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Big data may be big news, but many financial planning and analysis (FP&A) groups are still trying to come to grips with small data. Can FP&A move beyond?
“What’s different about today’s data environment is the immediacy and availability of data and the ability to access it,” said Larry Maisel, president of consultancy DecisionVu Inc. and a former Columbia Graduate Business School professor. “For organizations, it presents both an opportunity and a challenge, particularly for the FP&A function. The challenge today is not how to access it, but what information to access and how to use it productively to gain insight into trends and inflection points.”
According to most FP&A professionals and experts, FP&A is still at the cusp of integrating big data into core planning and forecasting and analytics processes. But change is afoot. According to Frank Niedermeyer, managing director for Accenture Strategy’s CFO and enterprise value practice, old data from two to three years ago shows marketing as the top consumer of big data and finance at the bottom. However, “with the new role of the CFO and availability of new systems, nearly every company I know of is working on changing that,” he said.
There are three things that need to happen for finance to make progress in that direction:
Ensure data integrity and quality. The first thing that must happen before finance can develop a big data strategy is for the organization to build a strong data governance and quality infrastructure. Big data flows in from multiple sources, structured, unstructured, internal and external. These data pipes feed into what some experts call “data lakes” or “sandboxes”. That’s where data governance is looser and data analysts can “play” before moving clean and qualified data into more structured, highly governed data warehouses. For many companies, that’s still work in progress. “The challenge for finance professionals is how to incorporate that [information] in sensible way into current data governance infrastructure, have the business recognize its value and realize the business value to set the stage for analytics and KPI development,” said Jenny Okonkwo, a senior finance professional and the founder of Transform Consulting.
Find the right talent. FP&A does not need to be populated by data scientists. But finance needs to have an aptitude for technology and a new mindset: it needs to shift from looking backwards at ledgers and historical information to looking forward at multiple types of data and predictive analytics. In many cases, accountants and FP&A professionals have come out of school without a lot of education in statistics, etc., according to Pras Chatterjee, director of product marketing at SAP for enterprise performance management. “They need a sound understanding of statistics and may need to work together with a data scientists,” said Chatterjee. So going forward, as finance works more directly with big data, it may require a new skillset. FP&A executives will be called upon to provide more analysis, more often and on the fly. “They’ll need a mature business analytics skill set,” Chatterjee added.
Added Ilya Umansky, CTP, FP&A, director of FP&A at The Ratner Companies, in this new world, the FP&A profession “will require a great degree of ability to organize, structure, and understand the data sets. I’d love to get somebody on my staff to be able to do it.” That would be a programmer who understands the business and data architecture, according to Umansky. “That combination of skills is hard to come by. It’s not even about understanding finance. It’s about understanding the business,” he said.
Clarify data ownership. According to FP&A professionals and consultants, FP&A has a unique opportunity to act as an analytics hub for big data across the organization. But before it can play this role, there needs to be clarity of roles. There’s no need for finance to capture the data itself, if the business is already collecting it in marketing or sales or HR, according to Maisel. “And it doesn’t necessarily require the same level of detail,” he said. What’s needed is a clear delineation of roles, between collection, data ownership and the kinds of data analysis different functions run on that data. In cases where the organization has already defined FP&A’s role as a business partner, “they should work with each line function to develop and deploy that information, but line function should retain responsibility to capture and use it,” he said. “FP&A shouldn’t advocate involvement; rather, they can add their own value to the analysis.” As new analytics tools become better and more accessible to finance, “that’s where the real value of FP&A will come from; it’s in the balance between being a business partner and a controllership function.”