Gartner released their top strategic technology trends for 2022 and Decision Intelligence was one of them. They described it as “a practical approach to improve organisational decision making”. It was the first time I had ever come across the term but it definitely got my attention. I wanted to find out more about it and how it could be used in practice.

To be honest, I found that to be a lot easier said than done. Decision Intelligence is still a relatively new field and there are some amazing pioneers out there such as Lorien Pratt, who wrote a book on the topic, and Cassie Kozyrkov, head of decision intelligence at Google.

However, I struggled to find actionable resources on the topic.

When learning something new, it is helpful to first understand its philosophical and theoretical components. However, you may get to a point, as I did, where you begin to ask yourself “great, this thing is important, how do I use it?” It is here that I found resources to be incomplete.

In this blog post, I will give a (brief) overview of decision intelligence and why it is important. Then I will do my best to explain how decision intelligence can be applied.

What is Decision Intelligence?

The basic idea behind decision intelligence is that “decisions are based on our understanding of how actions lead to outcomes“. It answers the question: “if I make this decision and take this action today, what will be the outcome tomorrow?”

Beyond that, making a decision is about forecasting through time in the hopes that the desired outcome can be achieved. Decision intelligence allows you to understand the ripple effect of a decision across other areas, both internally and externally.

Decision intelligence is not an alternative to current solutions or another tool in the data toolbox. Instead, it is a unifier: bringing together complex systems, business intelligence, analytics, machine learning, and AI into a framework where the decision is the main driver.

Instead of trying to find the tools and methods that store, retrieve, analyse, and support data, decision intelligence focuses on the decision first and the data takes on a supporting role.

Why is Decision Intelligence Important?

It is common to find a large gap between the decision-makers in an organisation and the data scientists and analysts. Decision-makers feel that they don’t have enough of the right information available to them to make big decisions that could have a significant impact on the business. On the other hand, many data scientists and analysts feel like their models, reports, and analyses aren’t being used to their full potential in the business.

Decision intelligence helps to bridge this gap, requiring a new way of thinking for both decision-makers and analysts. By focusing on decisions first and allowing them to be the driver of everything else, we can do a much better job of working together. Ultimately, models, reports, and analyses are not of any value (no matter how complex or cutting edge they may be) unless they lead to some action.

In organisations with so much at stake like the Millennium Project and the United Nations, there is a high risk of making decisions that can have unintended consequences. In these cases, the systems are so complex and overwhelming that decision-makers tend to suffer from ‘analysis-paralysis’, and rather than making data-driven decisions, they resort to just using their intuition.

With the rise of big data, we’ve got a new problem. How can we make the connection between data and the problems we care about?

How can data be used to solve really difficult problems like conflict and poverty? For a company, how can data be used to make more money and gain a competitive advantage? For medical providers in Sub-Saharan Africa, how can data help them decide where they should get medicine and which doctors they should train?

How to Apply Decision Intelligence

Through my research, I have found 2 main ways to use decision intelligence in practice. The exact methodology of DI is different for every organisation but usually involves a cause-and-effect diagram and a visual decision model.

The process starts with a decision in mind and by applying a blueprint for how to approach that decision, we can then determine what action needs to be taken to reach an outcome.

Causal Decision Diagram

The core application of decision intelligence is to understand how we can get from cause to effect. Lorien Pratt refers to the diagrams that allow us to examine cause and effect as a Causal Decision Diagram (CDD).

She had found a pattern in decision making through her work and created a template that contains the following elements:

• Decision Levers (choices) – the things you can change; decisions you need to make
• Externals – things that cannot be affected directly but are related to and affect the outcome
• Intermediates – factors involved in the decision
• Outcomes – the thing you are ultimately trying to acheive

Interactive Decision Models

The final output of a decision model is a visual representation of the causal decision diagram. This is the part of the decision model that incorporates data to objectively determine the effect of a decision.

The process of reaching a final decision model requires a combination of business intelligence, predictive analytics, machine learning, and various other analytical techniques.

Lorien Pratt gives a nice example of this visual model by considering the impact of the decision to invest in a new training program in a company. Levers can be adjusted and, together with some predictive models, the total investment benefit can be determined.

Decision intelligence is the missing link between data and decision-making. For all the amazing data science and artificial intelligence advances being made today, none of it is valuable unless it can be used to take action.

I hope this blog post was able to give you a sense of what decision intelligence is and how it can be used in practice.