Among the relationships that technology teams have with other business departments, the potential for improved IT-finance collaboration is quite possibly the most under-explored. It’s especially poignant when we consider the extent to which financial data can steer business strategy for the better.
Take, for example, the ubiquitous and unassuming concept of free shipping in ecommerce. Today, it is a no-brainer for all online retailers, but a few years ago, it wasn’t as obvious.
Jason Child, now CFO of SaaS company Splunk, tells the story of his time at Amazon’s Financial Planning & Analysis (FP&A) department. Way back in 1999, his team did a cost-benefit analysis of the free shipping model, which is arguably one of the key drivers of Amazon’s stupendous growth.
They tested free shipping as a lever against a 10% discount on each order and found that the former generated twice as much business.
“There was a small group of us that had a meeting with Jeff Bezos, and we asked how we can make this affordable every day including the impact of cannibalization, which is people already paying for free shipping,” recounted Child. “FP&A came up with the idea of a 5-day delay, where those who wanted free shipping would face a 5-day delay so it would be a separate class.” This led to the birth of Amazon Prime, which now has 200 million members paying $13 each per month.
This is the impact of data-driven financial analysis – or what is termed FP&A – in the business context. FP&A has the potential to transform the value proposition, operational model, strategic direction, or even the business model of a company.
However, like most data-driven practices, FP&A is bound by the shackles of reporting, control, and compliance. Research by DataRails showed that inefficient data processes and dysfunctional financial reporting costs US businesses a staggering $7.8 billion a year. Out of that, $6.1 billion is lost to low-value, manual data processing and management while $1.7 billion of revenue is left on the table because of Amazon Prime-like innovation not happening.
Let’s study the challenges that a lack of timely and accurate data places on financial planning and explore how automation can help you surmount them.
Poor quality data
One of the most common problems finance teams face is the quality and reliability of the data they collect. Even though they usually have access to accurate sources of data, the data is prone to inaccuracies over time as it is shared with and analyzed by multiple people or teams. More so when there’s manual copying-pasting involved.
The end result is that there is no single source of truth accessible to the CFO and senior management, which slows down (or worse, introduces errors into) the decision-making process.
“Financial institutions are operating in a complex, data-hungry environment. Unfortunately, they have fallen behind when it comes to automation and data integration practices, despite industry-wide recognition of the merits associated with an effective data strategy,” said Wayne Johnson, CEO & Founder of Encompass.
Data virtualization – integrating data from multiple sources, across multiple applications and in multiple formats – provides a clear path to information unity here. Analysts can retrieve and manipulate data without knowing where it is physically located.
Failure to act on real-time data
Cooperation between IT and Finance has never been more important in scenario planning, as companies try to move from crisis-mode to recovery-mode in the wake of the COVID-19 pandemic.
According to a survey by Workday, nearly half of C-suite respondents were worried that their organizations couldn’t analyze real-time data to make timely decisions or respond quickly enough to unpredictable market changes. Finance executives are struggling to generate, reconcile, access, and mine high volumes of data.
This doesn’t come as a surprise, because less than half of those involved in annual budgeting and planning activities say they use digital technologies to perform their analyses. Compare that to sales and marketing, where over three-quarters of team members routinely make use of automation.
“It’s no use if you answer a question two months from now when you have to make an important decision on pricing or channel tomorrow,” mused Valerie Martin, Finance Director at Autodesk.
Strategic FP&A is critical for integration, performance management, risk analysis, forecasting, and modelling across multiple business functions. The truth, however, is that finance teams are spending too much time performing manual tasks such as account reconciliation and financial close – in other words, sorting and organizing data instead of analyzing it.
“Since COVID-19, the role of financial planning and analysis has gained even greater momentum as businesses seek better understanding of their numbers. However, despite more than a decade of efforts, the daily life of an FP&A professional still involves strategy-sapping manual processes, including identifying and correcting errors, updating reports, and collecting data,” lamented Prof. Mikhail B. Pevzner of the University of Baltimore’s Merrick School of Business. “This is essentially depriving both companies and the wider US economy of billions of dollars of economic opportunity.”
Operations, productivity, integration, technology, everything takes a back seat to the bottom line. Revenue forecasts are always top-of-the-mind for CEOs, because that’s what dictates the flow of capital in the present.
And yet, a paltry 1% of the world’s biggest companies hit their finance forecasts precisely, per a KPMG study.
The corresponding loss in investor confidence is devastating. The study also found that whenever the revenue deviated significantly from predictions, the company’s share price suffered for up to four quarters.
While cloud-based financial forecasting solutions and ML-based algorithms can help you collect, mine, and gather data as well as run different scenarios, having optimized and consistent processes is often as important as having the best technology.
Automate your planning and plan your automation
Gartner estimates that by 2024, three-quarters of all new FP&A projects will extend their scope beyond the finance domain into other areas of the enterprise. Cloud-based solutions are already growing their automation capabilities to extend financial planning and analysis to different functions such as HR, sales, and supply chain management.
Conventional systems that also perform finance operations (such as ERP) depend on manual entries to a large extent and are prone to errors and discrepancies. However, the rise of AI-based software has accelerated finance automation, which Gartner defines as “technology that integrates machine learning and artificial intelligence for use in areas such as financial analysis, payroll administration, invoice automation, collections action, and preparing financial statements, reducing the need for human intervention in these activities.”
Companies that use finance automation can speed up and improve processes such as financial close, a lengthy, effort-intensive monthly process for recording and official reporting of transactions. Automating some or all of the multiple steps and submissions in this process improves accuracy and saves time spent on menial tasks.
Further, supporting technologies such as document automation and robotic process automation (RPA) enable auto-generation of documents from pre-existing text and forms as well as screen scraping and OCR to extract, validate, and consolidate financial data.
KPMG estimates that businesses can realize cost savings of up to 75% by automating finance operations, given faster turnaround times and less human intervention.
That said, automation doesn’t do away with the human element in financial planning. On the contrary, it enables financial analysts move away from everyday reporting to focus on big-picture analytics and dynamic planning.