Foundations of Operational Intelligence Engineering

Operational intelligence engineering enables evidence-based decision making by the systematic processing of data from various sources, leading to information relevant to individual, team, or organizational decisions. Although still in its infancy, operational intelligence engineering has the potential to advance decision making in any dynamic situation conducive to data-driven support, including finance, healthcare, disaster management, humanitarian negotiation, and climate response. Operational intelligence engineering focuses on designing appropriate architectures, solutions, methods, and tools. To that end, it defines the elements and generic high-level processes for operational intelligence and captures requirements for operational intelligence systems.

Systematic, comprehensive use of data for decision making requires careful requirements engineering to capture needs, metrics, constraints, and success criteria. Modeling, simulation, and scenario planning enable exploration of the problem space, identification of possible data sources, assessment of the ability to derive the needed information, and detection of knowledge gaps. A lack of data does not preclude a solution—transformation of high-level requirements into a data model, integration into the required format, and population from semantically aligned storages, text, or knowledge bases can lead to sufficient derived information. Proceeding in the opposite direction by identifying gaps between available information and what is needed and by establishing a research program for the creation of that intelligence is equally valid.