scale. That’s an application rather than a definition, but its common use as such can cause some confusion over the meaning of the term in general reading. For the purposes of this book, decision intelligence is meant by its broader definition and not a single application. However, given its prevalence in AI, the applications there are covered in