Forecast effectiveness is measured in how stakeholders can use future expectations to make better present decisions. The recipe for developing credible and accurate future expectations is a function of two key ingredients: First, a financial forecasting process that effectively captures the changing decisions and assumptions taking place across the enterprise. Second, a financial modeling infrastructure that can effectively convert new decisions and assumptions into accurate financial projections.
Collaboration: Regular engagement with enterprise stakeholders is a critical step in gathering the information that enables everything from understanding actual performance to projecting the impact of revised assumptions and decisions.
Analysis: With context from stakeholders and access to vast data sources, variance analyses become a tool to improve future forecast accuracy and highlight where course corrections are needed.
Insights & Recommendations: Rather than leave busy stakeholders with the task of interpretation, analyses distilled into key insights and recommendations enable quick and actionable responses, position the FP&A function as a strategic partner.
Driver-based: Build a financial modeling platform that enables projections to be a function of the core performance drivers and segmented how the business is actually managed - by responsibility center.
Process Integration: Developing planning and forecast temples that seamlessly integrate choices and assumptions into projections not only optimizes efficiency but enables rapid scenario modeling and decision-support analyses.
It is well recognized that for corporate finance professionals, making sense of information is a core competency. As such, many financial analysts spend much of their time navigating systems, learning efficient data procurement and organization techniques, and developing analytical best practices.
Although these are the core requirements of any finance professional, they often overshadow an equally important competency - collaboration. Effective collaboration both opens the door to a complementary source of data and information and is the channel by which finance can influence stakeholder decisions that ultimately impact business outcomes. Thus, it's collaboration that is the path to strategic partnership.
Questions are one of the simplest and most effective ways to develop stakeholder partnerships. Ask the right questions to build your own context and inform what should be analyzed. Answer the questions stakeholders have with facts, data, and insight. This simple "ask-answer" recipe will become the foundation for a credible relationship where finance is thought of as a strategic partner. Additionally, it's important to build an enterprise perspective - regular touchpoints with stakeholders spanning all business units and functional areas will maximize the value of collaboration.
|Clarify intent and articulate desired outcomes|
|Proactively schedule regular and recurring check-ins with key enterprise stakeholders|
|Extract stakeholder perspectives with well-prepared questions|
|Bring valuable perspectives to the table by answering key questions with insights and recommendations from robust data analysis|
While sometimes viewed as a "finance exercise" providing limited value to decision-makers, financial control analysis is an essential performance driving capability that allows analyses to be converted into digestible insights and recommendations. In order for the insights and recommendations to be actionable, the performance drivers of the accounts being analyzed must be clearly understood.
Moreover, disaggregating rate-driven vs. volume-driven variances and assumptions-driven vs. decision-driven variances helps to clarify performance attribution. Finally, recommendations must be communicated in a way that clearly and concisely translates performance drivers into available levers that decision-makers can pull. Through this process, financial control analysis becomes much more than a way to improve forecast accuracy, but rather it provides decision-makers with valuable information that can be used to course-correct and improve business performance.
|Clarify performance drivers of all relevant GL accounts|
|Procure and organize data from pre-mapped systems|
|Disaggregate projected vs. actual variances into rate-volume attribution|
|Prepare key questions and collaborate with stakeholders to extract context and form variance hypotheses|
|Disaggregate into decision-driven vs. assumption-driven variances|
|Analyze data to confirm/disconfirm hypotheses of the drivers to variances|
|Translate variance analyses into actionable insights and recommendations that decision-makers can use to improve performance|
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