3 Tips for Running Better Ad Hoc Analyses on Business Finance Data

Conducting ad hoc analysis is quite important for modern business enterprises

As one of the best sources of data for business analysis, a company’s financial data form the basis of working capital projections, required reporting to tax authorities, and even overarching business strategy.

Indeed, financial data holds a wealth of information for businesses seeking to grow their footprints. A company’s financial posture offers FP&A teams the opportunity to offer CFOs the insights they need to build resilient businesses.

However, analyzing financial data is challenging, especially on an agile basis. As companies gather more data than ever before, shortcomings in data governance and quality hamper analysts’ ability to dig deep into datasets. You can’t rely on the results of your “what-if” scenario projections if you only have access to metrics from last quarter.

As the pace of business speeds up, senior executives need on-demand insights to make informed decisions about market priorities and opportunities as they come up. The need for fast insights, therefore, shines a light on ad-hoc reporting, where analysts must query and retrieve data on the fly.

When combined with shortcomings in data analysis processes and the lack of training in data science principles, most companies run the risk of deriving incorrect conclusions. Here are three tips to run better ad-hoc analyses of your financial data.

Review Revenue Data Sources

Companies collect revenue from dozens of sources these days, and this makes ad-hoc analysis challenging. Revenue data comes in many formats and structures, and ensuring that these data comply with your own storage schemas is tough.

However, the issues with these datasets go far beyond storage concerns. Often, data from revenue sources arrives in forms that don’t lend themselves well to ad-hoc analysis.

For instance, revenue collected from iOS or Google’s Play Store arrives as a lump sum of data, itemized only by transaction without further context. Examining dataset context requires you to dig deeper into app interaction metrics and customer data repositories if you’re going to effectively correlate them to revenue trends. In contrast, POS terminal revenue data tends to be detailed and contains high granularity.

Standardizing revenue data granularity is essential if you wish to run analytics of all data sources. Fail to do this, and you’ll create data silos that offer incomplete views of your revenues. Even worse, you’ll have to manually transform and transfer data from one source to another before running analytics.

These processes introduce errors in analysis that lead to poor insight. Such tedious processes only add friction to ad-hoc analyses. For instance, you cannot run ad-hoc reports on datasets stored in spreadsheets, since the rigid nature of a spreadsheet does not allow you to modify parameters on the fly or dig easily into datasets stored elsewhere with new queries.

Neither can you incorporate real-time revenue data into your reports, since each silo will be updated at different times? Standardizing revenue data granularity might cause a loss of insight from some sources. However, it will allow you to automate data collection and cleaning, leading to flexible ad-hoc reporting functionality.

Use Bottom-Line Metrics

Analysts often struggle to convey the impact their models have on the business. A big reason for this is the use of irrelevant metrics. For instance, using an unquantifiable metric such as customer satisfaction to convey the impact of an innovative financial model is pointless in an ad-hoc report, since top-level executives can’t directly connect customer satisfaction to sales and revenues.

Bottom-line metrics such as ROI and IRR cut through the mist and speak directly to how well a business’s investments and projects are performing.

For instance, comparing revenue growth YOY is standard in most businesses. However, if your company is a high-growth startup, comparing quarterly revenue instead of annual figures in your ad-hoc reports makes more sense. Some early-stage companies will benefit by comparing monthly revenues since the growth they experience is exponential.

While ad-hoc reports offer you the freedom to dig into data on the fly, it’s important to define the metrics you’ll use beforehand to ensure you’re always measuring the right results. In short, explore your data, but be cautious of adopting metrics or benchmarks as you go.

When using these metrics, make sure you understand what they convey. For instance, ROI and IRR quantify returns, but they measure completely different conditions. ROI measures the overall return, while IRR measures the equivalent discount rate in an NPV calculation. In ad-hoc scenarios, ROI might offer better insight compared to an IRR’s longer-term focus. IRR requires time and discount rate inputs to give it more context.

In ad-hoc scenarios, hastily estimating these numbers can build errors into calculations that will exaggerate results. The discount rate you arrive at (the goal of an IRR calculation) might be wildly off as time unfolds. Macro factors such as central bank interest rates might render your calculations obsolete. A simpler ROI calculation will offer more flexibility and quick insight into an investment’s attractiveness.

Quantify all data points using dollar amounts to deliver maximum impact in your reports, and use data visualizations to communicate results when reporting to senior executives. While the financial world relies on data tables, they don’t lend themselves well to quantifying business impact to non-financial audiences.

Account for Revenue Model Biases

When running ad-hoc reports, watch out for the impact biases have on final results. By definition, ad-hoc reports look at the business impact right now and use real-time data. However, the diversity of a business’s revenue models might skew results considerably.

For instance, analysts at a firm depending on SaaS revenue will generally witness consistent trends for the most part. You’ll see costs spread relatively evenly, while revenues will also be predictable, assuming the firm’s products are seeing traction and the acquisition funnel is humming.

However, a freemium model works differently. Revenues per user will jump significantly as users convert to premium features following the free trial period in cohorts. Costs per user are also greater upfront in this model since the company will collect fewer revenues, leading to skewed margins per user.

Thus, when designing ad-hoc reports, you must decide whether measuring business impact per user makes sense, given how volatile the user count is.

Keepi the big picture in mind by accounting for seasonal trends and pricing model adjustments when designing ad-hoc reports. A company’s revenue model, after all, might create impact trends in consumer behavior as well. Thus, always examine the context in which revenue and financial data are collected.

Challenging but Rewarding

Financial analysis is challenging, but the results of ad-hoc financial reports help a business capture a snapshot of its performance. Remember to examine biases in data and install proper governance processes before drawing conclusions from ad-hoc reports. Follow the tips in this article, and you’ll ensure in-depth insights that future-proof your business.