Incorporating decision intelligence in data practices
Andreas Bartsch, Head of Service Delivery at PBT Group
As part of the ongoing series examining Gartner’s ‘Top 10 Tends in Data and Analytics for 2020’, this month the focus turns to decision intelligence. Defined as an engineering discipline that augments data science with theory from social science, decisioning, and managerial science, it centres on analysing the cause and effect of the decision-making process.
According to Gartner, this provides a framework to help data and analytics leaders design, compose, model, align, execute, monitor, and tune decision models and processes in the context of business outcomes and behaviour. In more practical terms, this sees the likes of data science, artificial intelligence (AI), and machine learning go through the decision-making process in an unemotional way.
Taking care of decisions
So, instead of having people think about different options, the decision intelligence framework and techniques enable an examination of various scenarios from data, social, and managerial perspectives, to make an informed recommendation. Humans are limited by the number of scenarios they can effectively look at given the information at their disposal. Eventually, in many cases, they reach the stage where they decide on an outcome and use any data available to them to justify their view.
Decision intelligence eliminates this human bias. It turns information into better actions at any scale, not encumbered by the limitations of people. Of course, this is not to say that it will replace the role of an individual. Instead, the data scientist and business analyst will become even more important as they each play instrumental roles in defining the algorithms used for the framework. Some elements might need to be qualitative while others quantitative. But ultimately, decision intelligence requires a person or a team of people who understand the business and what analysis is required to extract value from the data.
It is when decision intelligence combined with AI that the potential to enhance the process gets fully realised. Contrary to popular belief, AI is not a completely human-less technology. Technology is a reflection of its creators and systems that operate at scale that can amplify human shortcomings. Decision intelligence ensures responsible AI leadership can take place.
This only highlights how important it is to have a data engineer in place that can optimise the systems to make information readily available at scale. By injecting decision intelligence into the mix, organisations across industry sectors can benefit from more efficient services. For example, a bank can use decision intelligent to conduct predictive maintenance on ATMs that are frequently used ensuring they do not break down during peak transactional times such as weekends, month ends, or during busy holiday seasons. Price optimisation can be implemented at scale for airlines, hotels, and even pharmacies to adapt to changing market conditions.
Even though the concept of decision intelligence is still relatively new on local shores, the potential to disrupt the AI market is significant. Companies must be cognisant of this framework and gain an understanding of how to leverage it for optimal data analysis.