Predictive Analytics – The Need for Future Forecasting (Part 1)

Trying to predict the future is like trying to drive down a country road at night with no lights while looking out the rear view mirror. ~ Peter Drucker

The recent geo-political unrest and industry uncertainty over the past two years have brought to the forefront the need for better scenario forecasting in both commercial as well as public sector entities.  Things that cannot be predicted, with low probability and high impacts to business success and personal livelihood, stand out in stark and bold contrast to conventional business cycles and social regularity.  Whether these “fat tail” risk events as described by colleague Ian Bremmer will occur at increasingly frequent events, whether it’s a metaphysical “shif” in our personal consciousness, or whether it’s an increasingly angry Planet Earth expressing its displeasure with our unsustainable behaviors, few can argue that the need for a “better crystal ball” will begin to separate winners from losers in the market place.

Trying to predict the future is tough when you are driving down a country road looking out the rear view mirror.

So what does better forecasting mean?  To many it means doing what they have always done with better precision.  Reducing errors, scrap, what the Japanese call muda to create lean operations, leads to significant efficiency and productivity.  But even the most lean operations can’t predict the impact of a massive earthquake and tsunami, nor the impact of rating companies letting financial industries run amok and make bad decisions impacting the basic level of money supply in the US economy.  Much like Peter Drucker’s comments, a better crystal ball means that we need to stop living in the past and learn to look into the future.

Granted, the field of analytics has come a long way in terms of function, use, and ability.  I have written a book and numerous articles on the use of performance management best practices and approaches, where organizations are leveraging their asset in available information to make better decisions and craft more meaningful strategies.  Technology partners such as SAP and others have boldly moved into making the engine to conduct complex computations at light speed, even to the point of making these systems nearly real-time.  This allows decision makers the luxury of multiple complex scenario analysis over a morning cup of coffee (literally).  But still this process is limited by the information that we already have and not a predictive view of likely and unlikely scenarios in the future.

Over the next two months I’ll be discussing the need for future forecasting – the ability to take a truly closed-loop approach to performance management – and how this can be applied to strategic planning, operations and work activities in the organization.  It’s an exciting field which returns us to the more simple and commonsensical days of Peter Drucker where we can enjoy the country road without worrying about whether our rear view mirror is cracked.  The front windshield is so much wider and the view ahead is so much, much clearer.

To learn more about enterprise performance management, including risk-adjusted strategies, consider reading my book available at Galileo Press or several articles available via SAP Experts publications (subscription required).



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3 responses to “Predictive Analytics – The Need for Future Forecasting (Part 1)

  1. Bariki

    I am really excited for this series because only if we can make a better forecast then we can allocate our resources wisely. I think, forecasting is a necessary skills and tool for Manager and Business people.


    • Thanks, Bariki. What we are seeing is that not only is there a convergence of technology that can allow a framework for integrated planning, but also the industry and work-based function views that set the likely scenarios of where a forecast go (for example, pessimistic, unlikely, likely, optimistic, and my favorite “Herculean”). Making the planning process real-time with in-memory analytics means more time for greater, deeper and more specific sensitivity analysis and scenario planning.


  2. Pingback: Predictive Analytics – Future Forecasting via Commodity, Industry View (Part 2) | The View from C-Level

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