Financial data analysis: Definition, methods, examples and best practices

Financial data analysis is the process of using software to generate insights about a company’s financial performance. The end goal is to unearth actionable insights to guide strategic decisions. The process utilises a range of data and techniques, including DuPont analysis, discounted cash flow method and ratio analysis.

• Financial data analysis involves using software to generate insights about a company’s financial performance
• The end goal is to unearth actionable insights to guide strategic decisions
• The process utilises a range of data and techniques, including DuPont analysis and discounted cash flow method
• Financial data analysis helps companies manage risk, improve forecasting, identify performance gaps and spot profit drivers
• Challenges include poor data quality, manual processes, siloed systems and limited forecasting capability. 

It involves using technology and techniques to extract actionable insights from a company’s financial information. It relies on using financial management software or other financial data analysis tools to perform ratio, DuPont, discounted cash flow, breakeven, scenario, sensitivity analyses.

Insights gleaned from these analysis methods are interpreted by the finance team, which is made easier by software that visually depicts data via charts and graphs. The team can then offer financial management strategies to the company’s leadership based on the interpretation.

Financial data analysis vs financial analysis

The key difference is that the former is forward-looking, while the latter looks at the past and present.

Financial analysis involves evaluating core financial statements via more manual processes, calculations and interpretation. It aims to assess a company’s financial performance to evaluate its viability, within a defined historical context. 

Meanwhile, data analysis has a broader scope. It involves using advanced software, data mining and predictive modelling to analyse financial statements and non-financial data (like customer data and market trends). It aims to provide actionable insights to forecast performance, manage risk and create strategic plans. 

It plays a crucial role in helping a company turn raw business data into actionable insights and predictive intelligence. Financial data analysis helps inform strategic plans and decisions to navigate complex business environments, manage risks and assess financial performance.

Data-backed decisions

It gives business leaders a comprehensive, objective view of the company’s financial health, helping eliminate subjective biases. This allows them to make better decisions concerning capital investments, resource allocations, pricing strategies and expansion plans.

Risk management

Leaders gain a view of the company’s future risk landscape and vulnerabilities, allowing them to formulate mitigation strategies. Data analysis can pinpoint red flags like future cash flow issues, overexposure to debt and potential market risks (like increases in the interest rate or commodity prices).

Improved forecasting

Analysis of historical data and trends allows for finance professionals to predict variables that impact financial performance, like future sales and operating expenses. Cash flow forecasting software and other analysis tools help automate this process.

Identify performance gaps

Data analysis helps highlight where the company is underperforming compared to historic performance, forecasts or industry benchmarks. A ratio analysis, for instance, can reveal operational issues that may be invisible when simply looking at raw dollar figures.

Identify profit drivers

Profitability ratio analysis and the DuPont analysis (which we’ll explain later) help finance teams break down which products, services or aspects of the business are driving profit.

The type of analysis you’ll perform rests upon several factors, including your objective, the data sources used and when a decision needs to be made (based on the analysis). Let’s explore the three fundamental categories of analysis:

Fundamental analysis

This aims to measure the intrinsic value of a company, to see if its current market price is higher or lower than its ‘true’ value. Fundamental analysis focuses on evaluating earnings quality, asset utilisation and long-term debt sustainability.

It involves evaluating the income statement, balance sheet and cash flow statement. It can also factor in non-financial data such as that regarding market conditions, the company’s competitive position and its business model and operations.

One way of gauging a company’s intrinsic value is to perform a discounted cash flow analysis. We’ll run through a practical example of how to calculate this later in this article.

Technical analysis

This analysis is typically used by share traders but can also be used by a company’s finance team when issuing shares. It focuses on predicting future share price movements and trading opportunities.

Technical analysis gleans these insights by utilising historical share market data, including price movements, trading volume and market volatility.

Technical analysis example

Imagine a company that’s planning to issue a second offering of its shares to raise capital for a major acquisition. It needs to work out when is the most optimal time to issue the shares.

The finance team studies the daily price chart and volume trends for the company’s stock, using the moving average convergence divergence (MACD) to assess price momentum. This technique is a momentum indicator that reveals changes in the strength, direction and duration of a price trend.

The MACD analysis reveals that there’s an increase in positive buying momentum in the past week, which is often a harbinger of a short-term price increase. Trading volume is also high, which indicates significant market liquidity.

Given this insight, the finance team advises the company’s leadership to issue the shares immediately to take advantage of the strong buying momentum in the market.

