Why FP&A Should Own Data Structure

Those who have done analysis work – and then tried to influence a decision-maker – know that ease of availability of the right financial data in the right structure contributes most to the efficiency (and sometimes even to the effectiveness) of the FP&A analysis process. All else being equal, companies with a well-thought-out data structure will be more efficient in their analysis, because relevant data is easier to obtain. They will also be more effective in their influencing, because they can present ideas and recommendations using data that management understands.

Drive Efficiencies from Your Financial Data Structure

In an era of FP&A downsizing, FP&A leaders can often identify meaningful effort losses by reviewing the structure of their financial data as compared to the ideal structure for analysis purposes. To get a quick read, simply ask an analyst to walk you through the steps and time spent to complete a recent analysis. Every hour an analyst spends acquiring and restructuring data is an hour not spent analysing the data and preparing an argument to influence the decision-maker.

Gap 1: Consistency with Key Performance Indicators (KPIs)

The key to influencing someone is talking about something that they care about. Senior business leaders care about taking better decisions, decisions that improve the financial metrics that drive their annual bonus – key performance indicators (KPIs). If the financial data available to your FP&A organisation is not structured in the same way as the metrics on which your senior leaders are rewarded, your FP&A team will either be ineffective because they aren’t using meaningful data as they try to influence, or inefficient as they invest time and effort to manually rearrange the data into a structure consistent with the KPIs.

One of the most common data structure outages is a failure to adjust for management changes. For example, a vice president (VP) of sales leaves the company and another VP takes over their sales area, while retaining their own sales area. The data structure in the system is not updated to reflect this new aggregation, leaving that as a manual task for the FP&A organisation for every piece of related analysis.

Another common example is organic sales – sales growth rates excluding the impacts of currency exchange rate fluctuations and of acquisitions and divestitures. While this data is often reported externally at a macro level, many FP&A organisations do not have access to this data at an operating segment level or lower and are forced to use US dollar (USD) sales data for their analysis.

Gap 2: Sufficient Detail to Influence Monthly Decisions

Full profit and loss (P&L) accounting data (all revenue and expense items) is often only available at the macro external reporting level. While useful for understanding the financial results and trends at the total entity or operating segment level, it is often not detailed enough for influencing the most common decisions related to product and service offering choices.

Consider an FP&A organisation that is tasked with analysing the relative profitability and before-tax profit contribution of various product offerings, as an input to deciding the marketing plan for the coming year. Pricing and variable costs (costs specific to manufacturing that specific product) are often available at the product level, but advertising spend is often tracked at the total brand level or higher. The analyst is then forced into a time-consuming effort to understand and then allocate the advertising spend across product families within brands – and potentially even to individual product sizes – to create a full view of profitability by product.

If, instead, the advertising budget was initially input and tracked at the product level (or at least at the product family level), the data collection stage of the analysis would avoid this allocation effort. It would allow the analyst to either finish sooner, or spend more time on the analysis and influencing aspects of the work.

Gap 3: Operational and External Data Structure

Exceptional FP&A organisations understand that internal financial data is not always the most influential type of data for every decision-maker or type of decision. The best FP&A analysts understand what data is influential to the decision-maker and make sure to include that data as part of their argument – financial or not. While doing this can be a huge win from an effectiveness standpoint, if the structure of the operational or external data structure is not managed ahead of time to meet the business needs, it can add significant time and inefficiency to the data-gathering portion of the analysis activity.

Operational data is often easier to influence as it is generated within the company. Depending on the type of analysis, examples could include how often customer orders go unfilled, plant or line-specific capacity utilisation data, the frequency of manufacturing quality issues on various products, or the amount of inventory held above safety stock levels. In each instance, being able to report the operational data in the same structure as the financial data ensures that an FP&A analyst can incorporate the data into their analysis activities with minimal added effort.

The structure of external data is more challenging to influence. Examples include market share data, share of shelf facings by retailer in a market, share of merchandising activities by retailer in a market, or share of advertising spend within a market. If you work for a large company and spend a lot to purchase the external data, your first step should be to simply ask the data supplier to change the structure of the data to better meet your needs. If you are working for a smaller company, or if the data supplier is unwilling to make the change, you’ll need to evaluate how important the data is to running your business. If it isn’t that important, you can stop buying the data. If it is, you’ll have to either restructure your internal management to better match up with the industry norm that the data provider is trying to support or live with the analysis inefficiency.

The Path Forward

Hopefully the examples above illustrate why the structure of financial, operational, and external data should be important to any FP&A organisation interested in gains in efficiency and effectiveness. The next steps are simple:

  1. Assign an FP&A data structure owner.
  2. Establish a multifunctional data team (FP&A, accounting, IT, functional business owner as relevant) to work with the data owner.
  3. Conduct a periodic review of data structure status and related analysis inefficiencies.
  4. Gain approval to the data structure changes with relevant C-level functional owner (chief financial officer (CFO), sales, marketing, etc.)
  5. Implement the approved changes.
  6. Enjoy the efficiency gains.

Of course, financial data is important to all functions. In most companies, finance and accounting does not own many of the FP&A monthly activities. For this reason, it is highly recommended to include all functions owning FP&A-related steps into the multifunctional data team. This way, all functions are represented when assessing and approving the data structure. The other functions should support this assessment since they will also enjoy efficiency gains. FP&A should own overall data structure but the company C-level leaders and their organisations need to be fully supportive.

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