What is Decision Intelligence? A new buzz word? Technology? AI of some sort?

If we look at Decision Intelligence on the internet, we will see there are very different opinions on what it is. This is not surprising as it is a very open abstract concept, and everyone has their own point of view on the topic.

VP Analysts at Gartner defines Decision Intelligence as:

A framework that brings multiple traditional and advanced techniques together to design, model, align, execute, monitor, and tune decision models and processes.

And the Head of Decision Intelligence at Google defines it as:

a new discipline brings together the best of applied data science, social science, and managerial science to use data to improve the lives of business and lead AI projects.

What really is common between many of these explanations is the “need” for a whole “new” discipline. The fact is data & analyst leaders and AI enthusiasts trying to improve the decision making quality within organizations using algorithms and of course massive amounts of data for a while now.
Yet when we look at the stats from different reports of Forester, Gartner, and other firms we see many organizations are failing to see values in these technology-led projects.

Despite all the advancements in technologies and access to massive amounts of data, these projects could not improve the decision-making process in organizations and deliver better business value. Why is that?

Current Problems

The belief is that such technologies can drive better decision making and thus business outcomes. However, there is a real gap between this belief and the idea about what a decision really is. And on top this ambiguity there are more challenges organizations facing:

  • Many technologies try to improve the decision-making process in organizations, but they do not define, and model decisions itself. Hence, they do not understand the whole aspects of the decisions.
  • Data and analytics trying to use different types of analytics yet forgetting about the complexity of integrating the results of the algorithm within systems, processes, and take actions to influence a change – the last mile of analytics problem.
  • A single technique or technology is used based on team preference and expertise yet, the landscape of businesses is far more complex that the improvements can be achieved with a single technique.
  • Both data & analyst leaders and AI enthusiasts, are more focused on the accuracy of the algorithms and building them than understanding decisions, the impact and effectiveness of them on business operations in the scope of their projects.
  • The cultural aspect of automated decision-making (or in general any automation) is undermined with technologists. Culture change is required for wide adoption and therefore the successful implementation of any decision automation.

The promise of the new discipline of “Decision Intelligence” is to provide a framework that brings all different aspects of decision-making into a unified view to addressing the above challenges.

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How to Apply this new Framework and Principal?

For sure, based on the current existing challenges, companies should start thinking about how to use this framework. But there are many challenges to embrace this new principal, the “Decision Intelligence”.

Decision-Centric Approach to implement Decision Intelligence

Decision-Centric Approach to implementing Decision Intelligence that brings all the technologies into an integrated solution to deliver real business value.

First, talent acquisition in companies should be changing and looking for different types of talents based on a clear understanding of what problems those companies want to solve and what business are they in. Are they in the business of building AI algorithms? Or, are they in the business of using advanced technologies to make business operation efficient? If the answer is the later, team of data scientists working on an algorithm for 99.99% accuracy is not going to meet their expectations.

Second, the Decision Intelligence framework gives a very broad and high-level guide on what technologies and models should be looking at, does not provide any best practices, and clear guidelines on how to start and breakdown a complex business problem to ensure it is aligned with organization objectives and deliver real business value. Therefore, decision-centric approach is definitely a must-have complement to the Decision Intelligence framework and it will provide the details design for successful implementation of decision-making processes embracing all required technologies.

And last but not least, a technology that enables the team to put all different parts and components of the solution together, and enables the team to deploy and manage the solution in an iterative and flexible manner. This is a critical requirement for success as the final solution is definitely the orchestration of multiple smaller parts built using different techniques and technologies e.g. integrated solution with predictive and prescriptive analytics as well as decision robotics to use and to carry out the results of decisions.

Published May 28th, 2020 at 07:36 am