Ninety percent of CFOs report that they try to meet all demands for finance support, but this goal isn’t just ambitious — it’s impossible. The demands placed on finance teams have grown as business partners increasingly rely on finance for insightful, actionable analytic support to help them make strategic decisions.
If finance tries to meet every demand, including those that add little value, the quality of the analysis will suffer and strain limited resources.
Saying no is hard, and corporate finance leaders feel this dilemma with their business partners just like IT and other departments.
“Most financial planning and analysis (FP&A) departments still don’t follow the practice of saying no,” says Johanna Robinson, finance practice leader at CEB, now Gartner. “In fact, 90% of CFOs report that they strive to meet all demands for finance support requests made of their finance team.”
Read more: Efficient Growth: The New Finance Mandate
Teach employees how to make trade-offs
Although this seems like a simple concept, teams struggle to apply this in their daily work. To help determine where you can most effectively make trade-offs, use an established set of criteria to help employees know when to provide in-depth analytic support and when to scale back. Not only does this allow for the most important analytic requests to take precedence, but also helps your employees better prioritize their time.
Most finance teams have a limited knowledge about analysis being done outside the function
The CFO of one major bank uses principled criteria to prioritize when a finance team will spring into action to support a business request. The same criteria provides cover to earn time back for the most important requests by allowing the team to say “no” more often. The tiered system is developed through a set of both finance and non-finance criteria. Businesses with the highest growth potential receive customized support, while finance guides lower tiers to self-serve models. The result has been positive momentum in educating business partners on the difference between wants and needs.
Remove duplicative efforts
While getting comfortable with saying no is important, finance must also have a high degree of certainty that it is focusing on the correct areas. This is made all the more difficult as business units and other departments build out their own analytics, complete with conflicting, duplicative, and incompatible data and reports.
Most finance teams have a limited knowledge about analysis being done outside the function, which can lead to unnecessary or duplicated work. In addition, business stakeholders don’t know where to look for a particular analytic request, which wastes everyone’s time.
Currently, only 22% of FP&A teams have defined their analytic role relative to other analytic groups within the company. Ultimately, each department adds its own expertise to the analytic mix. Defining these contributions will help eliminate duplications of work and reduce the analytic burden on finance teams.
One telecom company decided to cut through the noise by proactively partnering with business units to design joint analysis areas that combine the strengths of each team into one unified report that both sides can endorse. Finance recognizes that marketing analytics will be superior in measuring customer attitudes, while it takes the lead in running ROI figures. This leads to less duplication and frustration and a better business impact from FP&A’s strengths.
Although each approach varies, they share a common characteristic: Less overall service to yield a higher quality of support.
Redefine finance roles
With the outgrowth of analytics, some CFOs have explored centralizing the analytic portion of decision support. In fact, 72% of organizations intend to centralize analytics by 2020. The benefits of such a move include leveraging scarce talent, improving efficiency and using data science to provide a deeper level of insight. However, many CFOs worry that such centralization will lead to a loss of influence and business acumen.
To combat this, a major manufacturing company uses a “hub and spoke” model to counter these risks. The hub in this case is the data scientist, who conducts deep analysis and partners with IT and analytics teams. The spokes — who are qualitative financial analysts — take these analyses and recommend them to the business units. The end result is a model that better protects the data scientist’s time while still providing business-specific viewpoints through FP&A analysts who serve as interpreters.