Your Business Has Its Payments Data. Now What?

A company’s transactions aren’t just numbers on a spreadsheet. Those numbers, it turns out, can be kernels of actionable intelligence.

In today’s digital operating landscape, if businesses are still treating payments data as just another line item, they could leaving money on the table. Smart business owners are mining their payments data for real-time insights that drive revenue, reduce fraud and sharpen customer engagement.

But there’s a catch: data accuracy declines at a rate of around 2% every month. This translates to an annual decay rate of 22.5%, meaning that if your firm’s data strategy is “set it and forget it,” you could be losing out big time. Without active maintenance, today’s insights could be riddled with inaccuracies tomorrow, making it an unreliable foundation for decision-making.

For enterprise leaders seeking to maximize real-time insights, unlocking trapped value in payments data requires a structured, disciplined approach. The challenge isn’t just collecting data; it’s refining it, structuring it and extracting strategic intelligence before it loses relevance.

Read more: Payment Data Is Back-Office Automation’s Hidden Gold Mine

At first glance, payments data may seem straightforward: a record of transactions between businesses and consumers. But beneath the surface lies a tangle of fragmented, redundant and often inaccurate information. Payment gateways, merchant acquirers and financial institutions each store transaction records in varying formats, creating inconsistencies that can cloud decision-making.

Left unchecked, these discrepancies can undermine fraud detection, obscure customer spending patterns and misinform pricing strategies.

For companies eager to turn raw data into actionable intelligence, the process starts with rigorous data assessment and cleansing. Artificial intelligence (AI)-powered tools can help eliminate redundancies, enrich incomplete datasets and normalize transaction identifiers to ensure consistency across multiple systems.

“AI and real-time enriched data represent a leap forward,” Sherri Haymond, co-president, global partnerships at Mastercard, told PYMNTS.

That data must also be protected. According to a December PYMNTS Intelligence The AI MonitorEdge Report, “COOs Leverage GenAI to Reduce Data Security Losses,” over half of COOs (54%) have turned to GenAI for improving their data ecosystems.

But technology alone isn’t enough. Businesses must also break down internal data silos that prevent cross-functional insights from emerging.

A major challenge is interoperability, or ensuring that data from various payment providers and banking partners can be integrated into a unified system. Without this, companies risk missing valuable connections between payment behaviors and broader business trends.

Cloud-based repositories and real-time data pipelines are increasingly becoming the standard for enterprises looking to consolidate their payments data into a single, accessible source.

For example, SAP and Databricks last month announced a new partnership and product that makes it easier for customers to unify all their data by combining their SAP data with the rest of their enterprise data.

See also: CFOs Embrace Data Clouds Amid Shift Away From Pure-Play Record-Keeping

A PYMNTS Intelligence report, “The Platform Business Data Readiness Survey: How Real-Time Data Can Drive Growth,” created in collaboration with Fiserv, examines the growing importance of data readiness for businesses aiming to optimize operations and unlock market potential.

Once structured, payments data can be transformed from a historical record into a predictive tool. Machine learning algorithms can sift through transaction patterns to identify fraud risks, detect shifts in consumer spending habits, optimize promotions and streamline operational efficiency.

Companies, with the support of unlocked payments data, can now look into leveraging transaction insights to negotiate better terms with suppliers, refine marketing strategies and even create new data-driven services.

Retailers, for instance, are using payments data to identify high-value customer segments and tailor personalized shopping experiences. Meanwhile, financial institutions are employing it to offer predictive financial advice and dynamic credit scoring models. The more refined the data, the greater its commercial potential can be.

Treasurers, in particular, can help to bridge the gap between financial priorities and technological possibilities. According to PYMNTS Intelligence, a full 77% of treasurers believe that at least one department in their organization would benefit from closer collaboration with them. Within the consumer packaged goods (CPG) industry specifically, that number jumps up to 88%.

As the volume of digital payments continues to soar, the businesses that succeed will be those that view payments data as a living asset, one that requires ongoing maintenance, governance and strategic application.

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