What Is Decision Intelligence?

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

In today’s increasingly complex 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 evolving customer needs, succeed among new digital competitors, and become more resilient and responsive to change.

TelsyOlimpo, Telsy’s platform dedicated to Decision Intelligence, is based on the OODA loop methodology (also known as the Boyd cycle), supporting strategic and operational decision-making processes and enabling the optimization of time-consuming analytical workflows.

Based on the integration and correlation of internal and external sources—both structured and unstructured—and implementing Machine Learning and Artificial Intelligence algorithms, TelsyOlimpo makes data and its analysis serve strategic and operational decisions and scenarios. It supports data-driven, time-relevant, and most importantly, informed choices based on real-time data analysis, providing information and insights derived from the correlation of a massive amount of heterogeneous sources.

The platform offers functionalities for various use-case verticals, designed for companies—whether large or small and structured—and for individuals who are highly exposed in the media or hold prominent public roles, and who are often particularly vulnerable in terms of sensitive and personal information.

Through an interface that is both intuitive and comprehensive, TelsyOlimpo delivers a strategic approach that combines data, predictive models, and human expertise to support more effective decision-making by analyzing large volumes of information, simulating future scenarios, and providing evidence-based recommendations.

This enables organizations and teams to make faster, more informed, and measurable decisions, reducing risks and uncertainties, and transforming data into strategic actions and concrete results.

Learn more about our Decision Intelligence services or contact us at contact@telsy.it.