By Varsha Ramesar, Managing Executive, Data and Analytics
The data revolution has fundamentally changed the role of the finance professional as we know it. In order to continue to be seen as a value-added service or department, finance professionals must evolve – not only to keep up with ever-changing technologies, but to access the benefits that data can bring to the business.
Traditionally, finance had to rely on historical, internal data to draw insights. Not only was this data limited in scope, but it failed to give a full perspective of how decisions today would impact the future. Now, the introduction of predictive analytics has helped moved finance’s analysis from asking “why did it happen?” to exploring “what will happen next?”.
Access to in-depth insights enables finance professionals to track customer data in real-time and evolve from simply keeping records, to carrying out in-depth analysis of the data. Their unique visibility of the holistic position of the business allows them to analyse and interpret anomalies and trends, and this information can then be passed to internal stakeholders to help them to make value-added decisions.
Gone are the days of finance working discretely behind the scenes as the “number crunchers” of the business. The future of the role will increasingly see finance professionals using value-added analytics to position themselves as a strategic voice within a business.
Too much data, too few insights
Finance data lives in many places within the company and goes by many names—chart of account, cost centre, product code, business units, projects, legal entities, as well as core reference data such as billing codes, taxonomies, geocodes, and product classifications. The finance functions of most organisations typically have a process for managing and changing this data, but many still don’t have a way to get actionable intelligence and insights from the data.
In fact, many organisations still struggle with their visibility into financial data management processes. Business users rely on emails, phone calls, and spreadsheets to help facilitate the process. And if the tools themselves aren’t confusing enough, the process for reviews and approvals can be just as frustrating. This is particularly true in large organisations with complex operations with multiple business units spanning different geographies, where—to put it politely—managing finance data can be extremely challenging.
In addition, while financial data has historically been the primary source of the finance team’s insights, other types of data generated by the business need to be used to further enrich the decision-making process. CFOs and their teams therefore have no choice but to jump on the data bandwagon if they wish to remain relevant.
Automation and analytics
A 2020 Accenture study suggests that about half of finance functions have received more requests for financial data, price and trade-related data, sentiment analysis, and risk and compliance data than in the past. The demand for data is even higher for finance functions in high-growth organisations. This is backed up by research by FSN on planning, budgeting, and forecasting, which also found that CFOs that make good use of non-financial data are able to forecast with 90-95% accuracy.
Unfortunately, many companies have not invested in the right automation tools yet, so even those finance teams that are using all of the data available to them are spending more time gathering the data than analysing it. According to PwC, even in top quartile companies, people spend 40% of their time gathering data, instead of working with it. That’s largely due to how typical organisations have hundreds of applications and systems where data is scattered, PwC says.
Data used by finance comes from a variety of different sources and systems, including ERP and CRM systems. This makes it even more challenging for the finance team to get a unified view of data, and to extract the right insights.
Analytics allows many of the tedious administrative duties that have long been central to the finance professional’s role to be traded in for a more efficient way of working. From processing data, to bringing it into context and getting answers from it, automation frees up people to generate deeper insights for the business. This allows the business to leverage their expertise, enabling the finance team to focus on providing a higher quality service and business insights than ever.
As data becomes more and more central to the finance professional’s role, and as organisations become increasingly reliant on the finance team’s insights to drive their business strategy, the relationship between finance and data will become increasingly more important. As in any other area of the business, turning finance into a data-driven function will help to increase revenue, decrease costs, mitigate risk, and help the organisation compete and adapt more effectively in a continuously changing macro-economic environment.
