Decision intelligence (DI) has been widely named as a top technology trend for several years, and Gartner reports that more than a third of large organizations are adopting it. Some even say that DI is the next step in the evolution of AI. Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies.

But until now, there has been little practical guidance for organizations to formalize decision making and integrate their decisions with data.

With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers.

This handbook addresses three widespread data-driven decision-making problems:

• How can decision makers use data and technology to ensure desired outcomes?
• How can technology teams communicate effectively with decision makers to maximize the return on their data and technology investments?
• How can organizational decision makers assess and improve their decisions over time?

Table of Contents

Chapter 1. Introduction

Chapter 2. Decision Requirements

Chapter 3. Decision Modeling: The Decision Design Process

Chapter 4. Decision Modeling: The Decision Asset Investigation Process

Chapter 5. Decision Reasoning: The Decision Simulation Process

Chapter 6. Decision Reasoning: The Decision Assessment Process

Chapter 7. Decision Action

Chapter 8. Decision Review

About This Book

This book is a practical, “roll up your sleeves” guide to how you can do DI, within your own organization or as a consultant or Decision Intelligence Service Provider (DISP) or Decision Intelligence Infrastructure Provider (DIIP) for others. It’s organized around a collection of nine DI “best practice” processes. We’ll walk you through each one, starting with how to decide if DI is right for your situation. We’ll show you how to go about designing a decision. By the time you work through the book, you’ll have a continuously improvable decision asset that is connected to data, AI, and more in a way that will drive competitive differentiation and success through better decision making.

But before we dive into the gnarly details, we feel it’s important for you to understand that you can start doing DI today. Seriously, we’re talking about 20 minutes from now: the time that it takes to get to the section called “Build Your First CDD, Right Now!” in the next chapter.

Who Is This Book For?

This book is for you if you’d like to learn how to introduce DI to your organization or to your clients. You might be an executive who takes decisions seriously, combining the best of diverse human and computer knowledge to drive competitive advantage. You might be passionate about addressing climate change, but you know that there’s a lot of earth observation (EO) data that’s going unused because data scientists don’t know how to connect it to decision making. You might be a data or AI consultant or an employee in one of the emerging DISP companies, looking to differentiate your practice by providing something new and valuable. You might be an ML expert who wants to maximize the value of this important technology, or a head of analytics or business intelligence who needs a way to communicate with your internal clients so that your technology helps them with better evidence-based decisions.

We’ve written this book for the “insurgent” bottom-up perspective, as well as for the lucky few who have obtained centralized executive sponsorship to take DI organization-wide. Indeed, we wrote this book in collaboration with a G20 central bank in the process of doing just that, and the bank has adapted this book for its internal use.

What You Will Learn

After completing this book, you will:

• Understand the kinds of decisions organizations make and which ones DI can help with
• Understand how to create, read, use, maintain, and reuse CDDs
• Have a “starter kit” of DI documents and templates that you can tailor for your organization

And you’ll understand how to use DI to:

• Structure decision conversations around desired outcomes (financial or not) and actions to achieve them
• Use state-of-the-art collaborative tools to map AI, knowledge, data, and more into decisions
• Find simplicity and order amid the confusion of complex data, tools, and decisions
• Provide value to your data management projects by guiding them toward the 10% of data that has 90% of the value
• Identify ways to integrate silos within your organization
• Specify your requirements for automated decision-reasoning simulations to a software team
• Earn greater trust and credibility for your data/analytics/AI team, because you speak your customer’s language

Please note that there are several topics that are not covered here. For instance, we don’t delve into the broader societal impacts of DI or its potential for solving complex problems like the climate and pandemics. These impacts are covered in Link. Finally, we don’t get into the technical specifics of how to build DI tools such APIs, interfaces, or AI and statistical models that interoperate with computerized DI models. Those technologies change quickly, but the principles we offer here stand on their own, independent of specific technological choices.