According to the survey:
- Only one in three organisations use a “specific system designed to provide analytical support for their FP&A function” and more than half are not using such a system.
- Less than half of respondents consider Excel “effective in supporting the analytical work in their company FP&A functions”.
- Forty per cent rate Excel’s effectiveness for supporting analytical functions as a “three on a five-point scale.”
Below is an actual case to illustrate the value of BI and analytics for FP&A functions. It examines some challenges, and discusses realistic expectations – all of which influence a company’s willingness to invest in technology support for FP&A.
BI and Analytics Opportunities
A company that we’ll call ‘FPI’ manufactures widely-known branded food products. A US$3bn roll-up of acquired brands, plants, people, and systems, FPI lacked a well-designed data warehouse to integrate operational and financial data from its various business systems. As a result, it also lacked the BI and analytics needed to enable FP&A effectiveness and help improve profitability. During extensive interviews, FP&A professionals identified a number of BI and analytics gaps:
- Senior FP&A professionals were handcuffed in their ability to dynamically measure, manage and improve the financial performance of FPI’s supply chain and production operations.
- Senior FP&A professionals were handcuffed in their ability to dynamically support marketing, sales and business development teams with stock keeping unit (SKU)-level and customer-level profit and loss (P&L) statements and variance analyses in relation to annual operating plans, brand plans and quarterly updates.
- Plant controllers reported that it was difficult to manage drivers of plant profitability – including production costs, batch yields and equipment effectiveness – in relation to forecasted and actual order volumes and mixes.
- Plant controllers lacked standardised historical information about performance trends in sales volume, production volume by SKU and actual raw materials usage.
- Plant controllers lacked standard automated variances analyses in relation to operating budgets, quarterly budget updates and standard costs.
Effectively, these BI and analytics gaps made it very difficult to optimise manufacturing, supply chain, and sales and marketing expenses and meet profit expectations.
Challenges and Expectations for BI and Analytics
The FP&A professionals at FPI were keenly aware that they needed better BI and analytics to substantially improve their ability to have a profit impact. Despite that awareness, FPI did not make the necessary technology investments to automate the full-range of FP&A tasks and overcome the BI and analytics gaps. This case illustrates the challenges and unrealistic expectations for BI, analytics and FP&A systems that need to be addressed if FP&A professionals are to have tools with the ‘valuable attributes’ that survey respondents to the 2014 gtnews FP&A Technology Survey say they need. These include:
- Challenge: Ignoring data integration fundamentals. Sound data integration practices have been known for nearly two decades, yet companies still think they can avoid the work required to establish a unified repository of fundamental operational and financial data. Without a well-designed data warehouse or data mart to support their tools, FP&A professionals are left to fend for themselves when acquiring the data they need, resulting in multiple versions of ‘business reality.
- Unrealistic expectation: That a single, off-the-shelf FP&A system can be expected to solve all the functional needs identified in the FP&A technology survey. Simply put there is no ‘silver-bullet’, despite what business professionals wish and vendors want us to believe. Best practices have shown that an effective FP&A systems architecture will be based on an underlying data warehouse or data mart connected to commercially-available BI, analytics and FP&A tools.
- Unrealistic expectation: That a unified repository of fundamental operational and financial data for enterprise cross-functional use can be built and maintained by FP&A professionals. The fundamental challenge to meeting the full range of ‘desired functionality in a FP&A support system’ identified by the technology survey is the lack of an underlying data warehouse or data mart. Building such a data structure requires a range of business and technology skills that are not often found within a single professional – even in those who have been in the data management business for years and who have had extensive specialised training.
- Challenge: Overcoming the perception that FP&A can have the desired business impact while using current methods. Organisations in many different industries operate within complex data environments, because the financial and operational business systems used to run their businesses are generally not integrated and not built on a common data models. To cope with this data complexity, organisations have evolved various workarounds – and these have not been effective in delivering the full range of desired systems support for FP&A tasks.
Investing in BI and Analytics
To move forward, companies must recognise that BI, analytics and FP&A tools can meet functional requirements if they will only invest in integrating data to support multiple FP&A uses. The choice is actually straightforward – continue to avoid the investment, or invest in an FP&A system built on a unified data warehouse or data mart with integrated operational and financial data that will support the full range of FP&A tools and tasks.
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