What it is, why it matters, and techniques. This guide provides definitions and practical advice to help you understand and execute financial analysis.
Financial analysis is the process of examining financial statements and other relevant data to assess the financial health and performance of an organization. This analysis typically involves reviewing a company's income statement, balance sheet, and cash flow statement to assess its profitability, liquidity, solvency, and overall financial position. Using the right tools and techniques to analyze your data can help you make informed investment or business decisions and gain insights that allow you to predict and improve performance.
Financial analysis allows you to identify the financial strengths and weaknesses of the organization and to make informed decisions about its future strategies. It is essential for investors, lenders, and creditors to assess the company's ability to pay back loans or to invest in the company.
Modern financial analytics tools can bring together data from all your systems to give you a holistic view of your business. This not only saves time, it helps you gain broader insights to predict and improve performance.
Financial statement analysis is critical for your organization because it:
Financial analysis also helps investors decide whether to invest in a particular company or fund. Investors can more easily compare different companies and predict future performance.
Download the ebook with 6 use cases of an active approach to financial analytics
There is a wide range of techniques used in financial planning and analysis. Almost all techniques involve interpretation and analysis of three key financial statements:
Many techniques involve performing financial ratio analysis. Ratios are calculated by dividing one financial statement item by another. Here are the top 20 most frequently used techniques:
Many financial analysts use spreadsheets like Excel to analyze data. While this may seem convenient, spreadsheets introduce many challenges. Modern financial analysis software overcomes these issues.
Financial analysis software capabilities:
See how to explore information and quickly gain insights.
Cash Flow Valuation Analysis
This financial analysis example displays various cash flow-related key performance indicators (KPIs) including the internal rate of return (IRR) by region compared to a target IRR, investment count by type, and a detailed cash flow table. Additionally, real-time indicators like the cash conversion cycle and working capital ratio could be incorporated. To aid in cash flow management, regression analysis can be utilized to forecast cash flow.
Click the dashboard below to explore.
Actual v. Forecast Expense Analysis
The financial analysis example below demonstrates a comparison between actual expenses and forecasts for a specific time frame, as well as the trend of expenses over time for each expense category. With a modern and integrated financial dashboard, you can easily explore this data in greater detail and extract actionable insights.
Click the dashboard below to explore.
In addition to the limitations of spreadsheets listed above, here are key challenges to be aware of as you perform financial planning and analysis in your organization.
Reliance on Historical Data. Financial statement analysis often relies on historical data, which may not be an accurate predictor of future performance. And, given the pace of business today, you may need real-time data about your market, customers, and operations to respond quickly.
Complex Models & Big Investment. Historically, if you wanted to conduct predictive or prescriptive analytics, you had to find and hire data scientists to develop custom machine learning models and make big investments in hardware and data engineers to integrate, store and manage your data. Automated machine learning (AutoML) now makes it easier for you to build, train, and deploy custom ML models yourself.
Limited View. Financial analysis typically only involves financial statements and doesn’t include other key factors such as industry trends, regulatory issues, and management capabilities.
Inaccurate Data. Many companies do not have adequate data governance policies in place and/or analysts manually gather and prepare the data themselves. This can result in intentional or accidental errors in the financial data.