Business Analytics vs Business Intelligence: All you need to know

business analytics vs business intelligence

Although business intelligence and business analytics both work on disparate medium to large data sets, they are conceptually different.

Business analytics uses statistical techniques to turn your business data from various sources into valuable insights and presents them in human readable format. Business intelligence on the other hand are the tools and processes helping you ‘visualize’ and tell the story of what has unfolded so far. Industry experts might have varied opinions on how the two are different from each other. Some call business analytics as offshoot of business intelligence. Some say business intelligence is slice of business analytics. It is true that they are not mutually exclusive, which is why the difference between the two gets diluted. Still, there is a difference.

The core difference is that business intelligence can help you see your rearmost and current business performances; while business analytics lets you predict the future and strategize accordingly. Say, if you are digital marketing manager with a limited budget for Facebook advertising. Through historical data you can only know what transpired in previous Facebook campaigns. However, this time you want to narrow down the target audience to know who is more likely to buy your product. You can then focus on that customer segment by alluring them with better promotional offerings. Here is when you’ll need a mix of both business analytics and business intelligence tools.

What is Business Analytics?

Business analytics aids organizations, across industries, in taking proactive decisions and foreseeing the future through statistical techniques. With the multiplication of data and its sources, organizations have realized that they need to make optimum utilization of data, more so in real time. In order to see future and be prepared for possible adversities these organizations use business analytics to make data-driven decisions.

How can Business Analytics techniques add value to your organization?

The first step is for organizations to clearly identify the business problems they are trying to solve. Without clear goals, a Business Analytics solution can be a futile expense. Very often business leaders don’t know what problem they are trying to solve through data. They might have data lying in their organization to give them fair understanding of what has transpired so far but they don’t know what questions to ask to foresee the future. Even so, there needs to be a defined purpose before starting a Business Analytics project.

Next is identifying relevant data sources, which could be internal or external heterogeneous big data. Then organizations need to start preparing and cleaning the data to apply scientific business analytics techniques. After the relevant statistical techniques have been applied the outcome is a model which is tested and validated for its robustness. This is where data scientists and statisticians come into the picture.

Implementing the best model to elicit the right information is the most important step. Here, as business leader, you get answers to most of their questions. This might eventually lead you to change and rethink through your business processes.

Lastly, the results are measured to judge the value created by the implementation of a Business Analytics solution.

Following are important Business Analytics techniques:

1. Descriptive Analytics helps to know what has happened so far. It is like looking back retrospectively on how many sales, how many leads, who bought your latest product, who responded to your latest promotional campaign etc. This is the most straightforward technique where historical and current data are analyzed to garner information on past and present performance. It is not wrong to say that Descriptive Analytics is Business Intelligence.

2. Predictive Analytics uses sophisticated algorithms and statistics to forecast multiple outcomes based on combination of metrics. Hence, it answers if-then questions pertaining to business goals and helps in planning for the future.

3. Prescriptive Analytics is advanced complex form of Predictive Analytics. It presents you with possible decision options in various scenarios.

What is Business Intelligence?

Now, let us dive into the realm of business intelligence. If business analytics is about mathematical techniques, data models, statistical methods and machine learning algorithms on data, Business Intelligence is about data warehouse, data visualization and dashboards.

Data Warehouse – Real-time organizational data or data from external sources get piped into one central repository system. Heterogeneous, unstructured raw data is extracted, transformed and loaded into data warehouse, which is at the heart of business intelligence.

Rules and queries are applied on datasets to find interesting patterns. Hence, data analysis is a key component of business intelligence.

Data Visualization and Online Analytical Processing (OLAP)– One word succinctly describes data visualization: storytelling. Interactive dashboards allow users to visualize data from various perspectives and angles. OLAP forms an integral part of BI tools and is the foundation of data visualization. These graphs and charts can be further drilled down to get to the desired information.


Undoubtedly, organizations see business intelligence and analytics as prerequisite means to have their data analyzed and presented in comprehensible format.

Business intelligence tools have come a long way in becoming much more sophisticated so that you can make optimum utilization without intervention of your IT and BI teams.

There are no more experiments, assumptions and gut feelings involved if you have right tools in hand to make decisions. You can derive better results if you choose the one that blends the business intelligence with business analytics capabilities.
Data Scout is one such-self service business intelligence tool that amalgamates everything.

Through Data Scout we let you save every single raw data point to help you figure out what kind of analysis could be done on such huge piles of data. It has an edge over other business intelligence tools in market as it allowA you to take advantage of business intelligence as well as analytics.

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