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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Learn to use, and not be used by, data to make more insightful decisions
The availability of data and various forms of AI unlock countless possibilities for business decision makers. But what do you do when you feel pressured to cede your position in the decision-making process altogether?
Decision Intelligence For Dummies pumps the brakes on the growing trend to take human beings out of the decision loop and walks you through the best way to make data-informed but human-driven decisions. The book shows you how to achieve maximum flexibility by using every available resource, and not just raw data, to make the most insightful decisions possible.
In this timely book, you’ll learn to:
- Make data a means to an end, rather than an end in itself, by expanding your decision-making inquiries
- Find a new path to solid decisions that includes, but isn’t dominated, by quantitative data
- Measure the results of your new framework to prove its effectiveness and efficiency and expand it to a whole team or company
Perfect for business leaders in technology and finance, Decision Intelligence For Dummies is ideal for anyone who recognizes that data is not the only powerful tool in your decision-making toolbox. This book shows you how to be guided, and not ruled, by the data.
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Seitenzahl: 547
Veröffentlichungsjahr: 2021
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Decision Intelligence For Dummies®
Published by: John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030-5774, www.wiley.com
Copyright © 2022 by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Library of Congress Control Number: 2021952522
ISBN: 978-1-119-82484-8 (pbk); 978-1-119-82485-5 (ebk); 978-1-119-82486-2 (ebk)
Decision Intelligence For Dummies®
To view this book's Cheat Sheet, simply go to www.dummies.com and search for “Decision Intelligence For Dummies Cheat Sheet” in the Search box.
List of Tables
Chapter 15
TABLE 15-1: The Four Structured Decision-Making Models
List of Illustrations
Chapter 3
FIGURE 3-1:
How
fast are these spinning again?
Chapter 4
FIGURE 4-1: The inverted V.
Chapter 6
FIGURE 6-1: Is a picture always worth a thousand words?
Chapter 9
FIGURE 9-1: Machine learning has trouble accurately recognizing objects among s...
FIGURE 9-2: Feast, an open source feature store.
FIGURE 9-3: Comparing what is with what could be.
Chapter 10
FIGURE 10-1: Looking at RapidMiner, a data science software platform.
Chapter 12
FIGURE 12-1: A set of PowerPoint decision tree templates.
FIGURE 12-2: A Microsoft Excel SWOT template.
Chapter 14
FIGURE 14-1: Causal – Scenarios, as shown in the Google Workspace Marketplace.
FIGURE 14-2: The home of what-if on the Excel Ribbon.
FIGURE 14-3: The Excel Scenario Manager Wizard.
FIGURE 14-4: The WhatIf add-in, as shown in Google Workspace Marketplace.
FIGURE 14-5: Excel's Goal Seek feature.
FIGURE 14-6: The Goal Seek add-in, as shown in Google Workspace Marketplace.
Chapter 15
FIGURE 15-1: The Coggle brainstorming tool.
FIGURE 15-2: Brainpartner's causal loop tool.
FIGURE 15-3: Visme's structural thinking tool.
Guide
Cover
Title Page
Copyright
Table of Contents
Begin Reading
Index
About the Book Author
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Introduction
Ready for a mind-blowing reveal on how to make great decisions, whether you’re using your own brain or some supercharged artificial intelligence application? Decision intelligence, a methodology for forming a decision aimed at achieving a specific outcome, is here, and it's on track to change forever how businesses plan for their future.
Everybody would agree that the goal in all decision-making is to reap the best possible outcome. Decision intelligence helps you achieve that goal by requiring that you decide that outcome first and then work backward from there to identify the processes and information you’ll need to make it happen!
Decision intelligence is built on science — several sciences, actually — but some of those scientific formulas can be grasped intuitively. The decision intelligence process is designed to improve your professional performance by a) ensuring that every business decision delivers the best possible outcome, b) pointing you toward innovations that are profitable, c) helping you become an industry mover by becoming a creative disruptor, and d) enabling you to flip failed AI projects into successful endeavors. What's more, decision intelligence can also be used to improve your private life via better decision-making, and you can often do it in your own head or on the back of a napkin or by using a simple table or spreadsheet.
The secret to success in decision intelligence lies in changing how you think about problem-solving and reordering your steps when it comes to the decision-making process. Ask yourself how much money, time, and effort your organization is willing to waste on yet another bad business decision or one more failed AI project, and then ask yourself whether you can afford to ignore a better way to make decisions — especially when you already have on hand much of what you’ll need to take advantage of a decision intelligence approach. It’s not often that you can turn your business around at little or no additional cost to you.
