What is Decision Intelligence? A New Buzzword? 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.

The “need” for a whole “new” discipline is common among many of these explanations. The fact is data & analyst leaders and AI enthusiasts trying to improve the quality of decisions 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 value 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 of what a decision really is. And on top of this ambiguity, there are more challenges organizations face:

  • Many technologies try to improve the decision-making process in organizations, but they do not define and model decisions themselves. Hence, they do not understand the whole aspects of the decisions.
  • Data and analytics try to use different types of analytics yet forget about the complexity of integrating the algorithm's results within systems and 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 than 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, their impact, and their effectiveness on business operations in the scope of their projects.
  • The cultural aspect of automated decision-making (or, in general, any automation) is undermined by technologists. Culture change is required for wide adoption and, therefore, the successful implementation of any decision automation.

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

How to Apply Decision Intelligence Framework and Principal?

For sure, based on the currently existing challenges, companies should start thinking about how to use this framework. But many challenges can embrace this new principle, “Decision Intelligence”.

Decision-Centric Approach to implement Decision Intelligence

Decision-Centric Approach to implementing Decision Intelligence 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 talents based on a clear understanding of what problems those companies want to solve and what business they are in. Are they in the business of building AI algorithms? Or are they in the business of using advanced technologies to make business operations efficient? If the answer is the latter, a team of data scientists working on an algorithm for 99.99% accuracy will not 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 the organization objectives and deliver real business value. Therefore, the decision-centric approach is definitely a must-have complement to the Decision Intelligence framework, and it will provide the detailed 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., an integrated solution with predictive and prescriptive analytics and decision robotics to use and carry out the results of decisions.

Last updated April 5th, 2022 at 05:13 pm, Published May 28th, 2020 at 05:13 pm