Press release: Financial planning and analysis and the data value chain


Far more efficiency and accuracy can be added to the FP&A function if organisations look across the entire data value chain.

By Upuli De Abrew, director at Insight Consulting. 

With data becoming more accessible and companies seeking to unlock its value through analysis, financial planning and analysis (FP&A) is becoming increasingly significant. However, decision-makers often do not trust the data powering their analytical applications, making financial forecasting and planning a difficult task. As a result of disparate data sources and time-consuming integration and consolidation processes, FP&A is usually fragmented and lacks input from operational areas.

When looking at overcoming the challenges of FP&A, a mistake that is often made is to focus on a narrow problem area, and to look for automation and technology to solve the specific problem. Far more efficiency and accuracy can be added to the FP&A function if organisations look across the entire data value chain, and focus not only on the technology at hand, but also on the business processes and people responsible for driving the business forward.

Access to high quality data
Ventana Research states that “77 percent of planning processes depend, to varying degrees, on having access to accurate and timely data from other parts of the organisation”. In many organisations, even those large multi-nationals with geographically and functionally distributed planning departments, people rely on spreadsheets to consolidate data and share it across various areas needing data for planning and analysis purposes. Not only is this process resource-intensive and error-prone, the sheer volumes of data mean that only aggregated information is shared, making the process of true integrated top down and bottom up planning unachievable. Creating plans across departments and organisations using aggregated data leads to inaccuracies and the inability to nimbly adjust and tweak plans as circumstances change.

By focusing on the data value chain, and understanding how functional performance metrics contribute to the overall financial performance of an organisation, analysts are able to map out the bottom up drivers for performance and therefore for planning and analysis. This points to a need for data literate, business process oriented individuals to drive this process using high quality, detailed data.

An organisation that I worked with recently had mis-matched sales and finance budgets, simply because the teams conducted their planning in silos. Sales worked their budgets based on discounts and promotions that they planned to apply during the new financial year, but finance had no visibility of this, so worked their budgets on a factor of the prior financial year, not understanding that Sales were targeting specific customers and regions with more aggressive discounts and promotions. Although they eventually massaged their top level numbers to match, tracking performance against these budgets became a nightmare, as Sales were analysing their performance in a different way to Finance.

Consider standardising on a trusted, quality source of data for all operational areas, complemented by tools and business processes which encourage integration, collaboration and better visibility across business functions.

A high degree of technological literacy is necessary for applying and employing advanced planning and analytics in finance. In addition, financial professionals have to truly understand the business in order to create planning and analytics solutions that talk to the true goals of the organisation. They also need to be able to communicate financial metrics to non-financial people in a way that they can be understood, and action can easily be taken on them. Non-financial people in turn, need a level of understanding of these financial metrics and what they mean for operational performance. This points to a need to grow a data literate culture across the entire organisation, encompassing technical know-how, business know-how, common terminology, and trust in the data that is being presented.

Data culture is a term that has risen in popularity in the last few years, and many organisations are turning their attention to creating data-literate cultures which value data and the decisions that they drive. Creating and maintaining a data culture requires constant work to ensure buy-in to data literacy programmes, and continuing to reward, recognise and communicate the value of data-driven decision making.

With the plethora of tools in the market today, the choice of technology available to support the people and processes around the data can sometimes be overwhelming. The key to choosing the right technology or technologies is to look first at the entire data value chain – where is data sourced from, where is it placed, what are the transformations it may go through, how is it used across the different functional areas, what data governance is in place or needs to be put in place, what other data is required in order to make meaningful, data-driven decisions, and how will this lend itself to planning and analysis. Focus on the business data first, then select the technology or technologies decision that will work best in each sector of the data value chain.

The challenges faced by most organisations in terms of financial planning and analysis are similar, no matter the industry or size of the business. Whilst it may be a daunting thought that so much has to be considered in order to get the full FP&A machine working optimally, with the right planning and prioritisation, it is possible to tackle the problem piecemeal and in an iterative manner, whilst keeping sight of the all-encompassing data value chain.