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In a world where unexpected events are now part of the normal course of business, it should be no surprise that business planning has become the pulse of corporate finance. Its discipline extends into the rest of the organization and has become a practice so valuable that 82 percent of executives consider it to be critical to enhancing revenues. Planning frequency is also increasing: 74 percent of companies are now planning more often than they were five years ago.
Planning, as businesses increasingly recognize, remains a key to better decision-making. Yet making the right business decisions and executing the right plans isn’t always simple. Human tendencies, trends, and perceptions can all influence strategic business decisions. These factors, among other judgements, can creep into the planning process and lead businesses toward biased results.
The evolution and transformation of finance can help prevent this bias from spreading throughout the organization. Transitioning beyond reporting to business partnering, identifying and removing bias within FP&A practices, and adopting toolsets that can empower finance-led planning rather than constrain it, can help finance teams deliver an unbiased, reliable, and informed view of where the business is headed.
Volatile market environments, stronger business insights
FP&A professionals have traditionally functioned as stewards of data. Over the years, operating as this trusted source of information has led to the elevation of FP&A’s value and importance. Its function provides the business with timely, comprehensive, and reliable reports—reports that are usually based on historical information and those that illustrate actuals versus targets. Yet the proliferation of data and technology presents an opportunity for finance to transform even further.
The world today operates at a far faster pace than in previous years. Heightened volatility, uncertainty, and risk put pressure on executives to make better decisions faster and to do so, many expect their finance teams to deliver more intelligent, forward-looking insights. These factors have helped change the focus of corporate finance teams and are shifting emphasis from hindsight to foresight.
Where does foresight begin? With data. Rapidly changing business environments mean that corporate finance teams need to dig deeper into data and they need to consider and integrate with additional sources. Well-versed in traditional financial data, finance teams now need access to operational data such as demand for products and services, customer churn, risk factors, econometric trends, supply chain metrics, and employee trends to help produce stronger business foresight.
Incorporating all relevant internal and external data is integral to deeper insights and better decision-making. The inclusion of HR or employee records alone, for example, while excluding data around demand, exchange rates, or commodity price volatility can result in an incomplete picture that can then be used to steer the business down the wrong path.
As finance increasingly focuses on incorporating external and non-financial data, it needs to re-evaluate its approach to forecasting. This could include better understanding lead-time to respond and its impact on forecast time horizons or how to match frequency of forecast or reporting updates to business variability.
Sleep, creep, leap: Where bias can sneak into finance
Forecasting, a key tool for steering business performance, is an activity that can be vulnerable to biases such as cognitive bias or incentives. Creating run charts that measure latest forecasts compared with actuals (forecast errors) is a good practice that can help identify the presence of bias which then need to be examined and eliminated. Mathematical models (e.g. driver-based models) and statistical models can be used to complement human judgement to reduce the presence of psychological bias. For machine-generated, algorithm-based forecasts, data integrity and cleanliness along with human interaction is critical to producing unbiased forecasts.
Testing assumptions through “what if” analysis is another critical action that can help finance reduce biased decision-making. This analysis is key to aligning operational plans to corporate objectives such as connecting demand and supply plans. As an example, if demand hovers with an over/under of three percent, businesses can employ a series of “what if” analyses to test and understand various impacts on supply. Technology that can support these activities in real-time is especially advantageous, as lags in decision time can prove costly in fast-paced markets.
How technology can empower corporate finance
As data sets deepen across the organization, corporate finance can create value by connecting data to insight, decisions, and action. To do so, their technology needs to be able to support these connections and empower a finance-led planning approach. Cloud-based Connected Planning solutions can provide real-time capabilities that help further reduce biased decision-making and deliver stronger business foresight to the business.
An analytical engine and flexible modeling platform can help teams quickly synthesize information from disparate data sets and infer trends that translate into insight through the use of predictive analytics. Here are some additional benefits of planning technology that can help reduce bias and enable forward-looking insights:
- Charts and graphs can be created to measure monthly forecast errors and identify bias such as cognitive, social, or motivational biases. The processes around forecasting can then be restructured to reduce the likelihood of bias. For example, finance teams can actively encourage views that differ from the norm during forecast review meetings to minimize the impact of social bias, such as the tendency to conform to the group.
- Mathematical models can reduce bias by relying on drivers and rates to determine forecasts. For instance, many businesses forecast volume and price separately in order to generate a revenue forecast. And many businesses use material volumes, material prices, usage, and wastage to determine cost of goods sold forecasts. These mathematical, or driver-based, models can reduce the impact of human judgement and thus the impact of cognitive bias.
- Statistical models applied to historical data can help create more reliable and less biased baseline forecasts. For instance, a time-series regression model is applied to 12 months of sales data in order to create a baseline forecast for the next six months. Human judgment around the impact of initiatives that have yet to take hold are added to this baseline to create the final updated forecast.
- Multi-dimensional analysis of data can help finance teams recognize trends from dimensionally rich data. For example, consider that green golf shirts have been selling better in Raleigh for the past 12 months, whereas white t-shirts have been selling better in Cincinnati during the same period. Dimensions of color, type, and city can be analyzed quickly to spot trends that inform actions and are reflected in updated forecasts.
Enterprise-wide planning solutions that connect plans throughout the business bring foresight for tomorrow. By embracing the new opportunities ahead for corporate finance and improving decision-making through data and technology, businesses can set the stage for more insightful and forward-looking decisions. Volatility and risk may bring uncertainty into many businesses, but technology can empower finance to steer it with confidence—and without bias.
Tony Levy is the Global Head of Finance Solutions at Anaplan.