Big Data continues to remain a black box term, even as we enter the second half of 2018. There are questions around it such as, how is it collected and what it takes to manage it and make it usable for business. The easiest way to explain this might be in the form of an analogy, something we could relate to our day-to-day lives.

Trading. Each day, fortunes are either made, lost, incrementally increased or decreased. It used to depend primarily on the boldness of the trader and even more so on the mood of the market. When modern technology emerged in the form of predictive analytics, it changed something.

In an instant, trading became a business of mathematical algorithms, custom indicators, integrated beliefs, and more. It became a thing that enabled the conversion of primary data into valuable insights, allowing you to make future projections based on both real-time and historical data. Predictive Analytics mitigated trade risk and ushered – in silence – data as the blood of modern society.

However, data does have its limitations. And predictive models merely predict, forecasting multiple outcomes based on a pattern of inputs. It is access to knowledge and is anything but infallible.

At the end of the day, the market is as human as the traders who make decisions behind these sophisticated, predictive models. It makes them both subject to the irrational.

Is it then truly valid to tout predictive analytics as the only big data trend to look out for in 2018?

An Overlooked Dark Horse in the Big Data Race: Prescriptive Analytics

Contrary to common perception, prescriptive analytics isn’t a specific type of analytics in and of itself. It’s more easily defined as an umbrella term for the several types of analytics utilized to improve decisions. Researchers have called it the goal of all analytics.

It could be used in two ways:

  • Inform the Decision Logic

Prescriptive Analytics provides input to a process. It could be as simple as aggregate analytics – say how much a customer spent on products the last couple of months – or as sophisticated as something that predicts the next best offer, or the degree of discount needed to entice said customer for future purchase.

  • Evolve the Decision Logic

Decision logics are known to become flawed or degrade over time, marking a sizeable chunk of historical data as unusable. But measuring and assessing the effectiveness and ineffectiveness of enterprise decisions could refine, or even redefine the decision logic to become better. It could be as sophisticated as embedded machine learning algorithms using decision logic to automatically decide what content is to be sent to target audiences.

In 2017, a Forrester Report outlined a warning to business enterprises – to stop wasting both money and time on unactionable analytics. With increasing use and value, prescriptive analytics has a significant role to play to drive analytics in today’s era. It is projected to make business decisions faster, enhancing enterprise efficiency and productivity.

The market for prescriptive analytics is expected to grow as well, with an expected value of $1.1 billion by the end of 2019. Elite organizations such as Google, General Electric, Pitney Bowes, and Pop Sugar among others seem to have caught on to this trend. Question is, will you?

Kishalay Gangopadhyay
Senior Director – North America Operations, Mastech InfoTrellis