Financial planning and analysis (FP&A) teams that want as much data at their fingertips as possible yet insist on using only “perfect” data in their analyses are unlikely to provide the kind of actionable insight many CFOs seek from those teams, a new report suggests.
The desired level of insight can come only from financial planners who incorporate more judgment into their analyses and fewer raw numbers, according to the CEB Financial Planning & Analysis Leadership Council, a new program of the Corporate Executive Board (CEB). To understand common FP&A challenges, the council performed qualitative analyses based on extensive interviews with 70 corporate FP&A heads and with academics and consultants. It also conducted some quantitative, survey-based research.
The finding: poor financial analyses may stem in part from overly detailed analyses that consume too much time. “When you give people too much information, they actually underperform,” says CEB executive director Michael Griffin. “There is more and more data coming in, but that doesn’t make it any easier for FP&A teams to deliver actionable insight to their business partners.”
To their credit, the leaders of FP&A teams willingly point the finger at themselves. Only 29% of those surveyed say they consistently deliver insights about the business. The rest say they sometimes or never do that.
The difference between those two groups lies mainly in their willingness to use subjective judgment to illuminate or discount what the raw numbers seem to say. In a separate survey of 444 finance employees of all types, the CEB found that 37% were “informed skeptics,” who apply judgment to their analyses and are comfortable with dissent and listening to other viewpoints. Those are the preferred type of staffers, flanked by two extremes: “unquestioning empiricists” (44% of respondents), who trust data over judgment and value consensus, and “visceral decision makers” (19%), who seldom trust analysis and make decisions unilaterally.
That unquestioning empiricists make up the largest of the three groups is hardly surprising, says Griffin. “Finance folks are very comfortable with data, but less so with the application of judgment into the data,” he says.
By contrast, the informed skeptics possess the relevant skills for decoding large amounts of data, managing ambiguity, and using judgment to influence their analyses, the CEB writes. “Unfortunately,” the board’s report says, “there are a relatively small number of these analytic experts, constraining the scope and depth of analytic capabilities across finance. As such, FP&A teams’ greatest risk comes from too much data, not too little.”
With judgment playing such a critical role in analytics, the CEB identified five elements of judgment and specified how they should be incorporated into FP&A:
1. Synthesizing diverse data. Integrate into the analysis both qualitative and quantitative data, as well as external viewpoints.
2. Inferring trends. Distinguish patterns that are relevant from those that are not; identify risks and opportunities based on data analysis.
3. Generating insight. Isolate actionable and noteworthy implications, and teach managers something new about their business.
4. Redirecting poor business assumptions. Surface key biases and assumptions that affect the results of data analysis; identify and size the impact of environmental factors that may not be reflected in the data.
5. Influencing business decisions. Deliver controversial messages comfortably and with authority; clarify decision trade-offs to internal customers.
The CEB also identified three broad take-aways from its research. First, stop relying on boilerplate performance-review criteria that were created for finance generalists. Instead, tailor the FP&A competency model by clearly defining analytic skills and behaviors that are unique to that discipline and that lead to insight generation.
Second, identify key decision points where FP&A can cut down on unnecessary, non-value-added work, and establish protocols for analysts to collaborate with business partners.
Third, don’t spend much time looking for the perfect data or analysis to answer business questions. Teach analysts to make smarter trade-offs between timeliness and accuracy by setting guidelines about which types of decisions or projects require perfection and which require only directional analysis.