Big Data offers the enterprise a world of opportunity to improve processes and save money. But the aggregation of troves of data points is a monumental task – let alone sorting, analyzing and making sense of that information.
The potential cannot be ignored, however – particularly in the finance department, where information from accounts payable, accounts receivable, treasury and accounting hold the keys to insight into cash flow, predictable payment behavior and new opportunities to boost the bottom line.
The issue, said Alexander Rinke, co-founder and CEO of Big Data company Celonis, is that oftentimes, businesses approach the analytics process by relying on static data points. While the information is there, the process by which organizations try to access it is flawed.
"Fortunately for almost all big businesses," Rinke recently told PYMNTS, "there is a data stream that's already being created and aggregated within their IT systems, documenting every action that takes place, called event logs.
"These 'digital footprints' are ubiquitous and can be leveraged to create a clear picture of what's really happening within an organization's financial processes, no matter how many IT systems are linked together," he added.
A relatively new type of data analytics software, called process mining, uses these logs to "visually reconstruct a business process, instantly exposing problems like bottlenecks, patterns of rework activities and dangerous noncompliance," Rinke explained.
What that means for financial processes like accounts receivable and accounts payable is a view of data in context, rather than isolated data points.
"Data within AP and AR systems used to be difficult to analyze because static data points don't always tell a story," said Rinke, "but with process mining, organizations can finally see their information within the context of a process."
He noted that other types of analytics technologies are not necessarily able to identify problems unless analysts know to look for them.
A 2014 survey from treasury management software company Kyriba found that 65 percent of corporate treasurers say they are challenged by a lack of visibility into cash forecasting data inputs, making it their largest forecasting challenge. More recently, corporate T&E software firm Coupa found that the majority of chief financial officers lack full visibility into company spend.
Without a full picture of financial data in context, executives cannot know where to look for a problem, let alone identify one, said Rinke.
"The problems that you're unaware of can be the most insidious, because you can't solve a problem that you don't know exists, and that's where analytics solutions like process mining fit in," he said, adding that traditionally, process improvement initiatives have required manual interviews and workshops for analysts and consultants to uncover inefficiencies in various business processes.
Today, businesses of all sizes are still challenged by the prospect of making sense of Big Data.
A report by SAP published last September found that 74 percent of corporates surveyed acknowledged that agility is limited because of the size of their data landscape; 86 percent admitted they know they aren't getting the most out of the data available within the enterprise.
"Our customers are some of the biggest enterprises in the world, and these global organizations are constantly dealing with a near-incomprehensible degree of complexity in their processes," said Rinke.
"Up until now, it's been an enormous challenge for businesses to get a singular view of their financial processes," he continued. "Even the most efficient and well-designed processes at reputable firms can contain variations that lose them tons of money and cause big headaches."
Mergers and acquisitions, system upgrades and consolidation of IT source systems can also add to this struggle, contributing to a lack of visibility into financial processes like supplier payments and company spend.
That complexity has corporates missing out on money.
Failing to strategically use the information within IT systems has a significant impact on corporates' bottom lines, he said, and opportunities like capturing early invoice payment discounts, or identifying frequent late-paying customers, are missed "a lot more often than they should, and certainly more often than anyone wants them to."
Process mining has the power to identify far more than payers with a habit of settling the bill too late. The technology is increasingly strengthening compliance and risk mitigation in the enterprise, too, but with companies continuing to struggle with an overwhelming volume of data, it's also exposing their companies to additional risk.
There is an upside to this problem, however: Rinke noted that today's technological landscape is sophisticated enough to empower the C-suite, with the ability to identify patterns in their finances.
"We have seen businesses identify and prevent six-figure accounting errors from happening within the first week of getting started with process mining, simply by looking at their processes with total transparency for the first time," Rinke noted.
"It should frighten business leaders to think that a pattern of recurring billing mistakes or missed discounts could potentially cost them millions of dollars," he continued. "But it should comfort them that there are out-of-the-box solutions readily available to stop this sort of thing from happening."