As custodians of company data, finance leaders are fielding more questions from internal stakeholders than ever before. Those stakeholders — from the board down to operational line managers — want more metrics, more analytics, and deeper and more meaningful insights into financial and operational performance. The questions themselves aren’t new. It has only been recently, however, that finance leaders have access to the technology to provide the answers in a timely and relevant fashion.
The finance function is well-aware of the pressure to deliver these timely and relevant answers. Meeting stakeholders’/internal customers’ needs ranked among the top priorities for CFOs and other finance leaders in Protiviti’s 2019 Finance Trends Survey.
In fact, while the changing demands of internal customers ranked fourth in this year’s survey, it was the top three priorities — security and privacy, enhanced analytics and process improvement — that were driving those increasing demands. CEOs, directors and others want to know how critical financial data is guarded, what real-time information it can deliver and how it can be used to identify gaps in processes in order to improve them.
To balance these increasing demands with the ongoing need to execute core responsibilities, finance leaders have been turning to technologies such as data visualization, robotic process automation (RPA) and blockchain. These advanced tools demand quality, governed data sets to operate in order to deliver much needed efficiencies in the finance function.
In the past, companies relied on humans to verify data reports and correct errors. Ten years ago, when financial data was used primarily as a source of directional guidance for marketing, prevailing wisdom was that random errors offset each other, and that the data only needed to be generally reliable, as long as it was taken in aggregate.
That was the nature of technology back then. If a cell phone dropped a call, for example, it wasn’t a big deal; customers had a landline for use in emergencies. Today, most people have given up their landlines, and they expect their cell coverage to be reliable. Similarly, in an environment where technologies like AI and RPA increasingly replace the human touch in critical data-driven business decisions, the expectations for data accuracy are huge.
Another factor driving data demands is external auditors who increasingly rely on the automated analysis of entire data populations instead of human-curated samples. As such, it is more important than ever for finance departments to understand what data they have available and to ensure that the raw data is accurate and categorized properly.
All this requires a multifaceted approach, which involves asking: “What data elements do we have? What’s the quality of that data? Can we use it and trust it? What are our data gaps and how do we fill them?” These questions, along with potentially more difficult ones such as “Should I leverage my customer data for certain purposes?” are increasing the overall complexity of data utilization. However, an organization must answer these questions — and build an effective data governance — before it can begin to effectively utilize its data assets and meet the demands of its stakeholders.
As I said in the beginning, the demand for more insight has always been there. Now is the time to put the premise into practice, recognizing that data analytics is rapidly evolving from an interesting gadget into an indispensable utility. Stakeholders’ expectations are increasing accordingly, and they expect finance departments to respond to them using the technology that is available. Our survey indicates CFOs not only know this but are prepared to meet that need by increasing budgets and boosting resource capabilities.
Download our survey report to review the detailed findings and benchmark your CFO priorities against peers.
