What is Financial Data Analysis? Examples & How to Do It

Financial data analysis is the process of making strategic decisions based on the company’s financial data.
CFOs, finance teams, investors, and founders use financial data analysis to interpret the company’s financial performance, identify trends, and assess risks.

The person analysing typically starts with your company’s financial statements (income statement, balance sheet, and cash flow statement).

Financial software offers additional financial data, including a summary of key ratios and trend analysis, to help your analysis. Financial benchmarks, economic data, and other external data are also critical for context.

The end goal is to use the financial information in these statements to make decisions related to budgeting, investments, cost management, and strategic planning.

There’s a subtle difference between financial data analysis and traditional financial analysis. Here’s how they’re different:

Financial data analysis involves using financial data to assess a company’s financial situation and make data-driven decisions to achieve financial goals.

Financial data analysis requires knowledge of analytical methods, including ratio analysis, DCF analysis, and breakeven analysis.

The complexity of financial analysis methods varies based on use cases. No matter the method you use, having the right software solutions in your toolkit can make the analysis significantly easier.

Here’s what you gain by investing time and money into financial data analysis:

Data-backed decisions

Financial data analysis helps you make smarter decisions about investments, resource allocation, pricing, and expansion strategies.

A comprehensive understanding of the company’s financial health and business trends enables you to take calculated risks and achieve business goals faster.

Effectively manage risks

Financial data analysis includes assessing various parts of business for risk exposure.

For example, monitoring your liquidity ratios and ensuring you always have enough current assets to cover short-term liabilities is critical to minimising liquidity risks.

Financial data analysis also involves other risk management activities such as stress-testing financial scenarios and creating contingency plans.

Improved forecasting and planning

A careful analysis will help you forecast revenue, expenses, and cash flow more accurately. But is forecasting that important?

It is—38% of startups fail because they ran out of cash and couldn’t raise more capital. Preventing a cash crunch, or at least preparing for it by forecasting your cash needs, is more a question of survival than anything else.

Identify room for improvement

Financial data tells a story about your business—how much profit did you make? What was your cost structure like? Did your topline grow more or less than competitors? If you don’t like any of the chapters in this story, you can jump in, find the root cause, and fix it.

Identify profit drivers

Remember the Pareto principle? 20% of your products, services, or business segments account for 80% of profits.

It’s not always that black and white in reality, but the idea is that you probably have a product, service, or business segment that contributes a major share of your net profits.

Financial data analysis helps you find these products, services, or business segments. Double down on them by optimising resource allocation to bolster your bottom line.

Different financial data analysis methods are used based on your use case. Below is an overview of some simple methods as well as a deep dive into the more complex methods.

Here are some basic financial data analysis methods:

• Ratio analysis: Ratio analysis involves calculating key ratios from financial statements to evaluate performance, capital structure, efficiency, profitability, and liquidity. Common examples include the current ratio, quick ratio, and debt-to-equity ratio.
• Vertical analysis: Vertical analysis involves expressing line items on an income statement or balance sheet in percentage, usually to analyse the cost structure or capital structure. Line items in an income statement are expressed as a percentage of revenue, while line items in a balance sheet are expressed as a percentage of assets or liabilities.
• Horizontal analysis: Also called trend analysis, horizontal analysis compares data over consecutive reporting periods to look for growth trends and fluctuations in line items of financial statements. This helps spot patterns in revenue, profitability, and financial areas where the company is growing, stagnating, or suffering.
• Cash flow analysis: Cash flow analysis involves using the cash flow statement to understand the sources and uses of cash. For example, you always want your operating cash flow to be positive. If it’s negative, your operations aren’t generating cash, and if that continues, your business is guaranteed to fail.

Now that we have the basic methods out of the way, let’s dive into more complex financial data analysis methods. Note that it’s not an exhaustive list of financial analysis methods.

DuPont Analysis

DuPont analysis is just another way to measure your return on equity (ROE). Instead of dividing net income by shareholder’s equity, DuPont analysis uses the DuPont formula:

The idea is to break down ROE into three components:

• Net profit margin: Indicates how much of the return on equity is coming from the company’s profits.
• Asset turnover: Reflects the efficiency with which assets generate sales, indirectly contributing to ROE.
• Equity multiplier: Measures your company’s degree of financial leverage and how much of the assets are financed through equity.

Traditional ROE provides a bird's-eye view of how well your company uses shareholder’s equity to generate profit.

The DuPont formula breaks it down into three key components that allow you to pinpoint factors that positively or negatively impact ROE.

