Organizational decision-making is often framed as a process or ‘life-cycle’ which includes framing, verification, validation, and valorization. An example is the excellent, short work on making quality decisions by J. Hammon et al entitled ‘Smart choices’ (http://www.amazon.com/Smart-Choices-Practical-Making-Decisions/dp/0875848575 ).
It is often the ‘valorization’ process where decisions fail. The word is obscure outside Europe, but interesting and trending, as it implies ‘organizational operationalization’ in the broadest sense (management AND worker adoption). It is this later notion which is often so fraught and poorly implemented. Thus the strong need for leadership, governance, and processes to bring decisions to fruition.
When we are talking about ‘enterprise decision making’ (decision making in ‘firms’), we are increasingly talking about the integration of organizational decision processes and IT systems-based analytics. The former involves organizational decision architectures, processes, and organizational decision-making ‘culture’ (i.e. does management allow for decisions to be questioned on a ‘scientific’ or data analytics-based basis). The latter involves not only an available technical infrastructure (hardware & software), but also available expertise, as well as data and information management governence and best-practices.
MIT Sloan (working with IBM) has put out some interesting articles tackling ‘organizational anlaytics maturity assessment’, for example Kiron, D., R. Shockley, et al. (2011). “Analytics: The Widening Divide.” MIT Sloan Management Review (Special Report) (http://sloanreview.mit.edu/feature/achieving-competitive-advantage-through-analytics/ ).
The perspective offered by Kiron, Shockley, et al is of interest to those seeking holistic maturity assessments for modern enterprise, to the degree that organizational structure, processes, and culture must integrate with IT analytics infrastructure (itself a fusion of technology and organizational processes).
In the ‘big picture’, my own perspective is that we can increasingly consider modern enterprise to be a ‘cyborg decision machine’, that is, a hybrid of human and technically-facilitated processes aimed at efficient ‘sense’making’ with the goal of efficiency-optimizing decision-making.
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