The use of prescriptive analytics is at its highest ever level, and Gartner predicts that the market will reach $1.1 billion by 2019 - 22% CAGR from 2014. This is clearly significant growth, yet it is still dwarfed by the use of predictive analytics, a technology that is surely of less use. To put the growth of prescriptive analytics into context, MarketsandMarkets has estimated that the global predictive analytics market will grow from $2.74 Billion in 2015 to $9.20 Billion by 2020 - CAGR of 27.4%.
Prescriptive analytics is the next stage of predictive analytics. It looks at the actions that can be taken as a result of the insights revealed by predictive analytics, analyzing current data sets for patterns and then evaluating what would be the outcome of the multiple scenarios that could be enacted based on the decisions made around leveraging the data. By doing so, it gives decision makers more information about the impact of each option based on specific key performance indicators, removing a significant element of risk and leading to faster decisions.
Google’s driverless cars are a good example of prescriptive analytics at work. They must make multiple decisions about their next step based on predictions of future outcomes. So, for example, when turning, the car must anticipate everything that a normal driver must anticipate - pedestrians, traffic - and take the action (moving out into the road) based on the impact that decision will have.
While the disparity between the predictive analytics market size and that of prescriptive analytics is simply explained by the relative newness of the technology, it does not explain why growth would not be far higher in a technology that is so clearly more beneficial. Mick Hollison, CMO of sales-acceleration software company InsideSales.com, argues that, ‘Predictive by itself is not enough to keep up with the increasingly competitive landscape. Prescriptive analytics provide intelligent recommendations for the optimal next steps for almost any application or business process to drive desired outcomes or accelerate results.’ Despite this, just 10% of organizations currently use some form of prescriptive analytics, according to Gartner, which will grow to 35% by 2020.
There are a number of possible reasons for the slow adoption. Many organizations are still skeptical of prescriptive analytics because it is still so unfamiliar. There is not the understanding there is with predictive analytics, and the pattern-seeking technology and machine learning algorithms that enable it are perhaps more complicated. There may also be some confusion as to what prescriptive analytics is actually bringing to the table. Many organizations have already invested heavily in predictive analytics over the last few years, and another wave of investments into a tool where they still feel human control is necessary - such as decision making - is likely to appear unwarranted. However, decision making processes need to change, as they are now required quicker than ever. Companies looking to implement prescriptive analytics need to ensure that employees understand that the system works for them as a complement to their work rather than a replacement. The reasoning behind the recommendations must also be made clear, as sometimes the logic behind machine-generated answers are not immediately obvious to humans.
Predictive analytics is still a highly useful tool, but companies that fail to take it to the next level are likely to lose competitive edge to those that do. Decision making processes need to be re-evaluated constantly with the pace of technological evolution now so fast, and prescriptive analytics are vital to this.