FP&A of the Future: Agile Planning and Predictive Analytics

The future of FP&A will not be slow and static. FP&A will borrow from many of the traditional planning and analysis processes and will make them more dynamic and accessible to companies of all sizes. In addition, the department will be facilitated by more powerful technology platforms and collaborative teams that provide insight instead of crunching numbers.

However, the looming question many organizations currently have and will have in the future is: “How do we bridge from where we are today to where we want to be tomorrow?” The key lies in the integration of predictive analytics and agile planning.

Demystifying definitions

Predictive analytics does not just mean predicting the future. It is about assessing and verifying insights that provide an indication of relationships and an ability to predict potential results.

Agile planning does not just mean planning quickly. It is about having the right people, with the right authority granted, enabled by the right platforms, to pursue the right initiatives fluidly and therefore make incremental but measurable organizational progress.

Combining integrated FP&A with predictive analytics and agile planning may be as challenging as saying my definitions in a single breath. Agile planning and predictive analytics, together, are all about process management and process leverage.

Pre-requisites for successful predictive analytics

When executed successfully, these processes allow a company to understand its data more thoroughly through the utilization of software and the identification of key drivers along with their relative cause-and-effect relationships. If insights are verified confidently, organizations can plan more about the future. This requires positioning people and enabling them, not just based upon talent but through technological capability. Thus allowing them to analyze, forecast, and reforecast promptly, flexibly, and at scale. In some cases, humans themselves do not do forecasting – machines conduct it automatically – but it is humans who instruct the machines what to do.

Last year, a food and beverage company discovered that finance and operations could not effectively analyze the relationships between drivers [CC1] [CS2] of consumer behavior, purchasing decisions, and underlying financial data. For one, there was basic inconsistency in the data that was being provided from multiple disparate systems. Core data was presented differently depending on which source it came from. The result was that any kind of decision-making based ‘upon data analytics was impossible at worst, and slow at best.

If the company did [CC3] [CS4] have sound data, predictive analytics for a food and beverage company might provide an assessment of customer behavior and meal demand. Within the context of individual retail location and hours of operation, predictive analytics could recommend pricing strategies for meal offerings and related promotions at each retail location. Further, in consideration of Covid-19, it could help identify customer value perception and thus suggest strategies for takeout, drive-through, curbside-pickup, and delivery options. Finally, product and meal demand would trickle down into purchasing, inventory, and cash flow decisions which would all be vital for financial management. Predictive analytics may be encouraged within just a single dimension of operations, but clearly it offers far-reaching benefits.

Improving data quality

When data is fractured and multi-dimensional, such as with this company, it poses an increased risk that we are making decisions relying upon poor quality data or even gut instinct. A key factor in agile planning and predictive analytics is being able to retrieve information that is timely, accurate, high-quality, and easily accessible.

Ideally, data should flow into a sort of hub, where it can be structured into what we in FP&A refer to as the “single source of truth”. Not only does data exist in a clean and usable form, these hubs of data also permit the establishment of relationship hierarchies which allow FP&A professionals to drill both up and down to high-level views and granular levels of detail. It is the existence and refining of these hierarchies and relationships that allow analytics to be meaningful, and thus more relevant.

Ensuring smooth analytical processes

Upon establishment of a hub for sanitized, accessible data, FP&A should be encouraged to develop uncomplicated, repeatable analytical processes. The goal of these processes is to leverage analytics for greater intelligence and thus facilitate better planning. Ensuring these processes run consistently also means that analytics can be applied and revisited, not just by the individual conducting analysis, but by others who may be collaborating on the initiative. There is little benefit in achieving software analytical and planning capabilities if there is a lack of repeatable processes within the FP&A function.

Achieving accurate predictions

When agile planning and predictive analytics are rolled out successfully, more accurate predictions may be achieved through rolling forecasts and cross-functional collaboration. Contribution will come from financial and non-financial groups alike as cross-organizational knowledge is encouraged rather than siloed. Reforecasting may take place in sprints with shorter lead-times because of the benefits of better predictive insights.

Challenges along the way

Perhaps the greatest challenge in the marriage of agile planning and predictive analytics is that, if rolled out successfully, as soon as insights are offered for planning, they may render themselves obsolete. Let me offer to explain.

Integrated agile planning and predictive analytics means we, paradoxically, never actually reach our objectives. Every transaction and every executed decision that takes place in an organization creates new data from which we launch our next set of predictive analytics and assumptions. That is, assumptions often do change as a new reality presents itself. This infers that the prior assumptions we relied upon to arrive at our decision may no longer remain sound. It also suggests we are in a state of continuous learning and development.

Just as we cannot turn back time on humanity to introduce today’s innovations to our predecessors, we cannot build time-relevant, high-quality assumptions on outdated data. When organizations aspire to embark upon greater capabilities in agile planning and predictive analytics, there is not a destination that is reached. It merely allows organizations to obtain greater capabilities that provide more effective new foundations from which to improve. The objective is to bypass arriving, as arriving may mean relying upon outdated assumptions. It is imperative that platforms and processes be managed with a mindset of incremental improvement, rather than with an destination in mind.

Summary: FP&A need to embrace new developments

We are entering into an era where solutions[CC5] [CS6]  and processes are increasingly flexible, scalable, agile, and collaborative. They are moving downstream in terms of ease-of-use and economic affordability which permits smaller organizations to stretch their FP&A capabilities far earlier in their maturity.

FP&A teams need to be assertive about adopting decision-making processes that are growing in their integrity and do not just offer rapid data outputs. As organizations increase in size and complexity, predictive analytics and agile planning will be more important than ever. This requires involvement of platforms and processes that can scale when necessary, rather than reinvent themselves at every stage of the FP&A journey.