How FP&A Can Address the Challenges of Poor-Quality Data

A new report, “FP&A Guide: Get Your Data Right,” focuses on how financial planning and analysis teams can access more reliable data to produce useful insights.

In 2006, British mathematician Clive Humby famously coined the phrase, “Data is the new oil.” Today, data permeates every facet of our life and work. Yet one of the most common problems facing finance teams is poor-quality data and the inability to produce useful insights from it. According to a Gartner survey, organizations believe bad data is responsible for an average of $15 million in losses per year.

FP&A is well situated to lead the types of transformative efforts that result in quality data.

This issue strikes at the very heart of finance. Financial planning and analysis (FP&A) teams demand reliable, accessible data to effectively perform key finance functions, including: 

Planning: Timely, accessible, quality data drives the ability to receive and incorporate actuals, continuously re-forecast, deploy rolling forecasts, and deliver assessments.

Performance management: FP&A supports the design and deployment of appropriate metrics to hold businesses and individuals accountable. The management reporting process should deliver the right information (useful insights) to the right person at the right time in the right format. 

Financial analysis: FP&A is about applying financial expertise and business understanding through modeling, pro formas, and analysis. Data is the input, and the data management tools can elevate the team’s performance. That includes ease of variance analyses, exception reporting, machine learning applications for forecasting, and creating a recommendation engine for actions.

Poor data is also a roadblock to productivity. Research shows that FP&A teams spend 50% to 75% of their time wrangling data and managing data processes, according to the Association for Financial Professionals (AFP) report “FP&A Guide: Get Your Data Right,” underwritten by Workday. That creates a multitude of challenges, such as manual work, inefficient processes, errors, high system costs, missed business opportunities, and employee frustration. Poor data also hampers technologies such as machine learning, predictive analytics, and prescriptive analytics.

Data Done Right 

The good news? FP&A is well situated to lead the types of transformative efforts that result in quality data. 

“Finance, and FP&A in particular, is uniquely positioned to support leadership—they have the business acumen to understand what information members of a company’s leadership team need to make good decisions,” says mCloud Technologies’ Independent Director Betsy MacLean in the AFP report. “Taken together, this affords finance the opportunity to function as the missing link that can bridge the chasm many companies have between reams of data and meaningful, insightful, actionable analysis.”

The AFP report presents six case studies that show how organizations have successfully addressed the challenges of poor-quality data. The case studies outline the critical steps in the digital transformation process and act as a key guide for organizations ready to embrace a new planning model—one that puts accurate, up-to-date information at their fingertips.

Stop flying blind with bad data: Read the “FP&A Guide: Get Your Data Right” today.

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