Nilly Essaides, Director & Practice Lead, Financial Planning & Analysis, AFP
Senior management and business partners increasingly rely on financial planning and analysis (FP&A) to provide them with the analytical engine to power better decisions across the enterprise. In practical terms, that means finance needs to have the right tools and the right information. At companies like Konika Minolta in Eastern Europe and Jabil Circuits in the U.S., FP&A is putting together the technology and process infrastructure to turn finance into the analytical engine of the organization.
According to Igor Panivko, finance director at Konika Minolta, the finance team put together the system backbone to provide an analytics solution for management reporting and business leaders. At Jabil Circuits, FP&A manager Matthew Kovach is leading the effort to create an analytics hub that makes use of key data to tell effective stories, run driver-based predictive analytics, and work across the organization to deliver better analytics capabilities. The two will share their stories at an upcoming session at the 2016 AFP Annual Conference in Orlando, October 24-26.
The Finance Advantage
Big data and the availability of new analytics tools have a lot to do with this. According to Tony Levy, business unit executive of business analytics of IBM Software Group, finance now has tools at its disposal that enable it to analyze and model data to identify trends and patterns and deliver “insight and foresight” to senior management and business leaders. When IBM surveys its customers, it finds that the intention to adopt advanced analytics is the fastest growing trend. More of his findings can be found in AFP’s recently published FP&A Guide, How FP&A Can Embrace Big Data to Driver Smarter Decisions.
In the past, one had to be a data scientist to work with big data. Today, there are tools available that allow end users to upload sets of data, analyze data quality and use advanced analytics to identify patterns and correlations and derive meaningful insights. And you don’t have to be a data scientist to use them. Many of these tools, like IBM’s Watson Analytics, use natural language query capabilities and generate compelling visualizations. These tools are part of a new category of intelligent data exploration and discovery.
Already, FP&A practitioners are putting this credo to use. They are building their own analytics engines to help their organizations perform better and become more profitable by seeing more clearly into the future. “We use big data to help us predict the future,” said the FP&A director at a large manufacturing company. For example, FP&A looks at how various products are selling, and projects how they’re likely to sell going forward. His team also uses analytics and big data to predict how external drivers will affect the company’s financial results.
As the central depository and dispenser of analytics, FP&A is acting as both the consumer and the source of operational and financial information. It collects data from disparate sources, such as business units, functions and geographies; it pushes it through its models; it creates digestible content; and then it distributes it to internal customers. That means finance needs to have the right platform to integrate information and the right mindset and tools to create the right presentation so that consumers receive something actionable rather than rows of numbers.
Finance is in a unique position to assume a leadership role in the adoption of big data and predictive analytics strategies. While it may not need to “own” the data, it can act as a central hub for analytics, pulling from multiple sources of information and using the data to inform is core planning processes, in particular forecasting, to help drive faster and smarter management decisions.
According to Frank Neidermeyer, managing director at Accenture Strategy’s CFO and Enterprise Value practice, such big data and new analytics tools present a tremendous career opportunity for finance to take the lead, given its core fiduciary responsibilities. “The challenge for finance is how to develop an enterprise view of analytics,” he said. “The first thing is to realize you can find out more. You can ask questions you couldn’t ask before and frame them in the form of business outcomes.”