Business Intelligence vs. Business Analytics: Differences Explained

Business intelligence (BI) and business analytics (BA) each allow data analysts to process information to help enterprises make smarter, data-driven decisions. Let's examine each in detail to learn the difference between them and how to use each in your business.

What are business intelligence and business analytics?

BI is a set of systems and technologies used by enterprises to visualize and share operational business data. BI can reveal trends and patterns in data that would otherwise remain obscure. Enterprises can use the reporting and visualization capabilities of BI to operate more efficiently and obtain valuable insights.

BA is the use of historical business data and statistical models to better predict why things happen within a business. These models can help determine causality and make predictions about future events. BA is more about statistical extrapolation than static analysis.

A few key factors differentiate BI and BA:

• Purpose: BI uses historical data to determine what happened within an organization, while BA uses this data to determine why those things happened in an attempt to make predictions. In other words, BI provides descriptive analysis of data and BA provides predictive analysis.
• Usage: BI is typically used to understand business operations to determine how past processes affect key performance indicators (KPI). BA, on the other hand, uses data to determine why processes affected the KPIs in such a way and to help build models to predict how changes will affect them going forward.
• Tools: BA tools are often more mathematically advanced than BI tools, as BA involves statistical analysis and complex processes like machine learning.

When to use business intelligence and business analytics

Choosing whether to use a BI or BA tool depends on use cases and business models.

Use cases

BI is great for streamlining business operations by locating inefficiencies and reducing costs. A comprehensive BI system can provide reports and visualizations for every aspect of a business, and self-service BI tools allow nontechnical users within an organization to contribute and analyze data.

BA is more useful for informing decisions about how to alter operations or products. For instance, a predictive model could show the optimal times and locations to sell certain products. It could even determine what new products your customers want and the best ways to sell them. BA practices like this can guide an enterprise through complex and changing business environments, and self-service BA tools allow all stakeholders in an enterprise to make data-driven decisions.

Business models vs. business processes

BI is great at optimizing processes. Almost any organization can benefit from BI, whether it's an established enterprise looking to enhance its operations or a fast-growing business that needs to better understand its operations to effectively scale and manage growing pains.

BA is better suited to organizations that are changing their business model or attempting to adapt to new environments. For instance, planners can use statistical models to suggest and verify changes to a product or service to better match customer needs. Note, however, that accurately predicting the effects of changing business models requires a substantial amount of high-quality data.

BI and BA case studies

Let's look at a couple of business applications of BI and BA:

High employee turnover

At any business, some turnover is inevitable, and it's important that business leaders understand data relating to turnover. What departments have the highest turnover? What times of year do employees typically quit? Are they going to other companies or going back to school?

BI tools allow managers to produce reports and visualizations that answer these questions, allowing them to respond. For example, an enterprise might determine that turnover is highest in the summer quarter. Management could then instruct the HR department to plan for increased hiring efforts before summer starts to ensure that enough new talent is coming in to offset the losses.

Alternatively, an enterprise could use BA to determine why turnover is high and discover how to reduce it. For instance, BA could reveal that employees are leaving to attend graduate school. The enterprise could then build a model to flag employees at higher risk of leaving and offer them the opportunity to stay with the company and take night classes, or, if part-time education was not an option, to enter into an agreement to subsidize their schooling if they returned to the company after graduating.

Once enough data accumulates regarding the effectiveness of this model, the enterprise could improve it to help decide which offer to make employees based on their individual characteristics.

Maintenance in manufacturing

A broken component in the production line can cost a manufacturer time and money. To avoid production delays, an enterprise can deploy BI systems that help managers understand what's causing errors or backups in the production line.

For instance, an enterprise could use aggregation and visualization tools to determine the most common problems in production. Let's say that a manager finds that errors are introduced into the product more often when a specific type of machine is employed. The manager could use this information to better train employees on the production line regarding the machine's use. Alternatively, if the machine itself was defective, the manager could begin to look for a replacement.

Using BA, on the other hand, the manager could build a model that predicts when a machine failure is likely to happen. For example, a high temperature may be a strong indicator that a machine is about to fail. Having such a model could allow employees on the production line to proactively replace a faulty machine and reduce downtime — or avoid it.

Take action with business intelligence and business analytics

BI and BA allow businesses to make decisions based on hard data, and each provides valuable insights. BI and BA work best when they're based on high-quality data centralized in a data warehouse.

Stitch provides a simple data pipeline for replicating data into a data warehouse to use with BI and BA. Try Stitch today.