Ratio analysis

A ratio analysis involves calculating various financial ratios to gauge a company’s financial performance and health. Types of ratios and what they measure include:

• Liquidity ratios: A company’s ability to meet short-term debt obligations
• Solvency ratios: A company’s ability to meet long-term debt obligations
• Profitability ratios: How efficiently a company generates profit from operations
• Efficiency ratios: How effectively a company uses assets to generate sales

Ratio analysis example:

Imagine a finance team wants to understand how well their company is turning inventory into cash. To find this out, it will calculate the inventory turnover ratio, which reveals how many times inventory is sold and replaced over a specific period.

Essentially, this ratio will help the team understand if the company is buying the right amount of stock and if it's selling it fast enough. The formula they will use is:

Inventory turnover ratio = cost of goods sold / average inventory

The team calculates that the company’s inventory turnover for the current quarter is three, which means it’s selling and replacing inventory three times in that quarter. It compares this to the ratio from the previous quarter, which was five times, suggesting operational issues.

With this insight, the team looks into possible causes. They then flag with the operations and sales departments that inventory is building up too quickly, tying up working capital and increasing the risk of stock becoming obsolete.

It recommends that sales implement new strategies, discount slow-moving stock and reduce future purchasing to free up cash.

DuPont analysis

This method involves analysing return on equity (ROE), which is a profitability metric that measures how much profit a company generates for each dollar of shareholder equity. The DuPont analysis breaks down ROE into three components to reveal what drives the company’s profitability.

• Net profit margin: Measures a company’s overall profitability, i.e. the share of revenue that becomes profit after costs, interest and taxes. Reveals how well expenses are managed and turned into profit.
• Asset turnover: Measures the efficiency with which a company utilises its assets to generate sales
• Equity multiplier: This reflects a company’s financial leverage, i.e. the extent to which it uses debt to finance assets.

The formula to perform a DuPont analysis is:

ROE = net profit margin x asset turnover x equity multiplier

Discounted cash flow

Discounted cash flow provides an estimate of the intrinsic value of a cash-generating asset based on the sum of its expected future cash flows. It operates on the principle that an asset’s intrinsic worth is the present value of its future cash flows.

The expected cash flows used in this form of analysis are therefore discounted back to their present value. This is done by using a discount rate that reflects the time value of money and risk.

How to work out discounted cash flow

The formula to use is:

Discounted cash flow = CF₁/(1+r)¹ + CF₂/(1+r)² + ... + CFₙ/(1+r)ⁿ

To work out the formula, you’ll need to have figures for:

• Estimated future cash flows for the specific time period (CF): For internal corporate valuation, this is typically the unlevered free cash flow a company is estimated to generate
• The rate at which you discount the expected cash flows (r): This is the weighted average cost of capital (WACC), which represents the average rate of return a company must pay to its debt and equity investors to finance its assets.

Discounted cash flow example

Imagine a company that’s in negotiations to acquire Business XYZ, which is asking $250,00 for acquisition. The company’s finance team performs a discounted cash flow calculation to determine if this is a fair price. It calculated it for the next five years; a time period that’s long enough to cover the business’s immediate strategic plans, but short enough to produce reliable projections.

The team estimates that:

• Business XYZ will generate a steady cash flow of $50,000 annually over the next five years (N = 5)
• The risk-adjusted discount rate (WACC) for the acquisition is 10% (r = 0.10).

This leads to the following calculation:

Discounted cash flow = $50,000/(1+0.10)¹ + $50,000/(1+0.10)² + $50,000/(1+0.10)³ + $50,000/(1+0.10)⁴ + $50,000/(1+0.10)⁵ = $189,539.30

The total discounted cash flow (or intrinsic value) works out to be $189,539. The finance team notes that the company therefore has a negative net present value of -$60,461.

Given this, the team advises the company’s leadership against acquiring Business XYZ under the proposed terms. This is because it won’t generate sufficient discounted cash over the next five years to justify the acquisition cost.

Breakeven analysis

This data analysis method tells you when a company’s total revenue covers its total costs, resulting in no net profit or loss. It allows the company’s finance team and leaders to see the minimum volume of sales needed to cover costs or to evaluate the impact of changes in costs or pricing.

The formula to conduct a breakeven analysis is:

Breakeven point (units) = total fixed costs / (selling price per unit - variable cost per unit)

Scenario & sensitivity analysis

These two methods are often used together to assess future financial risk and outcomes.

