Of the relationships that technology teams have with other departments in the business, the potential for improved IT-finance collaboration is probably the most underexplored. This is particularly poignant when considering how financial data can best guide business strategy.
Take, for example, the ubiquitous and unassuming concept of free shipping in e-commerce. Today it’s a no-brainer for all online retailers, but a few years ago it wasn’t so obvious.
Jason Child, now CFO of SaaS company Splunk, tells the story of his time in Amazon’s Financial Planning & Analysis (FP&A) department. In 1999, his team conducted a cost-benefit analysis of the free shipping model, which is arguably one of the main drivers of Amazon’s rapid growth.
They tested free shipping as leverage against a 10% discount on every order and found that the former generated twice as much revenue.
“We were a small group meeting with Jeff Bezos and we asked how to make this affordable every day, including the impact of cannibalization ie people are already paying for free shipping,” said told 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 that would be a separate class.” This led to the birth of Amazon Prime, which now has 200 million members each paying $13 a month.
This is the impact of data-driven financial analysis – or so-called FP&A – in the business context. FP&A has the potential to transform a company’s value proposition, operating model, strategic direction, or even business model.
However, like most data-driven practices, FP&A is bound by the chains of reporting, control, and compliance. DataRails research has shown that inefficient data processes and dysfunctional financial reporting cost US businesses a whopping $7.8 billion a year. Of this amount, $6.1 billion is lost to low-value, manual data processing and management, while $1.7 billion in revenue is left on the table due to lack of innovation. Amazon Prime-like.
Let’s explore the challenges that a lack of timely and accurate data places on financial planning and explore how automation can help you overcome them.
Poor quality data
One of the most common issues finance teams face is the quality and reliability of the data they collect. Even though they usually have access to accurate data sources, the data is subject to inaccuracies over time as it is shared and analyzed by multiple people or teams. Especially when there is a manual copy-paste.
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 operate in a complex and data-intensive environment. Unfortunately, they have lagged behind in automation and data integration practices, despite industry-wide recognition of the merits associated with an effective data strategy,” said Wayne Johnson, CEO and founder of Encompass.
Data virtualization – the integration of 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.
Inability to act on real-time data
Cooperation between IT and finance has never been more important in scenario planning, as businesses attempt to transition from crisis mode to recovery mode in the wake of the COVID-19 pandemic.
According to a Workday survey, nearly half of C-suite respondents were concerned that their organization could not analyze real-time data to make timely decisions or react quickly enough to unpredictable changes in the market. Finance managers struggle to generate, reconcile, access and leverage large volumes of data.
This is not surprising, as less than half of those involved in annual budgeting and planning activities report using digital technologies to perform their analyses. Compare that to sales and marketing, where more than three-quarters of team members regularly use automation.
“There’s no point in answering a question two months from now when you have to make a big pricing or channel decision tomorrow,” said Valerie Martin, chief financial officer at Autodesk.
Loss of productivity
Strategic FP&A is essential for integration, performance management, risk analysis, forecasting and modeling across multiple business functions. The truth, however, is that finance teams spend too much time performing manual tasks like 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 grown even more important as companies seek to better understand their numbers. However, despite more than a decade of effort, the daily life of an FP&A professional still involves manual processes undermining strategy, including identifying and correcting errors, updating reports, and collecting of data,” lamented Professor Mikhail B. Pevzner of the University of Merrick School of Business in Baltimore. “It essentially robs businesses and the broader US economy of billions of dollars of economic opportunity.”
Operations, productivity, integration, technology, everything takes a back seat. Revenue projections are always a priority for CEOs because that’s what dictates the flow of capital in the present.
And yet, according to a KPMG study, a measly 1% of the world’s largest companies have accurately met their financial forecasts.
The corresponding loss of investor confidence is devastating. The study also found that each time revenue deviated significantly from guidance, 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, extract and collate data as well as run different scenarios, having optimized and consistent processes is often as important as have the best technology.
Automate your planning and schedule your automation
Gartner estimates that by 2024, three-quarters of all new FP&A projects will extend their reach beyond finance into other areas of the business. Cloud-based solutions are already increasing 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 financial operations (such as ERP) depend to a large extent on manual entries 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, collection action and financial statement preparation, reducing the need for human intervention in these activities.
Companies that use financial automation can speed up and improve processes such as financial close, a time-consuming and demanding monthly process for recording and officially reporting transactions. Automating some or all of the steps and multiple submissions in this process improves accuracy and saves time on menial tasks.
Additionally, supporting technologies such as document automation and robotic process automation (RPA) enable automatic document generation from pre-existing texts and forms, as well as screen scraping and OCR. to extract, validate and consolidate financial data.
KPMG estimates that companies can achieve cost savings of up to 75% by automating financial operations, given faster turnaround times and less human intervention.
That said, automation does not remove the human element from financial planning. On the contrary, it allows financial analysts to move away from daily reports to focus on global analysis and dynamic planning.