Decision science and Analytics

What do some of decision science best practices say?
•    Look beyond the “spotlight” – One of my INSEAD professors said
•    Realize that decisions are influenced by sub-conscious biases.
•    Remain open
•    Use numbers to analyze the decisions from as many angles
•    Don’t rely on only yourself for decisions

Supporting business decisions through analytics
•    Look for hidden trends and patterns in large data points which is beyond the obvious and the earlier looked at report pattern

  •    If you had been looking at absolute top line and bottom line values to take investment decisions, look also at future top line and bottom line trends.  Past performance might not be indicator of future investment return value
  •     Use analytics to have a peek at the future

•    Get data validation as much as possible on gut decisions

  •     Business decisions right from investment options to reward choices to be supported comparing data points like net present value of investments and efforts

•    Do sensitivity analysis on data to understand the chances of change in decision required at different decision points

  •     You can create multiple go-no go decision points and run sensitivity analysis at each point  to understand the stability of your decisions in changing scenarios

•    Create a culture of data-driven decisions, supported by the people, processes and technology
As challenging it might seem, this can be done and requires senior management commitment.
–    People – Gather people comfortable with data and business insight together
–    Process – Ensure decisions are validated with data points and processes                                 ensure capture, dissemination and presentation of data. Ensure balanced 360                         degree view of information
–   Technology – Use technology to capture and process as many varied areas of                          data points as possible

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