Business Analytics vs. Business Intelligence: How to choose the right one

Learn more about how business analytics and business intelligence enable you to spot trends, measure performance, and make data-driven decisions.

Best Practices
July 10, 2023
Katie Geer photo
Katie Geer
Product Manager, Amplitude
Business Analytics vs. Business Intelligence featured blog illustration

Here’s a scary scenario: Imagine making a decision that impacts your business, but with absolutely no data to back it up.

That kind of decision could have negative consequences—and you won’t know what those could be or how they might affect your employees, your company, or your profits. On the other hand, it could have positive consequences, but you would have no insight into what was impacted and the size of the impact. What would help is making choices based on data analytics.

Business analytics and business intelligence enable you to spot trends, measure performance, and make data-driven decisions. Leveraging these insights can help you pinpoint opportunities for growth, develop targeted marketing campaigns, and build more efficient processes to give your organization a competitive advantage.

Key takeaways
  • Business analytics is used to collect and analyze a lot of data.
  • Business intelligence is used to help interpret and present data.
  • Together, BA and BI help improve business performance using the insights gained from data analysis.

What is business analytics?

Business analytics (BA) involves collecting, analyzing, and interpreting large amounts of data to develop insights that inform business decisions and strategies. BA helps organizations better understand their customers, products, and business operations.

Types of business analytics

There are four types of business analytics, and each has a different role to play in guiding your decision-making process.

  • Descriptive analytics is an analysis of historical data to summarize trends and patterns. For example, you might use it to look at past sales performance and see the trends over time.
  • Diagnostic analytics is also an analysis of historical data, but its purpose is to identify the root causes of events or performance issues. For example, if you find that sales performance dipped in the past, then you’ll want to look into why that happened.
  • Predictive analytics analyzes historical data to forecast future outcomes and spot any potential opportunities or risks. So, if you’re looking at that sales performance data, you can predict seasonal dips in sales.
  • Prescriptive analytics takes the data from predictive models and helps you find recommended courses of action. If you anticipate a dip in sales around the holidays, alert the sales team so they can adjust revenue goals for the quarter.

Examples of business analytics

Within those types of analytics, there are also specific kinds of analysis you’ll do, such as:

  • Market basket analysis analyzes customer purchasing patterns. It looks at the items customers have purchased together and the timing of their purchases. With this data in hand, your marketing team has the information needed to identify potential opportunities for cross-selling and upselling.
  • Customer segmentation groups customers into segments based on shared characteristics or behaviors. Grouping customers into segments allows you to target different audiences more effectively by tailoring the messaging of your products or services to the specific needs of each customer segment.
  • A/B testing compares two web page versions, ads, social media copy, images, or product experiences to determine which one performs better. By comparing the performance data of the two versions, your business can make an informed decision about which one to use.
  • Churn analysis measures the rate at which customers stop buying or using your product or service. A churn analysis helps indicate the overall rate of customer satisfaction and loyalty, and can be used to identify areas for improvement.

What is business intelligence?

Business intelligence (BI) describes the broader technologies, applications, and practices that are used to collect, analyze, and present business information.

Using BI analytics tools, companies can analyze data from multiple sources to identify trends, spot problems, and make better business decisions. You can also leverage BI to find ways to automate processes and improve customer service.

Also, BI tools offer a layer of security and privacy. You can store your data in the tool and have peace of mind knowing that your organization’s data is protected and any sensitive information is stored in compliance with data privacy laws and regulations.

Types of business intelligence

Just like business analytics, there are also different types of business intelligence that you’ll use depending on the insights and information you need, such as:

  • Descriptive BI focuses on analyzing and summarizing past data to detect patterns and trends. It’s used to better understand the performance of an organization and its operations.
  • Predictive BI uses historical data and statistical models to predict future outcomes and trends. It can accurately forecast customer demand and improve supply chain performance.
  • Prescriptive BI uses advanced analytics and artificial intelligence (AI) to provide actionable insights and helps organizations make decisions faster and more accurately.
  • Cognitive BI involves using artificial intelligence and natural language processing to analyze unstructured data such as text and images.

Examples of business intelligence

Imagine trying to present big, long columns of business data to a C-suite—it likely wouldn’t go well if you don’t have visuals to make more of an impact. The following business intelligence processes can show decision-makers what they need to see:

  • Data visualization uses charts, graphs, and other visuals to illustrate complex data sets.
  • Data mining, also called knowledge discovery in data (KDD), is the process of searching and sorting through large amounts of data to find specific information.
  • Dashboards are populated with real-time data, enabling users to access the most pertinent data and metrics quickly.
  • Automated reporting generates and distributes reports automatically based on business data without further analysis.
  • Natural language processing interprets and analyzes human language, for example, in customer reviews or feedback surveys.
  • Machine learning (ML) uses algorithms to analyze lots of data and helps highlight any correlations or insights into business problems.

Business analytics vs. business intelligence

Business analytics is focused on optimizing processes, while business intelligence is focused on interpreting data. But BA and BI both aim to improve business performance by using the insights gained from data analysis.

Business analytics uses statistical analysis to uncover patterns within data sets, like trends or correlations. You might find that your marketing team is spending a lot of money and time on paid search ads when content is bringing in the most well-qualified leads.

A business analyst looks at the data to find these insights, which help them identify opportunities for improvement and make predictions about future results. For example, if marketing continued to spend too much on paid search, the department would lose ROI, and it could affect its budget in the future.

But how do you let people know that trend is happening? That’s where business intelligence comes in. BI uses the findings from business analytics to provide insights into your data by using reporting tools and dashboards.

Choosing between business analytics and business intelligence

If you’re unsure which one to use, consider your current resources and needs. Do you have time to sift through and analyze complex data independently, or do you need tools to help you quickly identify patterns and trends and predict future outcomes? Or do you need tools to help you collect, interpret, and present current business data?

Odds are, you need both. BA is a subset of BI, so they work hand-in-hand. For example, you could use them to identify trends in customer behavior, then use that information to predict your customers’ future needs and develop strategies to meet them.

Start your data analysis with Amplitude

Analyzing your data doesn’t have to be complex—instead of spending countless hours and resources on this, get a ready-to-go solution like Amplitude Analytics.

Amplitude funnel analysis screenshot

Amplitude enables you to look at your data and easily spot trends that help your team make strategic, data-driven decisions. Sign up for free today.

About the Author
Katie Geer photo
Katie Geer
Product Manager, Amplitude
Katie is a product manager at Amplitude focused on acquisition. Previously she was in product at Redfin where she focused on experimentation and data instrumentation.

More Best Practices