Название: The Decision Intelligence Handbook: Practical Steps for Evidence-Based Decisions in a Complex World (Second Early Release)Автор: L.Y. Pratt, N.E. Malcolm
Издательство: O’Reilly Media, Inc.
Год: 2023-02-17
Страниц: 181
Язык: английский
Формат: epub (true), mobi
Размер: 24.0 MB
Decision Intelligence (DI) has been widely named as a top technology trend for several years, and the Gartner Group 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 Artificial Intelligence (AI). Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies. Until now, there has been little practical guidance for organizations to formalize decision-making and integrate their decisions with data.
Decision Intelligence (DI) is a methodology and set of processes and technologies for making better, more evidence-based decisions by helping decision makers understand how the actions they take today can affect their desired outcomes in the future. The key concept of DI is the idea that you can design decisions. Importantly, a decision, as we treat it here, is a thought process about an action that, once made, leads to specific outcomes without any further intervention.
Under the hood, DI is all about integration and connecting human decision-makers with data, AI, complex systems modeling, human behavior, and many other disciplines. Simply put, DI helps organizations to make better decisions by combining the best of human expertise with all sorts of technology.
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?
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