The question of “big data” resilience is now analogous to the global warming situation—it’s a reality with staying power.  

The payments sector has gone through unprecedented change in the last few years. Whether you’re a digital native or monolithic payment player, it’s time to make big changes to your big data strategies.

Whether you’re an issuer, acquirer, processor, merchant, or from another part of the payments network, you most likely are an excellent candidate for—and user of—big data analytics.

While there are many great examples of “data-driven” strategies within the financial services landscape, there is still a tremendous variation in terms of how firms are tackling big data. Companies using big data are largely focused on how to build for, draw insight from and act upon the data.

The adoption of data mining, analytics, and machine learning will continue to accelerate as a preferred method for managing fraud and risk in the payments sector.  Although the payments area is not the only financial sector that is turning to predictive and deep learning to combat fraud and risk, it definitely benefits from big data technologies that can process the sheer volume of data and provide faster and better insights. Beyond risk and fraud (debt relief) projects, increasing commitment will be and should be made into customer behavioral and transaction activity; the possibilities are just starting to be revealed in this sector.

Getting to the point where you can actually conduct advanced learning will be key to your success, and you’ll need to determine what systems (and people) are needed to track and analyze all of your data.

One of the reasons that the move to big data in the financial sector is largely incremental is that the demand for data scientists right now far exceeds supply. Another reason that these changes are incremental is due to the fact that larger, monolithic financial firms must take the costs and risks of change into consideration, and figure out how to integrate big data so that it improves both their business and their bottom line.

 “Big data” as a term may be frothy and over-hyped, but emergent payment technology will continue to accelerate, pushing its way into the future.

Essentially, big data will make it possible to provide payments anytime and anywhere. One of the larger steps will be choosing the right platforms to process the mountain of data quickly, and to provide analytic insight to drive some action, alert, or suggestion at critical influential interaction points within the payment lifecycle.

The other story in emerging payments is the fierce competition, not only with legacy providers, but also among startups. Major challenges include dealing with the complexity and variation of payment choices, and figuring out how to add value into the payment process. Evolving the path to “frictionless” or behind-the-scenes payment services are where larger bets are needed.

The bigger shifts will occur if the market is consolidated from top to bottom. If firms don’t actually consolidate through mergers and acquisitions, the service options will blend further, as the value-add puzzle needs multiple pieces to truly be effective. Inherently, the devil is in the data details, and capturing and reviewing it is critical to payment disruption.

The undercurrent here, which is tied to payment options—is with the multitude of current and forthcoming offerings. There are too many who fail to differentiate, whether they are new or established firms. What can help? It may involve improving the overall personal finance picture, or it may involve providing more timely and complete reward aggregation, thus improving the “path-to-purchase” for the consumer.

Sean O’Dowd is global financial services director at MapR Technologies