As big data gets bigger, it’s also getting smarter — see why predictive analytics is the most powerful part of any big data strategy.

If you could have one superpower, what would it be? The ability to fly and superhuman strength are fairly popular answers, but for people truly interested in business, the most likely answer would be this: the power to see into the future.

That’s what the explosion of predictive analytics tells us, anyways. The collection and growth of big data has empowered us to bring about a world of insights, and as Business2Community argues, the biggest of these insights are drawn through this new, algorithm-based discipline.

Granted, predictive analytics doesn’t give us exact, foolproof predictions of the future, but it can provide high insights on the probability of a given outcome, as well as help us bring about the ones that are favorable. While it’s not quite the equivalent of a crystal ball, it’s enough to ensure more profitable initiatives, and avoid disastrous mistakes.

Methods of Predictive Analytics

Predictive analytics can happen in a number of different ways to achieve different ends. One common technique is data mining, a method commonly used by the banking industry to help predict customer churn — when a customer leaves from one bank to another.

Data mining aims to extract knowledge and insights through the analysis of humongous amounts of data analysis that depends primarily on the use of modeling techniques. Using decision tree J48 (which uses C4.5 or 5.0 algorithms), we can better understand what causes splits within the data.

Essentially, a J48 decision tree takes all the decisions that banking customers make and tries to identify the ones that happen right before a customer leaves. Once the algorithm has found the strongest predictor for churn, data is segmented, drilled down, and insights can be taken away that can effectively predict when and why a given customer is about to defect to a competitor.

Similarly, logistic regression modeling can also help to predict churn. Logistic regressions work by basing the binary outcome (1 = customer churned, 0 = did not churn) on a wide set of possible predictors to best anticipate whether they’ll churn or not. Those predictors could include customer age, their account type (checkings, savings, business checking, etc.), their salary, and the number of card payments. By doing this, we can predict the probability of a churn, and therefore see the cost/benefit of a bank making sure that customer does not leave.

Applications and Limitations

Predictive analytics has become a popular trend primarily because of its practically limitless applications. They’re hugely related to the success of what we call algorithmic businesses: Netflix uses it to predict which movies you’ll enjoy, and Amazon uses it to recommend products. It’s what drives the ability of dating sites like eHarmony to predict good romantic matches, of websites to predict what ads you’ll click. It can even be used by doctors to predict treatment options.

Again, predictive analytics doesn’t provide any guarantees, but it can increase the likelihood of something occurring, and that’s no small asset. For instance, if you made a bet and I told you I could increase your chances of winning it by 25%, you’d jump at the chance. Even if 25% doesn’t seem like a huge value on its own, it can lead to much bigger gains. It’s like the process of elimination: you’ve got a better shot at passing a true-or-false test than a multiple choice one.

As big data gets smarter, it’s likely that more and more businesses will shift their focus away from the drudgery of data collection and towards sharpening their algorithms.