We live in a sophisticated digital age where finance professionals have access to financial data we could only dream about 10 years ago. But surprisingly enough, there’s not much faith behind those numbers.
In Trintech’s recent survey of global financial leaders, 40 percent said they don’t trust the accuracy of their financial data. In another report, Workday’s CFO Indicator Survey 2020, 63 percent of CFOs reported frequent conflicting data between their finance and operations teams.
A staggering 49 percent of CFOs said their finance teams fail to deliver meaningful insights from available data on a regular basis. The data is often untrustworthy and conflicts with other data.
Combine all these data deficiencies and you’ve got a tough situation for finance pros. Decision-making gets dicier when data fails to shed enough light on the past or future.
What causes such distrust in financial data?
There are several reasons for the growing skepticism of data in the financial technology space. The most noteworthy, however, is the widespread use of manual processes.
Many finance pros still use manual accounts payable (AP) processes that are highly susceptible to duplications, transposed numbers, math mistakes and missing data that can’t be trusted when trying to make sound business decisions.
What needs to change to overcome these data challenges?
You could find thousands upon thousands of words online about how to tackle different data issues. For this exercise let’s focus on two major steps finance pros can take quickly:
- First, use automated AP software powered by machine learning technology; and
- Second, recruit skilled data scientists and train employees to generate more trustworthy data that leads to better insights.
Now, let’s explore these in more detail.
AP automation software powered by machine learning
Machine learning is artificial intelligence that enhances data processing and insights generated from AP automation software systems. The technology automatically collects data and, as this process continues, better predicts future behaviors and improves the accuracy of data outputs.
Machine learning cuts the amount of manual intervention in invoice and payment processes. This leads to fewer mistakes, more accurate data and, ultimately, better decisions.
For example, as it receives more data, machine learning can help an AP manager predict with better accuracy, based on past payment behaviors, if a vendor is likely to make a late payment. This is much more desirable than less accurate probability data produced with manual calculations.
It’s worth noting a large portion of finance pros already use machine learning to leverage the value of predictive capabilities. According to a Deloitte CFO survey, 67 percent of CFOs use the technology, and 97 percent plan to use it in the near future.
That’s a promising trend.
Recruit and train for better financial data insights
The surge in machine learning will be crucial for overcoming data challenges. And so will training of more employees with data analytics skills.
Train them how to think more deeply and probe differently into what the data means. Teach them to take their findings and use them in the decision-making process. Make them compelling, big picture storytellers.
Examples of what highly trained employees could focus on:
- Should the AP team focus more next year on automating invoices and payments?
If the team already uses the power of AP automation software, can it turn its attention to choosing which accounting platform would integrate best with its AP software? To add manpower and build your company’s data capabilities, recruit data scientists and others with similar skills to the finance team. These pros excel at processing and manipulating data in ways that are accurate, useful, dependable, consistent and trustworthy.
In the end, the more talented your employees are at accurately organizing, synthesizing and analyzing data, the more you can trust it.
CFOs value strategic thinking most moving forward
What’s so interesting about these ways to improve the accuracy and trustworthiness of corporate data is that they align with the skills CFOs value most. As noted by Workday, CFOs place a premium on finance pros with predictive modeling and scenario planning skills.
“The majority (59%) of CFOs striving for leadership on business-wide transformation say that strategic thinking—the ability to identify opportunities and solve problems—will be the most important skill in the future.”
Also known as predictive analytics, predictive modeling uses statistical financial data to predict future outcomes. This type of modeling uses a steady stream of data to develop more accurate insights and forecasts about the future.
Using accounts payable software, for instance, predictive modeling could leverage data about a supplier’s past behaviors to predict better if they’ll pay late again.
In scenario planning, an AP manager could use demographic data about the number of people who automated their AP processes this year, such as age and the size of business they work for, to forecast how many others could automate AP processes next year.
It boils down to trusting data, making sure it’s accurate and using analytical skills to make better decisions. Finance pros should focus on making this happen in 2021.
A smart way to make sure your data is more trustworthy throughout your company is to use corporate-wide automated financial processes such as AP software for executing invoices and payments.
Next, make sure as many people as possible in the business learn to see beyond the obvious in the data they review.
There are countless clues hiding in corporate financial data, but they have to be spotted, sorted through and thought about. Data tells stories. Often there’s a story behind the story most people can’t detect.
The key is to have people in your finance department and throughout your business who can read deeper into what data actually means for the business going forward – far beyond what the numbers are at a superficial level.
2021 will be a great opportunity for finance pros to become more adept at connecting and clustering various types of data, to see a larger pattern with bigger business implications. Seeing those patterns, they can generate new ideas and insights that unlock revenue-generating and cost-cutting opportunities that drive business growth.
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