The new consensus emerging across payments, financial services and digital commerce is that high-quality data is no longer scarce. Access is no longer differentiating. And on their own, even the most structured datasets are no longer valuable.
Across a month’s worth of conversations with PYMNTS for the April 2026 edition of the What’s Next in Payments series, “The Data Game,” a dozen leading executives shared why what matters today is the ability to turn data into decisions, instantly, repeatedly and intelligently.
Taken together, the executive discussions underscore how the ongoing operational shift from data accumulation to data-driven execution is redefining how companies compete, design systems and ultimately create value. Four key themes emerged:
In a world where everyone has access to similar data, the winners will not be those who know more. They will be those who act better. And in that world, data is becoming the platform, not the prize.
Speed of Decisioning Is the Data Differentiator
The first realization confronting executives is the deceptively simple one that everyone has data now. Data has evolved from being “the new oil” to becoming an operational commodity.
“Data itself is no longer the moat; it’s the speed and confidence with which companies can use that data to turn it into decisions at scale,” Akhil Gupta, VP of product at Green Dot, told PYMNTS.
But abundance has created a paradox. Companies are richer in data than ever, and yet often poorer in decisions.
“Most banks have terabytes and terabytes of data, but what they’re unable to do is act on that data in real time,” Deepak Gupta, chief product, engineering and delivery officer at Volante Technologies, told PYMNTS.
This is the new divide: not between data-rich and data-poor companies, but between those that operationalize data and those that simply warehouse it.
“The big difference between companies that use data well and those that struggle comes down to how they actually leverage the data that they’ve captured and turn it into actionable outcomes, in real time,” Dewald Nolte, co-founder and chief strategy officer at Entersekt, told PYMNTS.
Companies are being judged less by what they know and more by how quickly and effectively they translate knowledge into action.
“Accumulating data is great,” Ahsan Shah, SVP of Analytics and AI at Billtrust, told PYMNTS. “But it’s not going to make the big difference. The untapped value is: do you have a context layer? Do you have metadata? Do you have domain layers that differentiate your business?”
Better Data Context Drives Better Data Outcomes
In today’s environment saturated with information, latency and fragmentation have become the enemy of value.
“What real-time transaction data is doing is enabling us to have a forward-looking assessment,” Rinku Sharma, chief technology officer at Boost Payment Solutions, told PYMNTS. “The question used to be what happened. Now the question is, what should we do about it right now?”
Key areas such as fraud detection, credit underwriting, and transaction routing increasingly depend on real-time decisioning. A dataset that delivers insight seconds too late is, in many cases, operationally useless.
“Data can no longer sit in silos. It needs to be connected,” Chris Trainor, head of platform strategy and innovation at Paymentus, told PYMNTS. “Winning the data game is not about collecting more data … it’s about controlling identity, context and execution.”
At the same time, rapid but poorly informed decisions can degrade performance as quickly as slow and disconnected ones. The challenge, therefore, is not simply to accelerate decision-making, but to embed intelligence into the decisioning process itself.
“We use data to try and understand at an individual customer level or applicant level what is typical and what’s typical behavior,” Anita Chalkley, chief credit officer for Synchrony’s home and auto platform, told PYMNTS. “The system that we have built is able to ingest thousands of attributes from multiple different data sources and make a decision in less than six seconds.”
After all, what transforms data into actionable insight is the ability to contextualize it by linking disparate signals into a coherent narrative about a customer, a transaction, or a moment.
“How can you use data to purposely build trust with your clients and customers, and even supply that data in intelligent ways back to them?” Maverick Payments Vice President of Product Justin Downey told PYMNTS.
AI’s Role Is Around Execution at Scale
Importantly, data context is not static. It evolves continuously as new data points are generated. This dynamic nature requires systems that can update and recalibrate in real time. And if context gives data meaning, AI gives it motion.
“What AI has done is enable us, in that real time, in that moment, to process multiple input signals and create a composite view of whatever decision we’re making,” Kaushik Gopal, executive vice president, insights and intelligence at Mastercard, told PYMNTS, stressing that, “AI is only as good as the data that you have and how structured that data is.”
That sentiment was shared by William Fitzgerald, VP of Global Anti-Financial Crimes at WEX, who told PYMNTS that, “Data is the lifeblood of AI. Your capabilities with AI are directly tied to how governed and accurate and enriched and contextualized your data is.”
Instead of powering dashboards and reports, data can now drive live decision systems thanks to AI. But the executives all struck a consistent tone around the fact that AI is amplifying human judgment, not replacing it.
“AI is not replacing judgment … it’s augmenting it, at a speed and at a scale that was previously untenable,” said Green Dot’s Gupta.
“The best companies use data to make good customers move faster, while at the same time reserving scrutiny for high-risk situations,” Max Spivakovsky, senior director of global payments risk management at Galileo, told PYMNTS.
In practical terms, this means investing in infrastructure that supports continuous data flow, real-time processing, and integrated decisioning.
“Customers who are employing the most cutting-edge analytics and technologies are seeing a 10x improvement in onboarding times, approval times, credit decisioning,” Hal Lonas, chief technology officer at Trulioo, told PYMNTS. “If you’re just sitting on the data, you’re just not using it to your advantage.”
Data Is an Operating System, Not an Asset
Perhaps the most profound shift emerging from the executive discussions was a redefinition of what data actually is. While traditional, asset-centric views of data emphasized ownership, volume, and control, data is increasingly understood today as an operating system.
“The lagging organizations treat the data as a storage problem while the leading organizations actually treat it as a decisioning system,” said Galileo’s Spivakovsky.
It is not something companies possess; it is something they run on.
“When a supplier asks why a transaction was routed in a certain way, they deserve an answer,” Boost’s Sharma said. “The companies pulling ahead are using AI and payments as a trust-building and growth engine.”
Underlying all of these changes is a deeper structural shift: the emergence of continuous learning systems.
“If you’re able to collect data across different channels, the speed at which you can look at the data and make sense of it is so much more powerful,” Entersekt’s Nolte said. “What was relatively difficult or very slow just a couple of months ago, you can do relatively quickly right now.”
Instead of asking how much data they have, companies are asking how effectively their data systems enable them to operate. The focus shifts from accumulation to functionality, from storage to orchestration.
“We used to say, ‘You’ve got to conform to these six things.’ Now, we’re getting into: give us the ocean,” Billtrust’s Shah said. “The diversity of data alongside this AI layer of context is the most powerful asset for any company.”
