FP&A Trends: Finance Transformation and the Future of FP&A

“While a reliable FP&A function relies on a solid business strategy and a sound financial acumen, at the execution level, the future of FP&A depends on three key building blocks: Digital, Data and Analytics. What are these building blocks and why do they matter from the FP&A perspectives? “

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CFO Dive: Making FP&A Analysts the CFO’s Braintrust

“The goal is to identify levers in each function area that drives that part of the organization’s overall success, put a dollar amount to the levers so leadership can know how much spend will result in how much improved performance, and then course-correct as the metrics rise or fall.”

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CFO Magazine: A Small Business CFO’s Practical Approach to New Technologies

“Consider how data analytics and visualization tools are becoming more powerful and easy to use. Could you leverage data analytics and visualization tools to enhance finance and other departments’ storytelling abilities, enabling cross-functional partners to understand current realities and trends better and thus make better decisions?”

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CFO Dive: Forecasting Cash Flow Under Volatile Conditions

““I think it’s important to forecast cash flows by estimated revenue by product, by customer, by currency, and by breaking down operating costs between fixed and variable and other categories. If there are certain variables, like key input prices that could fluctuate dramatically, having a proper sensitivity analysis can help.””

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Accounting Today: Data Analysis and Forecasting Software: Taking a Look Into the Future

“But in some practices, there’s been a degree of hesitation in adopting one of the excellent software applications targeted at this need. As with many applications, building and using an Excel spreadsheet model is still a popular approach. And some practices are hesitant to adopt and apply an analytical application that uses techniques they may not have been exposed to — essentially a “black box.” But in today’s world, many of the apps and software solutions in use employ techniques that you may not be able to detail step by step, but that produce results that are probably a quantum level above those that could be achieved a decade ago. And that’s a very good and profitable development.”

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CFO Dive: Data, Communications Pose Hidden Scaling Costs

“The central lesson from the exercise, Nucci said, is the hidden cost of misaligned analytics on a company’s ability to scale its operations efficiently. 

“I regret not taking the time to [centralize analytic operations] and have that single source of truth from a data perspective from the beginning””.

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Accounting Today: Now What? Bridge the Gap in Financial Planning and Analysis

“Transformation is no longer just “nice to have.” Business leaders must be able to rely on the validity, trustworthiness and relevance of the data used to analyze and report on end-to-end planning, budgeting and forecasting processes. The cost of continuing business as usual is duplication of the efforts, fragmented systems that take lots of people and time to reconcile, challenges to meeting regulatory compliance, an inability to determine where to prioritize resources, and lower stakeholder confidence.”

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CFO Journal: CFOs Target FP&A, Analytics for Improvement

“Among core finance functions, 63% of CFOs cited FP&A as the one they would most like to improve. Perhaps because of the increased demand on FP&A teams during the pandemic—from providing robust scenario modeling to monitoring and maximizing key priorities such as cash flow and liquidity—CFOs may have gained a clearer vision of how they’d like to reinvent FP&A.”

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McKinsey: Solving the digital and analytics scale-up challenge in consumer goods

“In the early days of digital and analytics transformations, companies prioritized individual use cases, largely in the commercial functions, based on feasibility and impact. To support the highest-priority use cases, companies then established a set of broad-based enablers—for instance, a data lake, a technology stack, and a technical organization that housed all newer talent profiles, such as data scientists. In theory, these enablers would meet the needs of the entire enterprise. In practice, however, generic enablers rarely meet specific business requirements. Successfully scaling up digital and analytics efforts thus requires a different approach: one that prioritizes fully enabled domain transformations rather than unrelated use cases.”

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