A scenario analysis is used to model the impact of specific, pre-defined future events on a company’s financial performance. It’s designed to test the resilience of a company under future economic or market conditions, typically modelling best-case, worst-case and most-likely scenarios.

A scenario analysis involves changing financial data for multiple variables in a logical way to reflect a single future scenario. For example, a ‘recession scenario’ would involve decreasing the company’s sales volume while increasing cost of capital and collection days (i.e. resulting in slower cash flow).

Meanwhile, a sensitivity analysis provides a narrower, more precise picture of future financial performance. It measures how changes in a single variable (like the interest rate, sales generated or raw material costs) affect a dependent variable (like net income or net present value). It helps pinpoint factors that carry the greatest risk to financial performance.

Let’s look at practical examples of how a finance team would analyse a company’s financial performance, predict future outcomes and interpret an analysis to offer strategic advice.

Financial data analysis example 1: Evaluating profitability over time

This example involves a financial team performing a horizontal analysis of their company’s income statements. A horizontal analysis requires evaluating financial data over a period of time, comparing figures from two or more periods and calculating the absolute dollar change and percentage change between the periods.

It aims to uncover significant trends or changes in financial performance over time, helping highlight strengths, weaknesses or potential concerns.

For this example, the finance team performs a horizontal analysis for the last five years. The analysis tracks the year-on-year percentage change in gross profit, operating expenses and net income. 

The team notes the value of gross profit, operating expenses and net income for the earliest year. It then expresses the values for these line items for each subsequent year. The analysis reveals the following over the five-year period:

• Gross profit: Increased each year by 10% on average, indicating strong sales and effective pricing or effective cost management relative to sales
• Operating expenses: Increased each year by 15% on average, indicating operational inefficiencies
• Net income: Due to the annual changes in gross profit and operating expenses, the analysis shows that net income increased by less than 10% on average each year.

The overriding observation from this analysis is that rising operating costs are eroding profitability. This is due to the increase in operating costs outstripping the growth in profit generated from sales each year. The finance team may then recommend cost control strategies.

Financial data analysis example 2: Assessing solvency before extending credit

This example involves a finance team performing a ratio analysis to assess the company’s solvency; that is, its ability to meet long-term debt obligations.

The analysis measures the risk of the company defaulting on its debt and helps the team see if its current debt structure is safe, particularly before considering taking on more debt.

For this example, the team performs two finance ratios; the debt-to-equity ratio and the interest coverage ratio.

Debt-to-equity ratio

This measures how much of the company’s assets are financed by debt as opposed to shareholder funds. The higher the value, the higher the debt risk, with a value between 1 and 1.5 generally considered a good balance of debt. The formula to calculate the debt-to-equity ratio is:

Debt-to-equity ratio = total debt / total shareholders’ equity

Interest coverage ratio

This shows you the company’s ability to cover its annual interest payments (as opposed to the principal it owes). A lower ratio indicates higher risk of default, with a ratio of 3 or higher considered healthy. The formula to work out the interest coverage ratio is:

Interest coverage ratio = earnings before interest and taxes / interest expense

Ratio analysis example: How values are interpreted and inform strategic advice

The backdrop to this example is that the company is planning a $5 million acquisition of a new automated packaging line, which will require securing a new bank loan.

From performing the ratio analyses, the finance team works out that the company’s debt-to-equity ratio equals 3. This means that the company has $3 of debt for every $1 dollar of equity, indicating a high level of debt and risk.

Meanwhile, the team calculates that its interest coverage ratio is 1.5, which indicates that the company’s operation profit barely exceeds its interest payments.

Based on these values, the team strongly recommends to the company’s leadership to postpone the $5 million acquisition, as this would push the interest coverage ratio even lower. The team advises that the company launch a second equity offering to raise $2 million in capital to increase shareholders’ equity, which would reduce the debt to equity ratio.

1. Define your objective

First, establish what aspect of your company’s financial performance you want to assess. Do you want to see if a new product is profitable? Or gauge the value of your company? Articulate the question or problem you need answered to work out what kind of analysis you need to perform.

2. Gather data

Once you have a clear objective in mind, gather all the financial statements and non-financial data needed for your analysis. This can include the income statement, balance sheet and cash flow statement, operational data like inventory levels and sales pipelines, as well as external information like industry benchmarks, market trends and economic data.

