Difference between Business Analytics and Business Intelligence

Experts maintain that business analytics is basically one term for a bigger concept and is associated with the following complex functions:

• Enterprise information
• Enterprise performance management
• Data warehousing
• Analytic applications
• Business intelligence
• Business risks
• Compliance
• Governance

BA vs BI

When you’re talking about data and what it can do for your company, a lot of terms get thrown around. Business analytics (BA) and business intelligence (BI) are two terms heavily used, but rarely given the same definition by any two sources. Some take the stance that they’re interchangeable, and others staunchly defend their position as to the meaning of each, and what would fall under those respective umbrellas. 

On a basic level, BI is the ability to take information resources and convert them into knowledge that is helpful in decision making. The traditional method of doing this involves cataloging and examining data from past decisions and actions, and using this as a way of setting metrics benchmarks for the future. In method, BA is an offshoot of BI. BA focuses on using data to net new insights, whereas traditional BI used a consistent, repeating set of metrics to steer future business strategies based on this historical data. If BI is the way to catalog the past, then BA could be called the way to deal with the present and predict the future.

Hosted business intelligence solutions like RJMetrics offer a combination of BI and BA by providing a data warehousing and reporting solution alongside a flexible interface for ad-hoc analysis and data discovery that can point you toward smarter decisions.

The Evolution of Business Intelligence vs Business Analytics

In the past, BI has been used to talk about the people, processes and applications used to access and extrapolate meaning from data, for the sake of improving decisions and understanding the effectiveness of targeted decisions. But this is where BI as a baseline failed; something that runs entirely off of static, historic data severely limits a user’s ability to make predictive decisions and forecast for the future market. When an emergent situation arises on a Friday afternoon, the user doesn’t greatly benefit from looking at metrics collected prior to the introduction of that situation.

The rapid growth and demand for BA comes from this failing, and is in a way the evolved form of BI solutions. In a business world whose speed is ever-increasing, the user needs to be able to interact with information at the speed of business, not looking back over his or her shoulder at what happened in the past. BI setups alone do not support the occurrence of users asking and answering questions in the face of marketplace events as they happen. A company that is data-driven sees their data as a resource, and uses it to hedge out competition. The more current the data the user has, the better jump he or she has on the competitor, who may or may not have become a threat in a time so recent that traditional BI data reporting wouldn’t even take them into consideration.

Many companies are commonly implementing advanced analytics on top of their data warehouses, to bridge the gap between BI and current day needs. Perhaps this is the origin of the confusion between terms, as organizations pick and choose from different combinations of services and have no real understanding of what to call these mash ups.

Equally relevant is the fact that more and more people are being asked to interpret data in roles that are not strictly analytical. Product managers, marketers and researchers are moving towards data as a way to formulate strategies, and traditional BI platforms make it difficult to push data into real-time situations and what-if scenarios.  With the importance of data-driven decisions increasingly becoming a realization for less tech-savvy branches of company teams, the need for more user-friendly and faster producing platforms also grows. Moreover, delivering the data that supports these decisions to a broader company team demands a more visual form of modeling tool, to improve understanding across all departments. Charts and graphs showing BA findings are quicker and more impacting than written out statistics and excel sheets full of data.

Data interpretation and the manipulation method of choice change as the market demands. While having a set of established methods is important to the effectiveness of a company’s strategy, it’s understanding the need for flexibility in the face of these changes that can be a company’s most valuable asset.