Decision-making is supposed to be informed, and for enterprises that can be best achieved through ontologies.
The primary aim of ontologies is to weave together thesauruses, for the meaning of words (business domains), and models, for the representation of environments, organization, and systems. But ontologies can also deal with the epistemic status of representations: actual, past, planned, expected, hypothetical, virtual, fictional, etc. To take full advantage of these capabilities ontologies must form the nexus of business intelligence and decision-making, and that can be done by gearing knowledge- and decision-making processes.
This first part introduces the cogs between Observation, Orientation, Decision, and Action (OODA) on the one hand, Data, Information, and Knowledge on the other hand. The second part will detail the learning roundabout.
Assuming a knowledge management framework, the objective is to pair (as in DNA strands) the Observation/Orientation/Decision/Action (OODA) steps of decision-making processes with their Data/Information/Knowledge counterparts for content.
The Observation/Orientation/Decision/Action (OODA) loop provides a basic blueprint for decision-making processes:
- Observation: data can be obtained from digital or business environments. It can be analysed and turned into knowledge (business intelligence) or matched with models of managed information.
- Orientation: reasoning (information) is applied to observations (data) and judgment (knowledge).
- Decision: judgment (knowledge) is supported by observations (data) and causal chains (information).
- Action: decisions are carried out through a mix of operations (data) and organization (knowledge).
Decision-making sequences can then be characterized in terms of knowledge management and timescales.
Decision-making is often confused with problem-solving, namely how to pick a solution given a set of resources, typically people, information, financing, materials. That paradigm ignores the temporal dimension of enterprises’ decision-making which are made of interdependent commitments meant to be carried out across shifting backgrounds and overlapping timescales. That temporality makes room for changes in decision-making contexts, typically:
- Uncertainty (observations): facts are not gold nuggets ready to be picked from river beds; their meanings are mined from data and applications as determined by segmentation (models). Confidence, and more generally the quality of data, can thus be enhanced during decision-making processes.
- Causalities (orientation): competing in digital environments means that root causes and rationales, usually set upfront for problem-solving, can now be reassessed on a continuous basis, with causal chains improved through the integration of data analytics and information models.
- Risks (decisions): since business competition is by nature a time-dependent, nonzero-sum game, risks can be reassessed until the “last responsible moment,” when delaying a commitment would reduce the range of options or the expected returns. Such reassessment (knowledge) can take into account reduced uncertainties (data) and improved causal chains (information).
- Experience (action): decisions taken at the enterprise level usually involve sets of intertwined commitments and deeds whose efficiency is determined by the alignment of operations (data) with organization (knowledge). Set in digital environment, experience can benefit from direct feedback combining knowledge and observations.
As to make the best of these changes, basic sequences can be factored out according to their impact on control variables:
- Commitments: decisions can be made with suspended execution when delays can help with the uncertainty of observations, the reliability of causal chains, and consequently the adequacy of risks assessment (a).
- Routine: decisions can be carried out directly based on observed data, without a reassessment of already secured information and causal chains (b).
- Automation: actions can be carried out automatically (bypassing organizational considerations) based on reliable observations and confirmed causal chains (c).
- Support: decisions could be made and actions carried out without observable impact on environments (d).
More generally, these patterns can also be used to characterize decision-making horizons:
- Strategic: when commitments must be made without reliable cost/benefit assessments within the relevant time frame; such decisions must be backed by risk-management schemes covering for ill-fated turns of events.
- Operational: when observed data can be directly mapped to information and put to use as knowledge, thus allowing for routine decision-making.
- Tactical: when the execution of decisions can be improved by additional data and better traceability.
- Support: for actions meant to improve the capabilities of enterprises’ organization and systems.
That brings back the temporality issue: how to manage decision-making horizons in digital environments characterized by continuous changes in markets, competition, and regulations. The answer is to weave together decision-making and knowledge management.
The digital revolution comes with both constraints and opportunities for enterprises decision-making. On the one hand decisions with lasting commitments must be taken in shifting environments with blurred horizons; on the other hand the weaving of software components into business processes greatly enhance their adaptability, especially when AI and ML technologies provide learning capabilities.
Assuming that decision-making processes are supported by knowledge management (KM), they could benefit from learning capabilities provided by Machine learning and Knowledge graphs technologies, the former to help with data, the latter as a conceptual glue between environments and enterprises’ information models.
More specifically, the pairing of OODA steps with KM enable dynamic improvements of observations, reasoning, judgments, and experience:
- Observation: data is not given but obtained through designed apparatuses; the relevance and quality of observations (cf. data mining) can thus be improved (a) during decision-making processes; improvements can also be achieved through feedback (cf. process mining) from modus operandi (e)
- Orientation and decision: models and the reliability of business causal chains can be improved when actual outcomes can be compared to expected ones (b); the same approach can also be used for the assessment of policies (c)
- Action and observation: given a decision, modus operandi can be improved through the analysis of actual and expected resources and execution (d)
The pairing of decision-making steps with topics (mined data, models, policies, modus operandi) opens the door to learning processes.