key principles

A “modern” approach to Financial Planning and Analysis has become a requirement for a successful corporate finance function. Gone are the days where finance teams were viewed solely as a report publisher that conducted reactive variance analyses. Instead, modern finance teams are able to create an agile, efficient, technology-enabled, proactive and highly valued decision support function. In order to achieve this success, the value proposition of core FP&A responsibilities, such as budgeting, planning, analysis, forecasting, and financial reporting, needs to be better understood.

To this end, our Financial Performance Management team has compiled a list of what we believe are the six key principles of a modern FP&A function.

1. Big-bang Doesn’t Work, Modern FP&A Capabilities are Built Iteratively

As a data and analytics consultancy, Ironside brings a unique perspective to the challenges CFOs face in creating a modern, technology-enabled FP&A function. In our experience, far too many finance teams have been involved in failed reporting, budgeting, and planning initiatives. This creates a reluctance to invest the time, money, energy and political capital required to make these initiatives successful. In many cases, consultancies are competing against ‘doing nothing’ where the incumbent is largely spreadsheet-based, manual processes.

key principlesOur unique FP&A perspective is anchored to a use-case oriented approach that initially emphasizes the ability to execute on a narrow, high value and compelling set of business questions as a mechanism to demonstrably prove the ability of the team and the technology to execute effectively. Coupling this with an ongoing, iterative implementation strategy will allow finance teams to regularly deploy solutions that build modern

analytical capabilities rapidly over time.

While single-event (i.e. ‘big bang’) go-live strategies are often required for ERP systems due to the cost and complexity of managing parallel environments, the success of an analytic solution implementation is directly tied to adoption. Figuring out the most effective way to answer a complex business question happens over time as analysts become more familiar with the capabilities and data delivered through the toolset. In this context, Ironside has seen a direct relationship between an iterative deployment approach and increased adoption, which leads to rapid advancement of modern FP&A capabilities within the finance function.

2. Finance Should Drive the Cascade of Business Strategy

As companies grow, ensuring that everyone is aware of, and executing towards, the overall goals of the organization is a significant challenge. Finance is central to the translation of the overall strategy to the individual functions and business units via a well-facilitated planning and performance measurement process. Additionally, finance has a deep understanding of the specific microeconomics of the company’s goods and services coupled with a macro view of the competitive landscape, which are critical contexts for strategy setting and execution.

All too often, we see forecasts, budgets, performance measurement and planning processes that do not have a direct linkage to the overall business strategy, which can significantly reduce the value of the finance team’s work to the organization.

3. True Ad Hoc Analysis Will Always be the Land of Wit, Spreadsheets and RPN Calculators

The critical decision support supplied by finance teams is often needed at, or near, real-time. Senior Executive time is scarce and finance decision support teams must be armed with recommendations, which are backed up by data and models created on-the-fly. Face-to-face executive meetings can also involve direct challenges to model assumptions and alternative scenarios that require the finance participant to know the numbers and modeling logic in detail, on-the-spot. Failure to be able to react in real-time can negatively impact credibility with executives. “Hold-on, let me pull up the model/dashboard to answer that question” is an instant hit to credibility. It’s in these types of “in-the-moment” or “never-before-analyzed” scenarios where “offline” tools such as spreadsheets and RPN calculators will always reign supreme for finance. Ideally, these offline approaches are only relegated to this specific type of decision support.

4. More Accurate Forecasts Build Trust and Reduce Risk and Effort

The ease of creating accurate budgets and forecasts is closely aligned with ease of predictability. Occupancy expense forecasts are accurate and simple to maintain due to the highly predictable nature of a signed building lease. Compensation expense forecasts for high employee turnover functions and revenue forecasts for highly volatile product lines, on the other hand, are difficult to predict and often inaccurate when forecasted based upon historical trends.

key principles

Inaccurate forecasts can significantly increase risk for finance in a capital planning and investor valuation context. An excessive cash/liquidity buffer is often needed to meet future obligations that may or may not materialize due to an imprecise forecast. Similarly, investment analysts running company valuation models are heavily reliant on forward earnings forecasts. In this case, earnings guidance deemed inaccurate can directly affect share price. Lastly, inaccurate forecasts can create a cascade of non-value added effort for finance teams due to the need to explain (sometimes over-and-over) why a flawed forecast is off.