For example, companies often generate excellent returns on equity. But when you apply the DuPont formula, you can tell if the high ROE is coming from genuine operational improvements or financial engineering (i.e., taking on debt).

Discounted Cash Flow (DCF) Analysis

DCF is an asset valuation method. You can use it to value any cash-generating asset—if you’re a farmer, you can use it to value a tree.

To calculate a DCF value, you need two things:

• Expected future cash flows: You need the estimated cash flows an asset will generate over its useful life.
• Discount rate: You need a discount rate at which you’ll discount the cash flows to calculate their value today. If you’re valuing your company’s equity, the company’s cost of equity can be your discount rate. If you’re valuing the entire firm or preparing a capital budget for a new asset, you can use the weighted average cost of capital (WACC).

Here’s the DCF formula:

Where:

CF = Estimated cash flow

r = Discount rate

Suppose you plan to sell one of your subsidiaries for £100,000. It’s a cash cow but eventually, let’s say five years from today, will need to be liquidated. In the meantime, your financial data analysis suggests that it will generate net cash flows of £20,000 per year. Your WACC is 5%.

Here’s what its DCF value would look like:

Here’s what this means: The value of £20,000 received over the next five years is £86,680. If you’re able to sell the subsidiary today for £100,000, you should.

The reason? The net present value (NPV) on this transaction is positive because the discounted cash flows are lower than the amount you’re receiving today:

£13,320 = £100,000 - £86,680

Breakeven Analysis

Breakevens are easy. When revenue and expenses are equal, you break even. Right?

Yes, but there’s a caveat. Expenses can be:

• Fixed: These expenses are fixed no matter how much you sell or if you sell at all. Rent and salaries are fixed expenses.
• Variable: Variable expenses are incurred only when you sell. Cost of goods sold (COGS) and commissions are variable expenses.
• Semi-variable: Semi-variable expenses have both fixed and variable components. For example, you may have a fixed base charge for electricity each month plus a variable component that depends on usage. Similarly, you may offer a salesperson a fixed salary plus a commission that varies with sales.
• Semi-fixed: Semi-fixed expenses are fixed up to a certain extent but increase in discrete steps beyond a specific threshold. For example, your warehouse rent is fixed. But as operations grow, you might need to rent another warehouse.

Factoring in all of these costs to calculate an accurat breakeven can be tricky. Let’s use an example to simplify this. Here are company ABC’s costs:

• Fixed: £10,000
• Variable: £10 per unit sold
• Semi-variable: £100 (fixed); £10 per unit sold (variable)
• Semi-fixed: £5,900 to hold 1,000 units in inventory in the warehouse

To calculate a breakeven, we need to focus on the contribution per unit:

Contribution Per Unit= Price - Variable Costs

Let’s say company ABC sells its product for £100. The contribution per unit would be:

£80= £100- [£10 (variable) + £10 (semi-variable)

This means the contribution margin (per unit contribution expressed as a percentage of the price) is 80% (£80/£100).

Company ABC’s total fixed costs are £16,000 [£10,000 (fixed) + £100 (semi-variable) + £5,900 (semi-fixed)].

We’ve assumed company ABC only holds 1,000 units in the warehouse.

Here’s the formula to calculate company ABC’s breakeven:

• In terms of units: Breakeven = Fixed Costs / Contribution Per Unit

• 200 units = £16,000 / £80

• In terms of revenue: Breakeven = Fixed Costs / Contribution Margin

• £20,000 = £16,000 / 0.80

In our example, the company’s breakeven would be 200 units or $20,000 in revenue.

Here’s a quick statement showing the breakeven:

Scenario and Sensitivity Analysis

Scenario analysis evaluates your company’s financial condition under certain assumptions.

Suppose you expect a major economic crisis over the next quarter and you want to assess how this will impact your revenue and input costs.

You list three possibilities (minor economic crisis, major economic crisis, and no economic crisis) and project your financial statements under each possibility.

That’s scenario analysis. It answers your business’s “what if” questions.

Sensitivity analysis is more of a stress-testing technique.

Unlike scenario analysis, where you test outcomes under a group of assumptions, sensitivity analysis tests how sensitive outcomes are to changes in one assumption while holding others constant.

Suppose you plan to invest £100,000 in new machinery to increase production capacity. You want to assess the potential ROI under the following scenarios:

• Best-case scenario: High product demand and low financing costs
• Base-case scenario: Average demand and moderate financing costs
• Worst-case scenario: Low product demand and high financing costs

Here’s what the scenario analysis may look like:

If you tweak any assumptions further while holding others constant, you’d be doing sensitivity analysis.

