Beyond Payments: What B2B Businesses Can Unlock With Clean Data

In today’s digital world, effective use of data defines increasingly defines business success.

That makes it critically important for businesses to define what both good data and bad data looks like.

Data isn’t created equally, making enterprise-level data standardization key to fueling decision-making, unlocking operational efficiency and enabling sustainable, strategic growth. Conversely, dirty data — characterized by inaccuracies, inconsistencies and omissions — can pose significant barriers to progress.

B2B transactions especially often involve multiple stakeholders, including suppliers, manufacturers, distributors and financial institutions. Data standards facilitate seamless interoperability and integration between disparate systems, ensuring that information flows smoothly across the entire value chain. This interoperability is crucial for automating processes, reducing manual interventions and minimizing errors.

But despite the clear advantages of data standards, many businesses continue to grapple with the challenges posed by dirty data, which can have far-reaching and negative impacts on B2B operations and payments.

Read more: How Data-Rich Environments Can Benefit B2B Payments

The Growing Imperative of Data Standards

Accurate data is the foundation of actionable insights, enabling businesses to identify trends, predict outcomes and make informed decisions.

Dirty data can lead to operational disruptions by causing errors in B2B transactions, miscommunications between stakeholders and delays in processes. For instance, incorrect billing information or inaccurate inventory data can commonly result in shipment delays, payment disputes and strained business relationships.

PYMNTS Intelligence in the inaugural edition of “The 2024 Certainty Project Report” found that uncertainty, particularly around payments, costs middle-market companies more than $20 million on average. Many of these uncertainties stem from incompatible technologies, manual data entry and the complexities of legacy systems that result in poor data quality.

“There’s a lot of messiness around payments, particularly very large B2B payments that might house hundreds or thousands of invoices with hundreds of associated line-item details,” Boost Payment Solutions founder and CEO Dean M. Leavitt told PYMNTS. “Large enterprises on both the AP and AR side are looking for ways to automate those processes, digitize them and reduce their cost as well.”

Ultimately, managing and rectifying dirty data can be resource-intensive and costly. Organizations must invest in data cleansing, reconciliation and validation processes to address inaccuracies and inconsistencies. These efforts divert resources from strategic initiatives and can erode profit margins.

“Businesses are becoming much more aware and much more savvy about how to operate digitally,” James Butland, U.K. managing director at Mangopay, told PYMNTS in January, explaining that handling B2B payments offline comes with “a lot of admin, a lot of costs and time lost.”

Read more: Why Data Analytics Represent the Frontier of B2B Decision-Making

Data’s Impact Across Compliance and Security

Regulatory compliance is a significant concern in B2B operations and payments, especially in industries such as finance, healthcare and manufacturing. Data standards help organizations adhere to regulatory requirements by providing clear guidelines on data handling, storage and sharing. Additionally, standardized data practices enhance security by reducing vulnerabilities and ensuring that sensitive information is protected.

Dirty data can expose businesses to regulatory and compliance risks by failing to meet data accuracy and reporting standards. Non-compliance with regulations can result in legal penalties, reputational damage and loss of trust among stakeholders.

To unlock the full potential of data in B2B operations and payments, businesses must prioritize the implementation of robust data standards and invest in data quality initiatives.

“A typical client is one that is focused on having their data reporting automatically fed into the ERP or TMS,” Meg Garand, head of CashPro Payments and CashPro API at Bank of America, told PYMNTS, noting that while, historically, that process has taken four to six weeks, “we’ve had numerous clients go live with current day reporting and or previous day reporting across all of their accounts within one week.”

As Bloomreach CFO Ninos Sarkis told PYMNTS in an interview posted in November: “You can’t control the geopolitical tensions, but what you can control is making your business stronger and more resilient during these times so that you come out the back of it a stronger company. … There’s a lot of relatively low-hanging fruit to make a business more efficient, more scalable and more automated.”

Still, just 25% of CFOs reported using analytics to forecast trends and gain insights, per PYMNTS Intelligence — a significant drop over the past several months, and one that hints at an emerging emphasis on process automation, new workflows and dedicating human resources effectively to mitigate uncertainty.

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