Business Analytics vs. Financial Analytics: What’s the Difference?

There’s a saying that data is just data until it’s analyzed. It’s the analytics that turns data sets into insights to guide businesses. Data users and decision-makers need to know which type of analysis will deliver the answers needed. Two common types of data analytics are business analytics and financial analytics. While business and financial analytics can overlap in the data they use and even have common goals—business and finance are often intertwined—they also have distinct differences and drive different use cases. These analytics inform business decisions, drive organization-wide improvements, and identify solutions to ongoing and emerging challenges. By contrast, financial analytics offer insights into current and future financial operations, allowing organizations to take actions that improve financial performance and boost profitability.

It’s best to think of business analytics and financial analytics as complementary rather than working against each other. For example, analyzing sales data benefits both the business and finance. Let’s look at how business and financial analytics are different—and why those differences are important: 

Business vs. Financial Analytics

The most obvious difference between business and financial analytics is the areas of focus. Business analytics looks at overall business performance and daily operations to inform decisions on strategies, processes, problem-solving, and other business-centric areas. These analytics enable a range of improvements and benefits, such as charting an accelerated path to reaching business goals and measuring progress along the way. Financial analytics focuses on all financial aspects of the business, which can range from determining profitability to measuring top and bottom-line performance to informing budget decisions. Applying these analytics also helps organizations predict cash flow, measure business value, and determine how changes, such as launching a new product or improving sales by a certain percentage, will affect profitability. Knowing the type of insights that are needed will determine which analytics need to be performed.

Business analytics are generally more widely used throughout an organization than financial analytics. A business analyst is a general term for anyone who performs business analytics. Other positions using business analysis can include data scientists, citizen data scientists, machine learning and AI developers, operations teams, chief data officers, and others across the business. Financial analytics falls under the domain of CFOs and their departments. They perform analytics to build financial forecasts, identify potential risks, predict future financial performance, and provide other financial information.

Business analytics helps with workflows, process improvements, and organization-wide decision-making. For example, analytics can identify inefficient business processes, such as bottlenecks that slow down operations, and determine the best avenues for improvement. With financial analytics, organizations can make more accurate financial forecasts and investment decisions. In conjunction with predictive financial models, the analytics can answer a variety of fiscal-related questions, such as determining a customer’s lifetime value, understanding how churn and net new customers impact revenue, and measuring ways that initiatives like implementing environment, social, and governance (ESG) best practices influence profit margins.

Each type of analytics has specific questions it answers for what/if scenarios as well as providing insights into business or financial areas. Business analytics typically informs overall business strategies, such as determining if there’s a gap in the marketplace where the company can introduce a new product line, and help the business prioritize goals. Financial analytics also helps inform strategies, but those strategies are tied to goals for the chief financial officer (CFO) and the broader financial team. These analytics uncover insights related to business expenses, the organization’s overall financial health, and investments, including investments in research and development.

For the best analytic results, all relevant data should be integrated and made available to analysts. This means business and financial data can be brought together for insights. Specific business insights can be uncovered by analyzing data related to operations, customers, supply chains, products, sales, marketing, employees, sales, and other business areas. Financial analytics looks at financial and economic data, which is needed for any fiscal planning. Current, accurate, and appropriate data is required for each type of analytics to deliver relevant and trustworthy insights.

Simplifying Data Analytics

In addition to business and financial analytics, there are other types such as sales analytics, compliance analytics, and risk analytics. They all have several things in common—they use data to inform decision-making, predict outcomes, identify and mitigate problems, and drive improvements. Regardless of the analytics being performed, organizations need a modern platform that can scale to meet growing data volumes, make integrated data readily accessible to everyone who needs it, and is easy to use for all analysts. The Actian Data Platform delivers these capabilities and more. Whether analysts want a deeper understanding of the business or are taking a deep dive into finances, the Actian Data platform makes it easy to connect, manage, and analyze data. The easy-to-use platform brings together all data from all sources to deliver the analytic insights decision-makers and stakeholders need.

Additional Resources:

• How to Make Your Analytics Journey More Data Driven
• 7 Ways Market Basket Analysis Can Make You More Money
• Six Data Privacy Trends in Data Analytics to Watch in 2023

About Jennifer Jackson

Jennifer Jackson is CMO of Actian. Known as JJ, she leads Actian’s global marketing strategy and team. With 25 years of branding and digital marketing experience, she takes a data-driven approach that stems from her background in chemical engineering and as both a user and marketer of data analytics technology. She engages in initiatives to optimize customer experience, transition to a SaaS model and expand the partner ecosystem. JJ previously served as VP of Digital Marketing and Strategy at Teradata, where she led digital, strategy and operations for web modernization and strategy execution.