Editor’s note: The author is presenting a webcast on Tuesday, June 17, about how to combine customer, mobile, and social data to increase revenue. Data Informed is hosting the event. Click here for more information and to register.
Analyzing data is nothing new. Organizations have been doing this for ages. In the past, it was called Business Intelligence or descriptive analytics. It involved looking into the past, from one minute ago to several years ago. The objective was to answer the question, “What has happened within our organization?” In the past few years, we saw the rise of big data and predictive analytics. This involves analyzing vast amounts of data to predict what is about to happen.
The future of big data, however, lies with prescriptive analytics. The objective of prescriptive analytics is not only to predict future outcomes, but also to make recommendations based on those outcomes. In focusing on the what, when, and why of future events, it attempts to answer the questions, “Now what?” or “So what?” and it completely changes the game of big data.
Prescriptive analytics can be seen as the last step in really knowing your organization. However, it is still really in the beginning and, in the 2013 Hype Cycle of Emerging Technologies by Gartner, it was called an “Innovation Trigger” that will require another five to 10 years to reach mainstream. Currently, it is estimated that only 3 percent of companies use prescriptive analytics, and even those do it with structured data only.
Despite being so new, prescriptive analytics promises to be extremely powerful and accurate in its predictions. Prescriptive algorithms use a large variety of techniques, such as machine learning, artificial intelligence, and mathematical sciences, to understand the impact of future decisions and adjust actual decisions based on that outcome. This will drastically improve decision making as it incorporates future possible outcomes when making a prediction.
One of the most well-known examples of prescriptive analytics is the Google’s new self-driving car. During every trip, it makes multiple decisions about what to do based on predictions of future outcomes. For example, when approaching an intersection, the car needs to determine whether to go left or right and, based on various future possibilities, it makes a decision. So, the car needs to anticipate what might be coming in terms of traffic, pedestrians, etc. and the effect of a possible decision before actually making that decision.
In the oil and gas industry, prescriptive analytics enables analyzing a variety of structured and unstructured data sets (including video, image, and sound data) to optimize fracking in oil and gas fields and predict performing and non-performing oil wells. Another application is to use prescriptive analytics to optimize the materials and equipment necessary to pump oil out of the ground or to optimize scheduling, production, inventory and supply chain design to deliver the right products in the right amount in the most optimized way for the right customers on time.
With prescriptive analytics, things are being done a little bit differently. Algorithms are unleashed on data with only minimal rules telling them what to do. They are programmed in such a way that they can take over and adapt based on changes in established parameters, instead of humans controlling the algorithms. With algorithms optimizing automatically, their ability to predict the future becomes better with time.
Of course, in order to do that, massive amounts of data are required. Fortunately, we live in a world that is constantly creating exactly that. And although prescriptive analytics is currently the focus of only a few big data startups, prescriptive analytics has tremendous potential be a disruptive force on businesses and how decisions are made. It has the capability to fundamentally transform industries and make organizations more effective and efficient, as it is already doing in the oil and gas industry.
Mark van Rijmenam is the founder of BigData-Startups.com and a big data strategist who advises organizations on how to develop their Big Data strategies. As such, he is a well sought after speaker on this topic. He is presenting “Correlating Sales Data with Customer Behavior Data to Improve Sales and Customer Interaction,” a webcast hosted by Data Informed, on Tuesday, June 17.