3. Select a data analysis method

From your objective, you can determine the appropriate method. If your goal is to measure the value of a cash-generating asset, use the discounted cash flow method. Or if it’s to measure operational efficiency, use a ratio analysis (like the DuPont analysis).

4. Interpret the results and make decisions

The most critical step is to interpret what the results actually mean and to articulate the findings into clear strategic advice.

For example, if a ratio analysis uncovers that the company’s current ratio has dipped below the industry average, the finance team must work out the reason why. If it’s due to the company having excessive debt, the recommendation might be to tighten credit terms for new customers and accelerate collection efforts on overdue accounts. 

Let’s look at what can hold your finance team back from performing accurate and efficient analyses.

Data quality

Often, different subsidiaries within a company will use unintegrated ERP systems, cloud-based accounting software or sales platforms, which makes data consolidation difficult. Some financial data may be located in spreadsheets, while data across different departmental platforms may be inconsistent.

The company may also not have strict standards for data collection and quality, leading to inconsistent and incomplete data. Timeliness of data is another challenge, as the lag time between a transaction occurring and it being recorded and reconciled in the general ledger can hinder real-time analysis.

Manual processes

Analysis via spreadsheets are not only time-consuming but also come with risks. Just one misplaced cell reference or incorrect formula can invalidate an entire complex analysis model. The manual steps required with spreadsheets are also hard to track and replicate, which makes auditing difficult.

Siloed systems

A finance team may not have a holistic view of the company’s operations due to data being siloed in multiple unintegrated platforms. For example, marketing spend tracked in the customer relationship management platform may not reconcile with the expense captured in the ERP.

Limited forecasting capability

The uncertainty of complex business environments and markets means that relying on historical financial performance data can often lead to errors. The assumption that historical trends continue in a straight line can lead to inaccurate forecasts when unexpected events like economic downturns happen.

Lack of visibility

The finance team may produce overly complex analysis reports that are difficult for non-financial executives to understand. Insights from analysis may only explain what happened as opposed to why, leaving company leaders unaware of critical context.

Maintain clean, high-quality data

Consolidated and consistent data is critical for the success of analysis. Strict data governance practices are needed across different departments to ensure financial and non-financial data is recorded accurately within software platforms.

Standardise reporting

Standardised financial reporting across departments helps make it easier to compare data from different sources. It also increases the reliability and accuracy of data, and reduces the effort needed to collect and validate data.

The finance team should ensure every team or department uses the same definitions and templates for financial reporting.

Use automation

Financial management software helps make analysis quicker and easier by automating time-consuming processes. It allows you to slice and dice data and automate data collection and reporting, with the ability to create standard dashboards that provide a real-time view of your company’s financial performance and KPIs.

Build repeatable models

Develop templates and models that you can easily update and re-use with new data. For example, you could create quarterly forecasting models. Use of standardised models ensure everything is using the same validated logic and assumptions, helping reduce operational risk and enhance efficiency.

Visualise insights

Financial management systems give you the ability to visually present data in tables, charts and graphs, making it easier for your finance team and non-financial executives to interpret data, spot trends, anomalies and exceptions.  

There’s a range of technology solutions that can help you implement these best practices and enhance the efficiency and impact of analysis:

Cloud-based financial systems

These systems provide a single source of truth for your financial data, offering remote access from any internet-connected device. The best systems automate reporting and allow you to create custom dashboards that visually represent data. They also allow you to track liquidity, solvency, profitability and efficiency ratios as well as forecast cash flow.

Data visualisation tools

A financial management system, spreadsheets or tools like Power BI, Tableau and Google Data Studio allow you to transform data into charts and dashboards. Your finance team can dynamically explore data and create interactive reports that are easily understandable for your company’s leadership.

Financial modelling tools

These tools, which include platforms like Excel or Anaplan, help you create complex models (like discounted cash flow, scenario and sensitivity analysis and budgeting models). Some offer automation, pre-built logic, validation checks and collaboration features that help make modelling easier, allowing you to avoid creating columns and manually inputting data.

Python and analytics frameworks

Python is a programming language that finance teams can use for advanced data analysis that goes beyond the capabilities of spreadsheets or other platforms. As an open-source tool, it offers extensive libraries that you can use to build custom predictive models like discounted cash flow, for instance, as well as tables to clean, merge, aggregate and transform data to perform fast and accurate calculations. 

Using Python, however, requires basic programming skills. Given it comes with a gentle learning curve, many finance teams can get up to speed with Python via online courses.