For example, if you were to assess the best-case scenario, assuming another 10% increase in demand than what you’ve projected, you’ll be able to see how sensitive your net profit is to change in demand.

In our example, your net profit would change to £271,000:

While the specific steps involved in financial data analysis depend on the method of your choice, here’s a general overview of key steps

1. Define your objective: What’s your goal? Are you trying to stress-test your finances? Are you trying to value your business? Are you trying to calculate a breakeven for a new venture? Pick an objective because that will determine the data you need and the method to use.
2. Gather financial data: Collect the data you need based on your objective. Most of the information is available on your financial statements. Projections and assumptions needed for some analysis methods require manual work. Metrics like inventory turnover, gross profit margin, and return on equity are generally available on finance software if you use one.
3. Select a financial data analysis method: What are you looking for in your data? Select a method that helps you answer your questions. Use our guide in the previous section to find a method that suits your needs.
4. Interpret the results and make decisions: Interpret the results of your analysis. If you’re not a finance expert, seek input from an expert. Make decisions based on your analysis and track progress to make sure you achieve your financial goals.

Let’s look at a real-life example of how financial data analysis works. We’ll use an (overly) simplified example of scenario analysis.

Suppose you work at Virgin Atlantic. You’ve been told to assess the impact of the rise in fuel costs because of the ongoing conflict in the Middle East. Here’s Virgin Atlantic’s income statement pulled from its latest annual report:

Holding everything else constant, you expect fuel costs to rise by 10%. Assuming Virgin flies the same number of miles as last year, the fuel costs could jump to £925.1 million—£84.1 million more than last year. This could make Virgin’s operating income negative and spell trouble for the business's survival.

It’s also clear that Virgin currently has a net loss and the primary reason for that is the finance expense. It’s a classic example of an overleveraged company that’s not generating enough operating income to cover its interest expense.

As far as the rising oil prices are concerned, it’s only going to add fuel to fire and make Virgin Atlantic’s debt problem turn into a nightmare.

Note: This is a simplified example used to explain how scenario analysis works. There are plenty of variables to factor in, such as non-cash controllable costs like marketing and one-off expenses like impairment and profit/loss on the sale of property, plant, and equipment.

A screen full of figures can feel overwhelming, but with the right tools, you can tackle the complexity of financial data analysis more effectively. Here are some tools you can use

1. Cloud-based financial software

Cloud-based financial software stores financial data. No need to call your accountant asking for the data you need for analysis—cloud-based solutions are remotely accessible, so you can access data from any device with an internet connection.

Top solutions generate automated reports that include ratios and other metrics that help you analyse your company’s financial status.

Financial software is also instrumental in helping you spot and fix financial problems. For example, if your profit margins were low last year because of high inventory carrying costs, you can track your inventory turnover and inventory-to-sales ratio.

Finance software can also help you monitor inventory levels so you can proactively manage them and improve your profit margins.

If you’re looking for cloud-based finance software, book a demo for Access Financials to learn more about how it can help with financial data analysis and transform your finance processes

2. Data visualisation tools

Data visualisation tools transform complex financial data into snackable visuals like charts and graphs. This helps you spot patterns, trends, and anomalies faster, especially when working with large datasets.

Visuals are a great way to share financial insights with non-financial professionals as well. You can add them to executive presentations and financial reports to quickly communicate insights without overwhelming the audience with data.

Tableau, Qlik Sense, and Google Data Studio are great visualisation tools to consider if you plan to add a data visualisation tool to your stack.

3. Python

Not all financial professionals know Python, but it’s a powerful financial data analysis tool.
It offers extensive libraries and integrates with various data sources, which makes financial data analysis easier, faster, and more accurate. From building custom DCF models to using machine learning to predict financial data, Python offers extensive advanced analytical capabilities.

The only problem? You need basic Python programming skills to be able to use it. Many finance professionals now take online courses to develop this highly valuable financial data analysis skill.

4. Financial modelling tools

Financial models are the cornerstone of financial data analysis. It’s a vital part of many analysis methods, including DCF valuation, scenario and sensitivity analysis, and budgeting.

Financial modelling tools like Excel and Anaplan offer various capabilities to simplify the modelling process.
Instead of manually creating columns and adding figures to a spreadsheet, use a financial modelling tool that can pull data from various sources and build ready-to-manipulate financial models.