Financial analytics is an important tool used to define business strategy. This important business intelligence practice is often confused with financial analysis, which is a different practice entirely.

While both do require vast amounts of data for the purpose of assisting in strategy, the two practices are distinctly separate. 

As technology has grown and advanced, it has created opportunities for business leaders to utilize data that was previously difficult to mine.

Part of the challenge that data sets presented in the past was in the fact that there were large volumes disparately warehoused across an organization. The lack of centralized data made it difficult to garner useful information. 

Advancements in data management and business intelligence have led to the widespread adoption of financial analytics.

In this FAQ, we will provide a description of what financial analytics is, why it is important, and the various ways organizations have implemented financial analytics.

What Is Financial Analytics?

Financial analytics is the process of performing ad-hoc analysis on specific business inquiries and the forecasting of potential future financial outcomes.

Its primary objective is to assist business leaders in developing strategies by providing them access to actual and reliable sources of information. This is typically done by providing insight into financial data, which is where the name financial analytics is derived. 

The practice of financial analytics provides a useful tool for leaders to garner a robust understanding of business trends and give insight that allows them to make critical decisions in a timely manner.

Financial analytics is different from financial analysis in that financial analysis is the practice of performing analysis on projects, budgets, business performance, and other financial transactions.

Financial analysis provides performance information on how a business is performing and whether or not it is profitable, stable, remains solvent, and has sufficient liquidity.

This is distinctly different from financial analytics, which is analysis performed on specific inquiries, with the result being a scenario-based forecast when possible. 

Why Is Financial Analytics Important?

Because the finance field is so data-heavy, financial analytics is able to aggregate and parse data into usable information for business leaders to identify trends in transactions or other business activities that allow them to develop more robust and relevant strategies.

The practice of financial analytics is also providing organizations with the ability to manage risk more effectively.

It does this by providing a continual information stream that is based on real-time data on how the business is performing. This performance information can assist leaders with identifying trends that expose the firm to adverse risk.

For example, HR leaders use financial analytics to identify employee turnover trends, allowing them to make adjustments and retain key individuals. Situations like this have the added benefit of reducing turnover costs in conjunction with reducing turnover risks.

Financial analytics also provides key insights into sales channels and market segments. This type of data helps to identify ways to increase margins or develop strategies to expand in markets where margins are best.

This type of data is often used in predictive analytics as well, helping to create forecasts that leaders can use to identify the impact of allocating resources to certain products and sales channels. 

Finally, financial analytics provide critical insight into supply chains, helping to manage inventory or raw materials better.

The access to a more robust pipeline of data is allowing businesses to manage cost, reduce shortages, minimize overages, and identify vendor partnerships that are the most accretive. 

It is important to note that perpetuating bad processes can occur through automation. The historical data that financial analytics relies upon is only as good as the processes that create it.

The process of financial analytics is unable to discern when the quality of data is poor. Proper use of predictive analytics helps to improve the quality of data.

Types of Financial Analytics

By utilizing financial analysis and other historical data, financial analytics can provide leaders with a robust view of a business’ historical, present, and potential future performance. There are some key ways that financial analytics provides this insight. 

Client Profitability Analysis

This helps to identify clients that are more profitable than others. The information provided can help leaders build better relationships with lower-margin clients or focus their attention on higher-margin clients to expand sales. 

Product Profitability Analysis

This analysis helps to identify which products are the most profitable, often leading to product mixes that, when combined, can boost margins. 

Cash-Flow Analysis

Real-time monitoring of cash flow KPIs often include working capital ratios and days in receivables. The analytics are often used to project future cash flows

Predictive Sales Analysis

In this analysis, trends are identified from historical data or other relevant indicators that help to forecast potential sales volume.


These analytics are used to identify the best value drivers for the business and other relevant metrics to achieve management’s goals.


Shareholder-value analysis is typically used in conjunction with profitability analytics and other revenue analysis. It is employed to attempt to calculate the value of the organization by measuring returns to the business’ shareholders. 

Using Datarails to PerformYour Financial Analytics

Every finance department knows how challenging performing financial analytics can be. Regardless of the type of financial analytics you are performing, it requires big data to ensure accuracy, timely execution, and of course, monitoring.

Datarails is a FP&A solution that can help your team create and monitor financial forecasts faster and more accurately than ever before.

By replacing spreadsheets with real-time data and integrating fragmented workbooks and data sources into one centralized location, you can work in the comfort of excel with the support of a much more sophisticated data management system behind you.

This takes financial forecasting from time-consuming to rewarding. 

Learn more about the benefits of Datarails here.