In order to accurately predict revenue for a volatile product line with an appreciable level of precision, finance teams need to consider more exogenous and econometric variables, such as consumer confidence and consumer debt levels. The significance of each driver needs to be understood, both for modeling purposes and for the business to understand what factors drive its performance. Best case, worst case, and likely case scenarios derived from stochastic modeling methods (e.g. monte carlo) can also help finance understand a possible range of outcomes.

Keeping a human touch makes models even better. For example, it is difficult to create a model that will accurately predict revenue in a year when a new product launches. This is where it is important to have a well-trained human in the forecasting process. Assuming the human involved in changing the forecast has an advanced analytics background, they will be able to use alternative models to assist with their ad hoc prediction.

5. Know Your Stakeholders (Beyond the Shareholders)

While company shareholders are the finance team’s most prominent stakeholders, it is important to also understand how and why financial information is used by the broader stakeholder community. A deeper understanding of finance stakeholders and their corresponding activities, as framed by specific use cases, is an excellent way to ensure that financial data and related analytics are deployed effectively by the central finance team.

Central finance teams often take a self-service approach to decision support, where departmental views of financial information are made available to broader operational and functional teams. To be successful with this approach, central finance needs to make themselves aware of how the self-service information is actually being used in localized decision making. In regards to the sales team, some of this usage could take the following forms:

  • Is the sales manager creating data dumps from the finance-owned tool and merging it with competitive information?
  • If so, is this being done offline in spreadsheets?
  • Does it conflict with other views of the same information?
  • Most importantly, what sales-related business decisions are being made from this spreadsheet?
  • Can the ability to answer this sales question be incorporated back into the finance owned platform?

A central finance team that does not proactively understand this type of consumption and usage of financial information by specific functions runs the risk of driving that function to create their own finance team (i.e. “shadow-finance”) and associated analytic solutions for themselves. In larger organizations, this vacillation between central and local decision support can be significant, inefficient and disruptive.

6. Efficiency Clears the Way to Effectiveness and Speeds the Shift from Reactive to Proactive

Finance plays a critical role in informed decision making. Virtually all companies clamor to know the financial ramifications of business decisions before they are made. The challenge is that decision support rarely follows a set schedule, which results in unpredictable re-prioritizations that, in turn, make finance teams inefficient and resource constrained. Ad hoc requests often become recurrent and then the rushed, error-prone, spreadsheet-based and manual processes used at the outset become a significant maintenance challenge over time.

key principlesAn IT and finance partnership that monitors ad hoc use cases that have the potential to recur is a key first step in reducing the ad hoc analysis burden. Data acquisition, data quality and consumption-and-reuse-friendly data modeling are core areas of expertise for the IT partners and, conversely, are often painful exercises for finance teams. A finance and IT partnership that focuses on integration of data processes created during ad hoc crunch times into enterprise reporting and analytics platforms will allow finance to become more accurate, efficient and effective.

In much the same manner, automation leads to a more proactive finance function. Conducting a timely debrief to evaluate ad hoc requests for potential automation builds a deeper understanding of the business question and the effectiveness of the answer. This more thoughtful consideration of the context around the business question will allow finance to explore additional, related questions before they are asked. Additionally, incorporation of ad hoc capabilities into the enterprise analytics and reporting environment will free up capacity for increased exploration.

Asking provocative, experience-based, yet tactful questions of our clients is central to Ironside’s Analytics Advisory approach. These 6 key principles are meant to provide a basis of understanding that will allow a CFO to build a roadmap that leads to a modern, technology enabled FP&A function.


Rolling Forecasts Whitepaper




About Ironside

Ironside was founded in 1999 as an enterprise data and analytics solution provider and system integrator. Our clients hire us to acquire, enrich and measure their data so they can make smarter, better decisions about their business. No matter your industry or specific business challenges, Ironside has the experience, perspective and agility to help transform your analytic environment.