Business strategy expectations for CFOs are rising, and it’s not just about keeping the books clean and planning the annual or quarterly budget any more.
A recent CFO study by TCS found that in the next five years, business finance leaders expect to play leading roles in several key areas of their companies’ growth, including tech-enabled business model transformation, the development of new products and services, and instituting new cultures and ways of working. It’s hard to drive these types of big-picture initiatives forward if you have your head in spreadsheets all day long.
Even the best CFO is just one person with a limited team, so as the saying goes, the key is to work smarter, not harder. Save those teams from annoying, labor-intensive, error-prone manual processes by automating as many of those tedious time-suck tasks as possible.
Leading CFOs are heeding the call. The most agile CFOs are focusing on developing technology and analytical capabilities to maximize the value of their data. Among the agile leaders who responded to TCS’s survey, 45% say that long-term strategic planning is a priority area for development, an answer given by only 35% of traditionalists, while 36% of agile leaders also prioritize core financial management, in contrast with 29% of traditionalists.
These decisions are already impacting the way CFOs operate. The survey showed that 52% of agile leaders are satisfied with the time it takes their team to manage financial reporting, and 51% with their ability to move talent to higher-value tasks, while only 11% and 19% respectively of traditionalists were able to make the same claim.
If you’re on the fence, here’s what automated financial data processing looks like, and what difference it will make to the value your finance team can bring to the organization.
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Greater Agility in Business Strategy Simulations
The pandemic underscored the need for agility and resilience in enterprise operations. It’s arguable that this is most important among finance teams, which increasingly act as the foundation for every aspect of business strategy.
However, finance teams are often the least agile department, which is no surprise when you think about how a typical finance team works. It’s tough to support agile, long-term strategy decisions when your data processing systems take days to complete.
At the moment, most company finance analysts enter data manually into Excel spreadsheets and use built-in tools to process it. This was good enough for the 1990s, but in the 2020s, data moves too fast for Excel to keep up.
DataRails replaces manual processes with automated data consolidation through the familiar Excel interface. DataRails’s automated system aggregates data from multiple sources, verifies and cleans it, and prepares it for more advanced AI projections – or for humans to consult without breaking their heads copy-pasting and reconciling varying datasets.
Automating data processing helps reduce errors, save time, lower frustration, and ultimately bring more agility. With more reliable and faster reports, CFOs can up their forecasting game to best practices like agile rolling forecasts instead of rigid quarterly reforecasting, and they can even run through ad-hoc “what if” scenarios in the middle of meetings.
Increased Trust in Finance Reports
When finance teams use spreadsheets and manual data processing, they’ll struggle to ensure data quality. As long as it takes to import all of the relevant figures, it can take even longer to identify and correct errors.
Automated data collection and processing means that data gathering isn’t just faster, the data itself is more trustworthy.
That’s particularly important when the top reason why finance teams don’t consult enterprise data is because data quality isn’t reliable, as TCS’s findings indicate.
Improving the quality of finance data breeds trust both within the finance department, and between different departments across the organization, which is crucial when making high-stakes business decisions.
More Accurate Forecasting
Forecasting is a key role for CFOs, and it’s quickly rising even higher.
It’s hard to meet these requirements without accurate forecasts about competition, markets, customer demands and so forth. What’s more, those forecasts in turn rely on data that is as close to real time as possible, from more sources and more diverse structures than ever before.
Finance teams that have to hunt data across the organization have less time to analyze it, plus by the time they unlock detailed sales data, for example, they’ll have lost the edge that comes from real time data updates.
Data flows more swiftly and smoothly with a tool like Integromat to integrate apps into seamless, automated workflows. These workflows automatically push and pull data to the places it needs to be, saving time and frustration in data gathering, cutting errors in data quality, and forming the basis for fast, friction-free forecasting.
Easier Access to Valuable Insights
Creating insights is one thing, but ensuring that finance teams can access them is quite another.
Augmented business intelligence platforms that use natural language for data queries democratize access to financial insights. Finance teams can run their own finance-related queries instead of going through data science teams, much to their shared relief.
In this way, you can remove bottlenecks for finance teams and speed up access to the insights CFOs need for forecasting, risk assessment, business strategy decisions, and more.
Ongoing Analysis
Because automated reports require so much less time and effort to produce, it becomes possible to adjust them as time passes and conditions change.
Augmented analysis spots patterns and trends in data faster than humans can, with less frustration and fewer tedious comparisons along the way, so you can compare more datasets and look for more issues than with human analysis alone.
With AI-powered automated complex analysis like that offered by Tellius, CFOs can quickly and easily produce multiple predictions based on different scenarios and compare them to find the one with the best outcomes, lowest risk, highest likelihood, or whatever your overriding concern may be. Again, this enables faster response times and greater agility in forecasting and decision-making.
Data Processing Automation Is More Than Just a Useful Tool
Automated financial data processing can be fundamental for business strategy. With faster, smoother data collection and processing, improved agility, continuous analysis, democratic access to insights, and more accurate forecasting from the right data processing automation, your finance teams can bring more value to the entire organization.
About Carson Derrow
