Decision Science is an emerging discipline with "Big Data" as its foundation.
Although decision making is both art and science; intuition and analysis, thinking fast and thinking slow. Due to the complexity and interdependence of digital nature, decision becomes more science than art, the whole purpose of analytics is to make better decisions based on data, big or small; wide or narrow; you can call it with any name like decision theory, science, and technology. The point is, how can it optimize decision making scenario and improve decision effectiveness?
Although decision making is both art and science; intuition and analysis, thinking fast and thinking slow. Due to the complexity and interdependence of digital nature, decision becomes more science than art, the whole purpose of analytics is to make better decisions based on data, big or small; wide or narrow; you can call it with any name like decision theory, science, and technology. The point is, how can it optimize decision making scenario and improve decision effectiveness?
Decision Engineering is not "just a new buzzword." It is a knowledge revolution for proactive structural decision simulation analysis and strategic decision analysis for crisis early warning and proactive feedforward reactors control against process operation uncertainties. The typical challenge seen with the traditional analytics approach is to arrive at insights, but not necessarily affect actual business decisions; and not always in a timely manner. Decision Engineering approach embeds analytics in actual business decisions - rather than leaving it to the receiver of insights to use or dump.
The role of a decision model is to systematize one's preferences and beliefs and identify their consequences as specified; Thus allowing critical comparison of one's holistic view to the consequences of the formally specified one. If the formal specification is reasonably close to the truth, this critical comparison is very helpful, because whenever you find a difference, you have the opportunity to improve either the intuitions (= an insight) or the model (= fix a bug or improve the logic). When the two points of view are reconciled, both are improved, and the model corresponds to the gut feel, and it identifies a choice with a rationale that works.
Improving decision quality is about reducing the uncertainties of the most variable elements; relative importance of various criteria, and estimates of future consequences of choosing various alternatives. There is the process of working with decision-makers to support their thinking through; subjectively, how they judge tradeoffs between choice criteria is more influential on decision quality than marginal improvements in the choice of multi-factor attribute analysis methods. Secondly, presenting forecasts of outcomes in value distribution terms contributes to creating a proper awareness of the reality that in many decisions, good decision making merely reduces the risk of error by modest amounts, in the face of an uncertain future environment.
Decision performance is based on the effectiveness of managing the life cycle of data --> analysis --> decisions --> performance. But a lot of people get a bit caught up on the analysis as if this is the end of the process: data --> analysis --> conformance. No, if the analysis doesn't lead to performance, it's rubbish irrespective of the apparent eloquence. This actually represents a problematic situation if a statistical approach cannot deliver the expected return; people might start to question the competence of the researcher rather than suitability of the approach.
Any decision-model runs the risk of creating a false sense of precision and confidence. Any NPV model, regardless of how cleanly constructed it appears, is typically based on a set of assumptions about the financial value of future cash flows that are in most cases a reasonable guess. It's nearly impossible to escape the bias and subjectivity in any type of decision. A simple multi-attribute rating technique try to solve this problem by using weights based on moving from the worst to the best in various criteria, even for a combination of rational and emotional criteria. Without that, the analysis is working in the realm of tangible measures rather than the preferences it is intended to embody.
Decision science is an emerging discipline, following the Big Data footstep, but both are the means to end, the end is to achieve business goals by making the right decision at the right time by the right person; from decision performance to business performance, the decision making is both hard science and soft touch.
0 comments:
Post a Comment