Making the financial planning and analysis function responsible for centralized analytics leverages the full potential of available data. It leads to ongoing insights generation and efficient planning processes, while transforming the finance role into a value-generator and key decision maker.
My company, Konica Minolta, is a Japan-based multinational technology company, with offices in nearly 50 countries. Five years ago, the company decided that it needed to streamline its data processing and establish key metrics across the enterprise by building a common analytical model. In addition, the company and its treasury team looked for opportunities within technical design to realize this idea and found that the analytical model is more a concept than just software. That is why it has to be developed as a customized solution for a company’s exclusive analytical and planning needs.
The best fit
From internal discussions of each department’s specific responsibilities, we found that financial planning and analysis (FP&A) collects most of the information about the business and manages the company’s key performance metrics. Therefore, it was deemed the best fit for defining strategies on how to manage the overall process of Konica Minolta’s analytics.
While studying the options of integrating enterprise analytics, we met a challenge when we reviewed the company’s operational logic and attempted to find ways of mapping it to our financial metrics, which existed separately from operations. The design of our analytical model was changing continuously during implementation. New knowledge constantly arrived and no precise blueprint existed before we started. However, the final solution was recognized and widely accepted as the source of reliable and logical metrics and classifications about our business.
As a result of our experience, Konica Minolta has outlined key distinctive features of the FP&A function. These define the company’s analytical and planning strategies. The features, are at the same time, challenges for successfully integrating the company’s disparate sources of information into a streamlined, managed modelling.
In the course of implementing centralized analytics, our experience has shown that the FP&A department enjoys a unique position in centralizing overall information about the business specifics because:
FP&A consolidates revenues and costs: More than any other department, FP&A is responsible for such company efficiency indicators as gross profit, operating results, and earnings before interest and tax (EBIT). Therein lies a challenge: how to extend pure financial aggregations of gross profit and operating profit into as many business segments as needed for structured performance management and link them to operational drivers? FP&A has a good opportunity to build a bridge between financials and operational metrics.
FP&A leads the company’s variance and causation analysis: This is a crucial role, assigned to financial teams for comparing a company’s activities in time, across strategic and operational plans, rolling forecasts and similar management tools. It presents another stimulating challenge: why not organize data in such a way that all necessary historical data is stored and mapped to interconnected financial and operational logic? At first glance this seems technically difficult, but modelling technologies can make it work. At Konica Minolta we built a dimensional model with master data, which is mapped to both historical and planning data.
FP&A teams are planning maestros: Consequently our planning experience should cover not just the highly aggregated financial figures, but the operational logic on which financials are based. In many situations, while planning, we collect data about estimated revenues as totals and without operational details. This is one reason why numerous versions in the planning process arise, containing just the financial data updates without direct links to key operational logic. The challenge here is building operational plans with detailed operational statistics as a ground layer for the planned financial data. By realizing this concept, it’s possible to create robust business modelling scenarios with the possibility of manipulating key business drivers and producing a range of planning scenarios with the most relevant results. Our experience suggests there is no single software for that purpose, so we needed to use a stack of analytical and planning tools for building such a scenario planning model. FP&A is crucial for bringing IT software tools together for organizing enterprise-wide integrated analytics and planning.
FP&A is the company’s value presenter: This role is a consolidation of all other functions and roles. Traditionally, finance departments calculate shareholder value, earnings per share (EPS) dynamics, capital ratios and similar indicators. The new challenge in presenting the company’s value is to determine the causes of its changes and prospects and the ability to manage them. By using purely financial data, it is difficult to produce reliable and viable information about value. We need a much broader outlook at our business, with key value drivers defined and integrated into the scenario planning model.
Room for improvement
As evidenced by the description of challenges for FP&A teams trying to leverage enterprise analytics, there are key knowledge areas where FP&A professionals need enhancement:
- Understanding enterprise applications infrastructure: enterprise resource planning (ERP), customer relationship management (CRM), human resources and payroll.
- Understanding company’s ERP modules: for aligning business area analytics between each other and with financial data.
- Mastering analytical tools and database query languages (SQL, MDX, DAX, R, etc.): Having the technical ability to extract and manage the data can significantly extend FP&A’s reach into company’s data treasures and free it from IT technical support, which is often slow and resource-limited. Konica Minolta also carried out spreadsheet pivot table training for advanced users. This allowed us to quickly digest a variety of data sources and build new insights and simulations from our core analytical model.
While building an enterprise-wide FP&A model, we discovered that the FP&A team should not consist only of financial professionals. It can and should invite either temporary or permanent IT specialists with database management experience, who are eager to help build mathematical models. We managed to develop this kind of cooperation between finance and IT by transforming and incorporating part of the company’s IT function into technical support and management of our FP&A model. The active participation of IT personnel in the development stage of the modelling led to the creation of a center of excellence in the company’s analytics.
As we see, centralizing analytics is a very challenging process, but rewarding in terms of developing the ongoing support of strategic and operational planning and understanding key operational drivers impacting financial results.
Our FP&A approach is to build analytics first and plan out of analytics and insights. By developing an integrated analytical model based on operational drivers, financial data and planning capabilities, we can optimize or reduce intuition in the decision-making process and improve the reliability of decisions based on proven data.