Enterprises competing in digital environments must navigate through massive and continuous flows of data and information. Assuming a knowledge management framework, the marches between business intelligence and decision-making can be mapped by combining basic decision-making steps (Observation/Orientation/Decision/Action pattern) with the nature of inputs: data, information, or knowledge:
- Observation: data can be obtained from digital or business environments. It can be analysed and turned into knowledge (business intelligence) or directly matched with models of managed information.
- Orientation: reasoning (information) is applied to observations (data) and judgment (knowledge).
- Decision: judgment (knowledge) is applied to observations (data) and causal chains (information).
- Action: operations are carried out in symbolic and physical environments.
Decision-making processes could then be defined in relation with four key issues:
- Uncertainties (observations): facts are not gold nuggets ready to be picked from river beds; their meanings must be mined from data with proper labelling (thesauruses) and refining (models).
- Causalities (orientation): competing in digital environments means that root causes and rationales, once set upfront, must now be reassessed on a continuous basis. Dealing with the induced causal mazes can only be achieved through the integration of data analytics and information models.
- Risks (decisions): since business competition is by nature a time-dependent, nonzero-sum game, decisions should be weighed until the “last responsible moment,” when delaying commitments would reduce the range of options or the expected returns. That makes room for reassessments (knowledge) taking into account changes in uncertainties (data) and causal chains (information); that can be best achieved with knowledge graphs.
- Operations (action): decisions taken at the enterprise level usually involve sets of intertwined commitments and deeds whose efficiency is determined by the alignment of operations and organization. Set in digital environment, that alignment can benefit from direct observations feedback.
Typical scenarios could also be defined depending on the issues considered:
- 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, bypassing already secured information and causal chains (b).
- Automation: actions can be carried out automatically (without organizational accountability) based on reliable observations and confirmed causal chains (c).
- Support: decisions could be made and actions carried out without observable impact on environments (d).
As it happens, these options can be used to characterize decision-making categories:
- 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 affecting the capabilities of enterprises’ organization and systems.
That integration of the different dimensions will ensure the traceability (observation/orientation) and accountability (decision/action) of decision-making processes. More generally it will reinforce the fusing of individual experience and creativity into collective learning.