Maverick Says Data Without Intent Is Just Expensive Storage

The data game in business and technology has traditionally been an accumulation exercise. After all, if data truly was the new oil, then it stood to reason that the companies that gathered the most of it would inevitably dominate their industries.

Those days are over. Enterprise investments in data storage, infrastructure and pipelines are now serving as the foundation for activating the ever-growing streams of operational and payments information.

“The companies that leverage their data with intent is what will create an advantage over time,” Maverick Payments Vice President of Product Justin Downey told PYMNTS during a discussion for the April edition of the “What’s Next in Payments” series, “The Data Game.”

“How can you use data to purposely to build trust with your clients and customers, and even supply that data in intelligent ways back to them?” Downey added.

That distinction — intent — is fast becoming the dividing line between data-rich and data-effective organizations. Data is no longer a differentiator in itself; it is the raw material for differentiation.

While many businesses continue to accumulate information in sprawling systems, leaders are focusing on how data feeds continuous decision-making loops. What’s new is not the reliance on data, but the scale and speed at which it can be operationalized.

From Data Abundance to Decision Scarcity

Most large organizations now operate in a state of data abundance. Customer interactions, operational processes, supply chains and digital ecosystems all generate continuous streams of signals. Cloud platforms have made storage cheap and scalable, while modern analytics tools promise insights at unprecedented speed.

“We’ve always used [data] to inform our decisions,” Downey said. “We’re entering a new phase where AI can really help with the decisioning, moving things along.”

Rather than replacing human judgment, artificial intelligence is compressing the time it takes to reach it. In industries where speed and accuracy are tightly coupled, such as payments, underwriting and fraud detection, the ability to quickly synthesize massive datasets is already proving to be transformative.

For Maverick, much of this transformation is concentrated at the very beginning of the customer lifecycle where underwriting, which can often be seen as a compliance-heavy bottleneck, is being reimagined as a strategic lever for both growth and trust.

“The biggest gains for us happen before the first transaction even occurs,” Downey said. “What questions are redundant? What are things that we can move faster through so the good clients go through quickly. And if there’s any friction, let’s identify where it’s at and make sure that it’s smarter friction.”

The focus is on eliminating unnecessary manual steps while preserving rigor. Identity verification, for example, replaces redundant data entry, while intelligent escalation routes edge cases to human experts. The goal is a system that is both faster and more discerning.

“We want to make sure that experience is seamless and it’s a positive experience for the customer,” Downey said.

This approach reflects a broader shift in digital experience design. Instead of applying uniform checks across all users, companies are increasingly segmenting interactions based on risk signals. Low-risk users move seamlessly through onboarding, while suspicious activity triggers deeper scrutiny.

After all, as fraudsters themselves adopt automation and AI, defensive systems must keep pace.

“If you’re a company that isn’t using elements of AI, you’re going to fall behind. The reality is the fraudsters are using it, too,” Downey said.

Embedding Data Into the Fabric of Work

Still, despite the rapid adoption of AI across enterprises, Downey emphasized that maturity lies not in usage alone, but in governance. The next phase of the AI cycle may be defined by how well companies establish the guardrails necessary for ensuring that tools are deployed responsibly and effectively.

“It is not complete automation. It’s not a turn-it-on-and-let-it-go thing,” Downey said of artificial intelligence. “It is a turn it on, get the data to human experts, and build it into your workflows.”

By rapidly surfacing patterns and outliers, AI enables decision-makers to focus on higher-order questions like why something is happening, where risk is concentrated and how strategy should shift in response.

This is particularly relevant in heavily regulated industries, where compliance complexity can slow innovation. By embedding data into long-term innovation roadmaps, organizations can better anticipate regulatory shifts while continuing to scale.

“Your roadmaps are a year, two years, three years out,” Downey said. “That data can help inform the target that you’re trying to hit off in the distance.”

In that sense, as in many others, the competitive moat around data is one that’s increasingly no longer built on accumulation, but on intent.