What is financial analytics?
Financial analytics is the creation of ad hoc analysis to answer specific business questions and forecast possible future financial scenarios. The goal of financial analytics is to shape business strategy through reliable, factual insight rather than intuition.
By offering detailed views of companies' financial data, financial analytics provides the tools for firms to gain deep knowledge of key trends and take action to improve their performance.
The benefits of financial analytics
As a subset of business intelligence and enterprise performance management, financial analytics affects all parts of a business and is crucial in helping companies predict and plan for the future.
Financial analytics involves using massive amounts of financial and other relevant data to identify patterns to make predictions, such as what a customer might buy or how long an employee's tenure might be. With a wealth of financial and other relevant data from various departments throughout their organizations, corporate financial teams are increasingly using this data to help company leaders make informed decisions and boost the company's value.
By helping businesses understand their top- and bottom-line performance (along with other indicators, including financial and macroeconomic data), measure and manage their assets, and forecast variations within the organizations and industries in which they compete, financial analytics offers insight into organizations' financial status and improves the profitability, cash flow and value of the business. Financial analytics also helps companies improve income statements and business processes.
Financial analytics and the CFO's role
Business transformation and advances in technology -- from big data to customer analytics software to data warehouses -- have contributed to companies' move to use financial analytics. The changing role of the corporate finance department has also influenced this move.
Chief financial officers traditionally relied on historical data and trends to forecast future performance. However, they are changing their focus as they increasingly tap into technologies, such as advanced data analytics, machine learning and automation. As finance departments have begun adopting financial analytics to home in on what's happening in the business and what that's likely to mean going forward, their roles have changed from information provider to problem solver. Having more timely access to information is helping companies make quicker, better informed business decisions.
Many experts consider predictive analytics an essential element in the digital transformation of finance. A key part of this is the ability to examine historical and new data to assess what's relevant to a specific company -- be it macroeconomic data, industry trends or petroleum prices -- to improve forecasting and decision-making.
The application of analytics is crucial in financial services and other data-intensive fields. Financial services businesses, including investment banks, generate and store more data than just about any other business in any other sector, mainly because finance is a transaction-heavy industry. While banks have, for many years, used data to measure and quantify risk, data analysts are now taking on the role of influential internal consultants, responsible for communicating to senior executives key insights on how to improve the organization's overall profitability.
Today's financial institutions not only analyze structured data, such as market or trading data, but also unstructured data, which can include data sources from news outlets, social media and marketing materials.
The importance of financial analytics
Financial analytics can help companies determine the risks they face, how to enhance and extend the business processes that make them run more effectively, and whether organizations' investments are focused on the right areas.
Advanced analytics and its ability to leverage big data will enable organizations to rethink their strategies for solving problems and supporting business decisions. Analytics can also help companies examine the profitability of products across various sales channels and customers, which market segments will add more profit to the business and what could have an impact on the business in the future.
Continuous visibility into financial and operational performance will help with more than just decision-making; it will also increase visibility regarding the processes that support those decisions. So, rather than getting data on employee turnover rates and the related costs after the fact, financial analysts and HR leaders will be able to see what problems employees are having and intervene to improve performance and prevent costly turnover. Another plus is the potential for improved electronic linkage of records across the supply chain so that data will only need to be entered once.
Despite the promise of financial analytics, business experts from the academic and corporate worlds warn against automating bad processes. They note that the processes that provide financial insights based on historical data are often disconnected and leave serious data gaps. Poor-quality data can hurt business performance and lead to incomplete or inaccurate customer or prospect data, ineffective marketing and communications efforts, increased spending and bad decisions. To improve results, companies should use predictive analytics properly, improve the quality of their data and manage it effectively.
Types of financial analysis
Financial analysis refers to the process of evaluating businesses, projects, budgets and other finance-related entities to determine the stability, solvency, liquidity or profitability of an organization. In addition to focusing on income statements, balance sheets and cash flow statements, financial analysis is employed for evaluating economic trends, setting financial policy, formulating long-term business plans and pinpointing projects or companies for investment.
Types of financial analysis include the following:
- Horizontal analysis refers to the side-by-side comparison of an organization's financial performance for consecutive reporting periods. The aim is to determine major shifts in the data. Later, this information could be applied to a more detailed analysis of financial results.
- Vertical analysis pertains to the proportional analysis of a financial statement. Each line item on a financial statement is listed as a percentage of another item -- for example, every line item on an income statement is provided as a percentage of gross sales, while every line item on a balance sheet is given as a percentage of total assets.
- Short-term analysis provides a detailed review of working capital, involving the calculation of turnover rates for accounts receivable, inventory and accounts payable. Any differences from the long-term average turnover rate should be studied further because working capital is a significant user of cash.
- Multi-company comparison entails tallying and comparing major financial ratios of two organizations, usually in the same industry sector. The aim is to determine the companies' relative financial strengths and weaknesses.
- Industry comparison contrasts the results of a specific business and the average results of an entire industry. The purpose is to determine any unusual results in comparison to the industry average.
Key types of financial analytics
Examining financial and other relevant information, financial analytics offers various views of companies' past, present and future performance. The following are key types of analytics that can help companies of different sizes:
- Predictive sales analytics may include the use of correlation analysis or past trends to forecast corporate sales.
- Client profitability analytics helps differentiate between clients who make money for a company and those who don't.
- Product profitability analytics entails assessing each product individually, rather than establishing profitability overall at a company.
- Cash-flow analytics employs real-time indicators, including the working capital ratio and cash conversion cycle, and may include tools such as regression analysis to predict cash flow.
- Value-driven analytics assesses a business' value drivers, or the key "levers" the organization needs to pull to achieve its goals.
- Shareholder value analytics, which is used to tally the value of a company by examining the returns it provides to shareholders, is used concurrently with profit and revenue analytics.
Financial analytics software programs
As the way information is now collected and analyzed presents a significant shift -- along with new challenges -- software can help reduce the complexity. Financial analysis software can speed up the creation of reports and present the data in an executive dashboard, a graphical presentation that is easier to read and interpret than a series of spreadsheets with pivot tables.
Popular financial analysis software programs include the following:
- Oracle Financial Analytics is the modular component of Oracle's integrated family of business intelligence software applications. It enables insight into the general ledger and provides visibility into performance against budget and the way staffing costs and employee or supplier performance affects revenue and customer satisfaction.
- SAP ERP Financial Analytics helps organizations define financial goals, develop business plans and monitor costs and revenue during execution.
- SAS Business Analytics provides an integrated environment for data mining, text mining, simulation and predictive modeling -- a mathematical model that predicts future outcomes -- as well as descriptive modeling, a mathematical model that describes historical events and the relationships that created them.
- IBM Cognos Finance provides out-of-the box data analysis capabilities for sales, supply chain procurement and workforce management functions.
- NetSuite provides financial dashboards, reporting and analytic functions that allow personal key performance indicators to be monitored in real time.
- MATLAB allows developers to interface with programs developed in different languages, which makes it possible to harness the unique strengths of each language for various purposes.