Digging into decision intelligence

Digging into decision intelligence

In this post, I will purposefully drive the term ‘decision intelligence’, as an experiment, but feel free to read ‘data science’ instead.

Cassie Kozyrkov, at Google, has been promoting the idea of decision intelligence. Decision intelligence, the term is coined by Kozyrkov, aims to regroup known decision-making methods, insights, best practices under a common umbrella. Examples of these known methods and insights are:

  • AnalyticsStatistics, both frequentist and BayesianMachine learningDecision scienceBehavioral economicsThe psychology and neurology of decisions

Data science is purposefully omitted from the list above. Data science is a popular nomer, but not well defined: isn’t all science data science? Perhaps this post is an experiment to see if the term decision intelligence better demarcates a coherent field of interest. To start, a central tenet of decision intelligence is that a lot about the field currently known as data science has to do with making decisions; on a large or a smaller scale. How does one decide on how to decide? That is the scope of this post.Part of the outcome of this new demarcation is that data science projects as we knew them can be better executed by being more precise about the structure of the different tasks and the required roles.

Is there such a thing as a decision architecture or infrastructure, and how would that look?

The goal of this post is to summarize hopefully a large part of the decision intelligence content as posted by Google’s Kozyrkov in different blog posts and media. As a summary, it is preliminary because although based on a sizeable chunk of blog posts, it does not aim to include all content. And it is not clear if all information is published online. In this post, I will purposefully drive the term ‘decision intelligence’, as an experiment, but feel free to read ‘data science’ instead.

data-science statistics machine-learning decision-intelligence

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