• Kevin Rubin, CFO at Alteryx


• 17.02.2022 09:45 am
#Data #finance

Over the past two years, the pandemic has placed numerous hurdles in front of companies trying to do business. But even before the Covid crisis began, some departments had to scale obstacles daily to accomplish their tasks. This is most true of the finance department. Accomplishing these vital financial duties - from payroll, to cashflow - is proving more and more complicated as challenges mount on a daily basis. 

Many finance teams now struggle to manage the different and increasingly complex data sources they must deal with. On average, data workers leverage more than six distinct data sources, 40 million rows of data and seven different outputs while producing their analysis. Doing these repetitive tasks manually is inefficient and can often lead to problems with data quality, as well as a lack of auditability and oversight.

This challenge is compounded from the continued use of outdated legacy systems. Spreadsheets, for instance, are regularly used as a proxy for data preparation and analysis, but are error-prone and expose the organisation to compliance and trust issues. Financial analysts and accountants are increasingly frustrated with mundane spreadsheet work — according to a recent IDC study, $60bn is wasted every year in the US due to data workers such as finance professionals spending hours and hours on spreadsheets. 

Amidst this background of exponential data growth, CFOs now need to invest in more robust processes, such as systems built specifically for data analytics and data science. In addition, they should invest in the digital skills and data literacy of their workers through training so that they can access the full potential of these new tools. Upskilling the existing workforce is an essential component of a successful digital transformation strategy. 

Automated analytics workflows can empower organisations to speed up manual processes such as collecting and sorting the data needed for reconciliation and work more efficiently by freeing up staff to work on more creative or value-added work, such as identify future revenue streams.

In fact, according to one IDC report, using data science and modern analytics allows finance departments to complete financial forecasts 74% sooner, make decisions 25% faster, and improve financial report accuracy by 16%. 

Finance analysts often have to account for regional variations in tax planning and rates. In spreadsheets, this would involve juggling multiple VLOOKUPs and pivot tables to try and get the data in the correct format for tax planning. But with automated analytics workflows, it is possible to build a simpler end-to-end calculation process that handles both complex real-world data as well as the uncertainties of dealing with missing or incomplete information is possible. This replaces a manual set of tasks with a fully repeatable visual workflow that highlights data issues for reconciliation. Freeing up the financial analysts’ time to focus on other high-priority work.

This digital transformation of the finance department offers the ability to close, consolidate and report faster and with increased reporting accuracy. It frees up resources tied to manual repetitive tasks, while reducing operational costs and increasing efficiencies.

Unfortunately, as more and more businesses recognise the power of data science, they are encountering a similar problem: a lack of data scientists. Due to high demand, there is now a global shortage of data scientists within the labour market. According to Quanthub, this shortage grew to 250,000 in 2020 and shows no sign of stopping today.  

However, there is a way for CFOs to navigate this shortage. Instead of competing for the limited supply of data scientists, CFOs would be better off upskilling their current workforce. Many financial employees already possess the foundational skills necessary to becoming citizen data scientists – combining their analytical and technical skills with the essential business context needed to make impactful decisions. 

When faced with challenges, some executives may assume they need to hire additional outside talent, but this is not always the right approach. By fully embracing digital transformation and data science, the finance department can substantially diminish process cost, while successfully redeploying talent to value-add activities. While the past few years have been a challenge, the future defined by opportunity.