Decision Intelligence (DI) is a practical discipline that aims to support decision making by understanding and engineering the intelligence process, driving the decision-making process and the evaluation (and improvement, through feedback processes) of the outcoming results.
As a discipline, it is based on the psycho-social concept of decision making, expressed in the subset of decision intelligence which includes:
- Decision Modeling: make the decision-making models explicit (their flow and their logic, the actors involved, the deterministic and non-deterministic aspects, structured and unstructured) to understand and make operational the processes underlying the decisions;
- Decision Intelligence Systems: the implementation of decision-making models orchestrated by machines.
Decision intelligence: an overview
Decision Intelligence, therefore, is a set of enabling technologies to improve decision making designed to meet the growing need for transparency, repeatability, interpretability, impartiality, reliability, and accountability deriving from the dynamism and complexity of corporate ecosystems.
In short, it is a data-driven process that enables faster and more accurate decision making by leveraging a comprehensive information landscape, enabling data-driven decisions to be made faster and more accurately.
DI combines artificial intelligence, machine learning, contextual intelligence and automation to specifically support analysts in the data gathering and data analysis phases, in order to generate concrete and actionable outputs that can be applied the specific decisions contextualization and the customer needs analysis, in order to understand the need for decision support creating business value and speeding up time-consuming processes.
It also increases organizational skills in using huge (and various) amounts of data to obtain detailed information, contextualize business decisions and review the impact that decisions will have on the organization or specific person.
Decision Intelligence does not replace humans in the decision-making process, as the analyst is certainly an added value of the analysis.
However, it is through the help of the platform that the analyst is able to contextualize, organize and analyze heterogeneous data in order to provide his recommendations to the decision maker.
Therefore, it increases the analysts’ ability to make decisions, improving them, contextualizing them and making them more coherent.
As the use of Decision Intelligence in business processes increases, the speed in making decisions and contextualizing information increases proportionally by supporting intelligence investigations, which are achieved more easily and less costly.
Decision Intelligence includes a feedback loop (also known as closed-loop learning) in order to retrain and improve the system over time.
Origins and theories
Decision Intelligence is based on the assumption that, in most organizations, the decision-making process could be improved if a more structured approach were used.
It seeks to overcome a decision-making “ceiling of complexity”, characterized by a discrepancy between the sophistication of organizational decision-making practices and the complexity of the situations in which such decisions must be made.
DI represents a practical application in the field of complex systems, which helps organizations navigate the complexity of the contexts in which they have to operate.
It can also be considered as a framework for advanced analysis and machine learning techniques, which can be used comfortably from the desktop of the non-expert decision maker.
Decision Intelligence theorists believe that many organizations continue to make bad decisions. In response to this, DI seeks to unify a set of decision-making best practices.
Telsy’s Decision Intelligence: Context Intelligence
In the growing complexity of today’s scenarios, one of the main priorities for organizations is to become a data-driven company, supporting big data analysis processes with artificial intelligence technologies to meet the changing needs of customers, be successful among new digital competitors and become more resilient and responsive to change.
At an operational level Olimpo, the Telsy’s Decision Intelligence solution, incorporates the OODA loop methodology (or Boyd cycle) supporting strategic and operational decision-making processes, allowing for efficient time-consuming analytical processes.
Based on the integration and correlation of internal and external sources of the organization, both structured and unstructured, and implementing Machine Learning and Artificial Intelligence algorithms, Olimpo allows to place the data and its analysis at the service of decisions and strategic and operational scenarios, favoring data-driven, time-relevant and above all informed choices, based on the analysis of data in real time, providing information and insights deriving from the correlation of a massive amount of heterogeneous sources.
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