“Companies have tons and tons of data, but [success] isn’t about data collection, it’s about data management and insight.”

– Professor and Head of the Data for Business Performance Institute Prashanth Southekal

It’s easy to amass information. In fact, these days we’re inundated with it from all directions. The challenge for companies is to ensure that it is accurate and that they know how to transform it into actionable insight. 

Garbage in, garbage out

To paraphrase the American satirist Tom Lehrer, a company’s data is a bit like a sewer. What you get out of it depends on what you put in. You can collect all the figures in the world, but if they aren’t reflective of reality, they won’t do you a lot of good.

The problem? Many companies aren’t all that confident that the information they have is that accurate.

According to Experian’s 2021 Global Data Management report, 55% of business leaders don’t fully trust their data assets. 

But it doesn’t stop there: 69% of workers say that they or their CEO has made major business decisions based on inaccurate information. 

Those are shockingly high numbers with wide-ranging implications. Without an accurate idea of cash flow, for example, it becomes almost impossible to engage in cash forecasting or planning for the future. On the accounts payable side, faulty reporting of what an organization owes and when it is due can lead to late payments, damaged vendor relations, and difficulty knowing the business’ true liquidity. Without reliable Days Sales Outstanding (DSO) or Average Days Delinquent (ADD) numbers, it is difficult to identify bottlenecks in the accounts receivable process. 

Well, there’s your problem…

So, what exactly is causing this bad data issue? For many organizations, it comes down to continued reliance on outdated processes and informal modes of operation.

In the absence of cloud-based automation, many finance departments are forced to work between multiple systems and manage manual inputs, which are prone to mistakes. That information feeds vital decisions in turn, which, when based on erroneous figures, only deepens the impact. When you follow the process through, it’s easy to see how something as simple as a single typo can lead to exponential problems. 

An ounce of prevention is worth a pound of cure

The 1-10-100 rule suggests that if it costs $1 to prevent an error, it costs $10 to fix it, and $100 in the event of failure. Investing the time and money to improve data accuracy is far less expensive than attempting to clean up the mess later. Luckily, there are some practical steps that organizations can take to do just that.

Without adopting an automation solution, businesses can still improve accuracy by establishing consistency in the data input process. A key first step is to eliminate multiple points of entry and centralize where data is stored. The average company with 200-500 employees uses an estimated 123 software as a service (SaaS) applications, while companies with fewer than 50 employees use as many as 40. The lack of communication between these disparate sources increases the odds of errors.

It is also important to create a formal approval process so that every invoice is treated the same. Security measures, such as requiring multiple sign-offs for invoices over a certain dollar amount, are also beneficial. This process keeps managers accountable for ensuring data quality and conducting checks as an invoice makes its way through the system.

Another simple step is to eliminate any existing informal communication channels, which make it difficult to audit the process and make any necessary changes. Departments should do away with practices such as using e-mail to request invoice approvals or relying on paper-based expense receipts. 

Let the Technology Do the Heavy Lifting

There are a few technology solutions that use automation, machine learning (ML), and artificial intelligence (AI) to make your AP and AR teams’ work both easier and more efficient.

Adopting a document management system can drastically increase data quality. Software that uses Optical Character Recognition (OCR) extracts information from scanned documents or images, and the best solutions are up to 98% accurate.

That said, there are limitations to what OCR can accomplish. Numbers and information require context to be useful, and the technology does nothing to consolidate or organize the data in a way that leads to actionable insights. 

Intelligent AP and AR solutions can bring significant value here. On the accounts payable side, Beanworks’ solution leverages machine learning to extract information from invoices, receipts, and PDFs. With complete visibility from the time an invoice is received until it is paid, it provides companies with a more accurate idea of what they owe and how long they can hold onto their money.

For accounts receivable, YayPay takes AI technology and uses it to analyze customer payment behaviors, providing insight into which accounts are likely to pay late. This can help create more accurate cash forecasts, as well as guide AR teams’ activity, allowing them to get ahead of accounts that may have problems paying their invoices.

Automation also lowers the likelihood of invoice exceptions, which take time and resources to resolve. In AP, this means you are less likely to pay invoices late, avoiding late fees, fines, and damaged vendor relationships. In AR, it helps ensure that you are paid on time and that you aren’t feeding customer dissatisfaction. 

When considering how to clean up your data and avoid making business decisions with faulty or misleading data, your smartest bet is to find the right AP and AR platforms that bring efficiency and ease to a complicated process.

For more information on how to improve data visibility and accuracy, read the white paper “Best Practices for For Finance Leaders to Improve Data-Driven Decision Making”.