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CFOs want analysts trained in finance data science

Analytics expertise is widespread in operational realms such as manufacturing and HR. Finance data science must be a priority for CFOs to secure a place at the planning table.

Ask business leaders to describe business analytics, and you will be lucky to hear a single mention of finance analysts.

Instead, you're likely to hear about marketing and sales executives using new ways to analyze vast stores of data to figure out what customers want and what they'll pay to get it. You might hear about manufacturers using advanced analytics to set plans for materials and labor in light of seasonal demand.

Or how about chief human resource officers using HR analytics to clarify objectives for talent engagement and development? Clearly, more businesses today are learning to prosper with analytics. But here's the news: Finance is finally getting in on the act, and finance data science is becoming a priority.

The majority of finance managers surveyed by APQC, my business research and benchmarking firm, say that data science should be emphasized in their training programs. There were 154 survey participants, mostly finance professionals, from large, complex companies in a cross-section of industries globally. APQC asked people 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 to some degree; fewer than 5% say it's not important at all.

The implication: If finance analysts want to be taken seriously at performance planning sessions, they will have to hold their own with the theory and practice of modern business analysis. When finance analysts layer their economic perspectives on top of operating choices, they'll need performance views that draw on well-reasoned insights and are defensible with finance data science and statistics.

This need is even more urgent when leaders of operational functions are off and running with their own powerful statistical engines and plenty of trend data. To be blunt: When decision makers are using advanced analytics to compare, say, new strategies for volume, pricing and packaging, finance looks silly talking only in terms of past accounting results.

Finance data science can improve business forecasting

If the business is becoming data-driven, financial forecasting has to be driver-based and nuanced. And that means teasing apart probable economic consequences across the chain of value creation.

Consider just a few questions. Does analysis suggest that customers are more likely to flock to retail stores for luscious price discounts at different points in the year? What's the expected impact on regular-priced items? What about purchases made online? How will inventory and logistics scenarios change under various circumstances? What are the most likely outcomes for revenue and profit streams?

In the past, many finance analysts were constrained by blunt tools, lousy data and bad habits. Perhaps the best they could do was to take aggregate sales estimates from operating managers and plug familiar assumptions about costs and profit into their Microsoft Excel spreadsheets. Forecasts were geared to accounting history. Surely, many finance analysts couldn't later explain why their forecasts proved wrong.

APQC's research shows that business leaders today are demanding fast, high-quality decision-support from finance. They want practical analyses in areas such as:

  • revenue and profit sources
  • process efficiency 
  • customer-service economics
  • resource allocations, and
  • requirements

No wonder half of the participants in APQC's survey cited the "realization of the need for better business analytics" as the event that prompted a fresh look at financial management process improvement. The challenges lie in how to address these gaps.

Three-quarters of survey participants say they are currently engaged in at least one significant finance process improvement initiative, and they put equal weight on finance cost reduction and stronger business analysis. But while many are happy to invest in streamlining financial systems and processes to reduce the overall cost of running finance, they still place investments in staff training and education low on the priority list.

Arguably, any CFO can try to have the best of both worlds. But until development of finance data science talent is elevated in the CFO office, it's going to be a struggle for finance analysts to hold their own in a planning process that is increasingly driven by sophisticated analytics.

Next Steps

Know the benefits of accounts payable automation

Make sure data scientists understand the business

Use the analytics features of CPM software

This was last published in October 2016

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What finance data science skills do your analysts need to work on?
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Very good informative article, only which more companies and CFO’s realizes what value analytics can bring to the table.
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