The ability to foresee the future would certainly be the ultimate competitive advantage. In reality though, no business has a crystal ball for making critical decisions. That’s why all critical business decisions have always carried a certain amount of risk. This risk has always and will always be part of the competitive game.
While the elimination of risk is impossible, big data is forging a pathway for businesses to reduce it. Predictive analytics has been in use for a number of years and big data Hadoop is helping improve it’s usage and improve outcomes in the process. With big data, no longer is the size of the sample set a limiting factor, as a lot more data is available from a modeling perspective.
At it’s core predictive modeling is an algorithmic method to forecast results depending on the scenario. With enough data and the right tools, predictive analytics can be a reliable tool in the hands of decision makers.
Predictive analytics may seem like a new or foreign concept to the average consumer. In reality, it is regularly present throughout most of our daily lives. Here is an example:
Trucks are the lifeblood of urban sustainability. They transport everything from food to fuel. Recently Jeff Foster Trucking was planning a 50 truck expansion to their fleet. An expansion of this size would cost them an estimated $6 million. Part of these purchased vehicles replaced existing vehicles running on older technology. When evaluating the selection of new potential trucks, management looked at several criteria. A leading factor was the trucks’ fuel economy. Often businesses in this industry make fuel economy decisions based on differences of 0.1 to 0.2 miles per gallon. To make the most profitable decision, Jeff Foster looked to their data. They started by using their historical data on fuel performance across their entire fleet. Electronic control modules sourced the data which was segmented down to the first quarter of 2013. While this was a great start to enhance corporate decision making, it wasn’t good enough to stand on its own. Electronic control modules have less-than-reliable accuracy. Other human factors also contributed to variables that weren’t calculated with the existing data. This is where Jeff Foster turned to predictive analytics.
With predictive models they integrated variables such as driving behavior, weight and trailer types. They used this model with data for the first nine months of 2013. The results were staggering. As it turned out, the discrepancy between fuel economy was stark between manufacturers. One manufacturer had significantly lower miles per gallon than what the company expected. Moving forward, Jeff Foster and many other trucking companies will be able to reduce costs and emissions with the added analysis provided by predictive models.
Predictive analytics plays an important role in the effectiveness and risk management of many businesses today. And with the affordability and availability of new big data technologies, its use has only begun.