Study the past, if you would divine the future (Confucius)
Predictive analytics sounds like every manager’s dream come true. The premise is simple: look at the data on past behaviour and performance as an indicator of future behaviour and performance, and react accordingly.
Predictive analytics are not new. Indeed some would claim that the concept of predicting the future using history can be traced back to Lloyds of London in the 16th century, when underwriters took into account the past history of ships’ masters, the vessels they were commanding and the ships’ historical journeys when deciding to underwrite insurance for a future voyage.
In the business world of the 21st century, historical data is available at an unprecedented level of detail and depth. Enterprise business intelligence vendors such as MicroStrategy have embedded a wide range of statistical functionality through data discovery and visualisation tools. Desktop computing power is freely available, which will rapidly calculate complex algorithms against vast amounts of data.
Leveraging predictive analytics for planning
image: Garry Knight, Flickr
Is it true that you need an advanced set of skills in areas like statistics, modelling and algebra? Skills that the average person involved in planning simply doesn’t have?
You can’t simply throw all your available data to the predictive analytics wall and hope it churns out something that makes sense. If you do, it will let you down every time. You have to choose which of the thousands of available algorithms would best suit the data you have to provide the best prediction.
Therein lies the nub of the problem: time is precious, your budget submission date is looming, and you just don’t have the time and perhaps the skill to evaluate which predictive algorithm you should use.
Six Degrees: a better way
Six Degrees delivers the power of predictive analytics to your planning process, quickly and with confidence, and without the need to be a data scientist. How? By using existing historical data and freely available computing power to help you quickly determine which approach and algorithm to use.
Choose the data you wish to apply predictive analytics to.
Use Six Degrees functionality to run and compare a range of algorithms on identical data.
Choose the preferred algorithm and create the forecast.
Refine the prediction, if needed, in the light of new knowledge which history does not know or show.
Finally, present your forecast with confidence!
Always remember the black swans
The inability of predictive analytics to account for “unknowns” is known as the black swan theory. It gets its name from the assumption (back in 16th century London) that all swans were white, because all depictions of swans and all knowledge of swans to that point showed that they were. But in the latter half of the century, when an explorer in Australia saw a black swan, the theory was disproven. All swans were not white.
But how can you possibly account for what you don’t know? You can’t, and therein lies the challenge. Predictive analytics can’t account for all of the unknowns, and even trying to drops you head-first into a bunch of complicated, complex models that even the best statisticians can’t wrap their heads around.
Six Degrees recognises that predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature. Providing the ability to efficiently enhance the forecast with knowledge of planned events that will likely affect the probable forecast ensures that information unknown to the algorithm can be captured to improve the accuracy of the forecast.