Finance transformation is making a fast comeback. According to new research conducted by APQC, CFOs are keenly aware of the need to improve the finance function. Operating managers are under tremendous pressure to perform, and boards and shareholders offer no forgiveness when growth strategies hit the ditch. So, business leaders are reaching for help from advanced data analytics, welcoming big data, automated logic and predictive analysis in the areas of operations, logistics, supply chain, product development, sales and marketing. Finance must keep pace with the enterprise’s data-driven race toward competitive advantage. That means mastering the analytical methods, tools and terminology of the day.

APQC research shows that a growing number of CFOs are beginning to understand that they need to put finance’s analytical talent to work. They must help managers in the field tease out hidden costs and opportunities before, not after, the race is run. Meanwhile, CFOs still have to reduce the cost of finance, improve cycle times and boost staff productivity.

The agenda for change

In the spring of 2016, APQC issued a survey to learn where and how financial management teams are now directing their process improvement efforts. APQC surveyed aspirations and plans among 154 participants, mostly finance professionals from large, complex companies in a cross-section of industries globally. APQC also looked at the challenge in the context of stories from senior finance executives tasked with increasing the advisory value delivered.

It is clear that finance improvement initiatives are back in action after a long lull. Three out of four survey participants are now engaged in such a campaign.

What is pegged for enhancement? The most prevalent items are:

• Stronger competencies in financial planning and analysis (FP&A) and
• Fast and affordable transaction-processing.

Most organizations are not trying to overhaul all components of finance at once. In speaking with program leaders, APQC heard about various stages of design, building, testing and implementation. We also found improvement scopes that range widely, with the overarching corporate imperatives serving as the focal points.

But three themes stand out:

• Predictive analysis based on financial and operating data
• Investments in information management innovations and
• A dire need for new thinking about finance talent development.

Finance needs data science knowledge

Business analytics will not help to predict financial outcomes with precision. Operating people still have to execute their plans with care. Crucially, the underlying assumptions driving the selection of scenarios to test and the data to be manipulated must be well-reasoned. Finance analysts will need to put together logical observations about data relationships and clearly communicate why, for example, one scenario contains more performance risk than another.

Big data and analytics can, if done properly, illuminate underlying assumptions about internal and external dynamics and which variables are so important that they could, if they malfunctioned, cause the entire performance plan to tip over. This is the risk management side. The other involves pointing out to strategists why one plan has the potential to deliver more revenue and operating profit than another. That’s not found in the traditional variance analyses.

Big data and statistics-driven analysis give financial analysts unprecedented opportunities to dig deep, understand and prepare for likely contingencies, and help the business take corrective actions. Tied in with traditional management accounting outputs, it can give CFOs more information to keep their enterprises ahead of business risks. The old ways will no longer do. Relying on historical costs and sales data alone is a risky way to guide decisions about future customer demand, supply and operating costs, and the pacing of gross profit.

Of course big data means different things for finance than for marketing. Depending on the industry, it might mean tapping into a company’s sales and operations planning platforms, or studying global macroeconomic trends that affect a supply chain. Data from multiple sources can, if used properly and combined with sound analytical methods, provide a stronger feel for cost drivers, whether they are seasonal purchasing trends, workforce availability or factors that affect the cost of raw materials.

By developing data science knowledge in finance, ensuring that FP&A experts have reliable listening posts in operations, and restructuring financial planning and analyst teams to focus more on rich, real-time analytics, CFOs can move finance toward a greater level of respect at the strategy table. APQC found that most companies are looking to equip FP&A teams with knowledge of data science itself (math, statistics, and computing). APQC asked survey takers to rate, on a scale from one to five, how important they think data science will be to finance employees from here on out. Most peg data science as important—some say extremely important—while less than five percent say it’s not important at all.

Mary C. Driscoll is senior research fellow at APQC, a business research firm in Houston.