business analytics (BA)

Business analytics (BA) is the iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. Business analytics is used by companies that use data-driven decision-making.

Data-driven companies treat their data as a corporate asset and actively look for ways to turn it into a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and an organizational commitment to using data to gain insights that inform business decisions.

How business analytics works

Once the business goal of the analysis is determined, an analysis methodology is selected, and business data is acquired to support the analysis. Data acquisition often involves extraction from multiple business systems and data sources, then cleansing and integrating data into a single repository such as a data warehouse or data mart

Initial analysis is typically performed on a smaller sample set of data. Analytic tools range from spreadsheets with statistical functions to complex data mining and predictive modeling applications. As patterns and relationships in the raw data are uncovered, new questions are asked and the analytic process iterates until the business goal is met.

Deployment of predictive models involves scoring data records that are typically located in a database. Then the scores are used to optimize real-time decisions within applications and business processes. BA also supports tactical decision-making in response to unforeseen events. And, in many cases, the decision-making is automated using artificial intelligence to support real-time responses.

Types of business analytics

Specific types of business analytics include:

  • Descriptive analytics, which tracks key performance indicators (KPIs) to understand the present state of a business;
  • Predictive analytics, which analyzes trend data to assess the likelihood of future outcomes; and
  • Prescriptive analytics, which uses past performance to generate recommendations about how to handle similar situations in the future.
analytics comparison diagram
A diagram outlining the difference between predictive, descriptive and prescriptive analytics

Business analytics vs. business intelligence

While the terms business intelligence and business analytics are often used interchangeably, there are some key differences.

Companies usually start with business intelligence (BI) before implementing business analytics. BI helps to analyze business operations to figure out what has worked so far and what needs improving. BI uses descriptive analytics.

By contrast, business analytics focuses more on predictive analytics and generating actionable insights for decision-makers. Instead of just summarizing past data points, BA also aims to predict trends. The data collected using business intelligence lays the groundwork for business analytics; from that data, companies can choose specific areas to analyze further using business analytics.

Business analytics vs. data analytics

Data analytics is simply the analysis of data sets to draw conclusions about the information they contain. Data analytics does not have to be used in pursuit of business goals or insights. It is a more general term than business analytics. The definition of data analytics includes business analytics -- business analytics is a type of data analytics. Business analytics is the use of data analytics tools in pursuit of business insights.

However, because it's a general term, data analytics may be used interchangeably with business analytics.

Business analytics vs. data science

The more advanced areas of business analytics can start to resemble data science, but there is also a distinction between these two terms. Even when advanced statistical algorithms are applied to data sets, it doesn't necessarily mean data science is involved. That's because true data science involves more custom coding and exploring answers to open-ended questions.

Data scientists generally don't set out to solve a specific question, as most business analysts do. Rather, they will explore data using advanced statistical methods and allow the features in the data to guide their analysis.

Business analytics examples and tools

There are a host of business analytics tools that can perform these advanced data analytics functions automatically, requiring few of the special analytical skills or deep knowledge of programming languages necessary in data science.

These tools help businesses organize and make use of the massive amount of data that modern enterprise cloud applications produce. These applications may include supply chain management (SCM), enterprise resource planning (ERP) and customer relationship management (CRM) tools.

Below are some popular business analytics tools:

  • Qlik, which has data visualization and automated data association features.
  • Splunk, which is especially popular for small and medium-sized businesses because of its intuitive user interface and data visualization features.
  • Sisense, which is known for its dynamic text analysis features and data warehousing
  • KNIME, which is known for its high-performance data pipelining and machine learning
  • Dundas BI, which is popular because of its automated trend forecasting and its user-friendly, drag-and-drop interface features.
  • TIBCO Spotfire, which is considered one of the more advanced BA tools and offers powerful automated statistical and unstructured text analysis.
  • Tableau Big Data Analytics, which is also highly regarded for its advanced unstructured text analysis and natural language processing (NLP) capabilities.

One example use case would be to aggregate data from various enterprise applications using a DataOps analytics platform like DataKitchen, then to use Tableau to present that data internally to employees. The data, for example, may be used to indicate which customers were likely to cancel their subscription to the service the company offers. The insights offered by BA tools allow employees to identify customers at risk for cancellation and take measures to keep them subscribed.

When choosing a business analytics tool, organizations should consider the sources they will be drawing data from, the nature of the data they will be analyzing, and usability. A good business analytics tool will be easy enough for the common business user, but also enables more advanced users to take advantage of its features.

This was last updated in January 2021

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