Data is an essential resource that powers the information economy and foresighted companies know that the data is valuable only when it is gathered completely and accurately and connected to other relevant data in a timely manner. Using business intelligence (BI) allows organizations to look into their historical and current data sets, and it provides them predictive views of their business operations.
Augmented by artificial intelligence (AI) and machine learning (ML), BI provides enterprises with decision-making context and recommendations. This significantly drives a move towards decision intelligence, the creative blend of technology into enterprise decision-making strategies and workflows.
What is decision intelligence (DI)?
Decision intelligence is an emerging field that involves different decision-making techniques to design, model, align, implement, and track decision models and processes. Integrating it offers a framework for business decision-making and processes through the integration of ML algorithms.
Since DI provides intelligent decision-making to businesses, it performs by observing germane information, investigating collected data, considering existing business capabilities, and contextualizing decision time.
DI vs. Self-service BI:
Self-service BI provides end-users the ability to capitalize on their data without having technical skills. Since it is crucial to comprehend the impact of today’s decisions on all of the desired outcomes, the next-generation BI tools can be the obvious solution leveraging faster and more modern cloud data platforms to solve business problems.
Unlike self-service BI, decision intelligence is focused on injecting the right information into the problem at the right time, or more accurately, at “critical moments of truth”.
The path ahead:
It is when decision intelligence is combined with AI that the potential to enhance the process gets fully realized. By injecting decision intelligence into the mix, organizations across industry sectors can benefit from more efficient services. It is often viewed as a marriage between data science, decision theory and social science.
According to Gartner, more than a third of large organizations will have analysts practicing decision intelligence by 2023. This is the next step for organizations looking to establish a cohesive decision strategy across people and departments. The return on investment will depend on the particular use case, implementation and how transformative the DI becomes for the enterprise.
Even though the concept of DI 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