About This Book
The book you’re holding in your hands is a guide primarily for you if you’re a business or finance leader. The book aims to fill you in on decision intelligence, a new framework for making better, more profitable business decisions. It also serves as an introduction for artificial intelligence (AI) and digital decisioning practitioners to take a different approach aimed at making automated decision processes deliver desirable business outcomes. To top it all off, this guide shows you that decision intelligence is not merely a business approach — it’s equally useful when making decisions about your personal life.
This book takes a studied approach to having you reimagine the decision-making, by focusing on a set of discrete tasks you need to accomplish. Here are those tasks, in no particular order:
Flip the data mining model from data first to data last.
You start with a decision aimed at the best possible business outcome and end with the data and the processes you need to bring about that outcome in the real world.
Rebalance human and machine roles.
Decision intelligence calls for a redirection from a data driven to a decision driven organization. This framework clearly casts humans as decision-makers, where AI acts as sidekick, and where data is relegated to a supporting actor.
Map changes caused by putting the decision first in terms of
Business impact
Processes
Tools
Business and Ethical Principles
Teams
Learn decision theory and a multidisciplinary approach to decision-making:
You learn which steps you must take in order to succeed with decision intelligence, from new perspectives on
Business impact
AI projects
Upstream and downstream decisioning
Disruptive innovation
Job roles
This book answers your questions about what decision intelligence is, which conditions must be created at your company in order for it to succeed, how you can plan a project, and how to implement it successfully. I've also made an effort to ensure that this book can be used in myriad ways and by anyone, from individuals to powerful leaders of huge organizations. As such, it offers these benefits:
An overview of the steps involved in putting the decision before the data in the decision-making process
A guidebook with practical suggestions for the various options, overall flexibility, and choices of implementations of a decision intelligence strategy
A reference book divided into parts, chapters, and sections so that you can quickly find the content you’re looking for when you need it
This book — designed so that you can swiftly get a grasp on everything — features many examples, instructions, checklists, illustrations, and tables. It’s also structured systematically according to the decision intelligence framework and its many moving parts.
Conventions Used in This Book
This book doesn’t have many rules. The entire book is structured so that you can quickly find everything you need and get a grasp on the content. The detailed table of contents helps you jump right to the information you need, and each chapter begins with a brief and succinct description of the chapter's main topics. Whenever topics overlap or other chapters are mentioned, cross-references help you conveniently jump back-and-forth between the chapters. If you’re interested in a particular term, you can look it up in the index.
Foolish Assumptions
This book is not (only) for decision-makers in business or finance. Decision intelligence is too crucial for improving business outcomes to be contained only to the C-suite and data scientist levels. In organizations that practice or seek data- and AI-democratization, decision intelligence should be practiced at every level of decision-making throughout the organization, even at the microdecision and mundane-decision levels. Whether you work at a company, an educational institution, a research institute, a public agency, or a nonprofit organization, you can benefit from the decision-driven approach that is at the heart of decision intelligence. Whether you have an education in the technical, economic, management science, or social science fields, this creative approach gives you new ideas on how to use what you know (and what you have to decide) more productively.
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 ForDummies 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.
This icon points to tips and tricks that should be helpful when you apply and implement an idea. They show you how you can improve your project.
The Remember icon is used to highlight information that’s particularly important to know or that can help clear up possible confusion later.
This icon makes you aware of potential stumbling blocks and warns you when to not do something. If you avoid errors that others have made before you, you’ll save time, money, and effort.
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 more detail than other forms of decision implementation. Therefore, I recommend that you read the Parts 1 and 2 first to ensure that you have a good grasp of the framework overall before touching on related topics in other parts or chapters.
Otherwise, experiment with the reading strategy that works best for you. Jump to different sections while you read this book, if that makes sense to you. If necessary, reread a chapter multiple times or look up individual terms in the index. The idea here is for you to come up with your own way to read this book effectively. And don’t forget to keep it nearby for quick-and-easy reference as needed while you work through your first few decision intelligence projects.
Part 1
Getting Started with Decision Intelligence
IN THIS PART …
Mining data verus minding the answer
Learn why math-only approaches are weak
Watching the details and missing the big picture
Discover the epiphany in the inverted V approach
