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The world we live in runs on data that we can use to make better decisions. In the business industry, you have access to different data forms irrespective of your niche. What you do with available data and how you utilize it to create solutions is vital to the success of a business organization. 


Business intelligence and data analytics are the primary ways you can utilize data. However, we find that students with interests in data-driven initiatives often use both terms interchangeably. Although they both depend on data, business intelligence and data analytics are not the same.


Business intelligence (BI) describes past occurrences using historical data. By extension, the exploration of historical data can be a crucial tool for making future business decisions. Conversely, data analytics uses elements of data science to predict future scenarios. 


Here, we’ll highlight the differences between business intelligence vs. data analytics. We’ll also provide examples and in-depth explanations to answer all your questions about data analytics and business intelligence.

What Is Business Intelligence?

Business intelligence means using data to make excellent business decisions. It’s the total of technologies, practices, and software that enhance the collection, sorting, analysis, integration, and presentation of information. 


From the above, business intelligence combines both complex and straightforward data processes to influence business decisions. Some of the fundamental tenets of business intelligence include data mining, business market analysis, and data visualization. These tools will help business managers make informed decisions based on facts and figures. 

How does business intelligence works?

Business intelligence exists to eliminate guesswork and gut feelings while making crucial business decisions. To do that, business organizations will have to sift through information from data sources, including supply chain data, marketing analytics, and sales dashboards.


Business intelligence works by bringing these varied data sources under a single window to provide real-time updates, analysis, and reporting. Here are the primary steps that go into business intelligence:


  • Mining raw data from available databases. 
  • Sorting and data analysis.
  • Turning data into easily understood visual forms like graphs.
  • Sharing data analysis results with team members to make decisions.
  • Comparing and contrasting performances due to data-influenced decisions. 

Examples of business intelligence

Business intelligence has applications in different business operations. Below are some examples:

Customer interaction

Companies that own call centers can enjoy the advantages of business intelligence. Customer interaction analytics can help companies monitor calls and identify phrases that encourage successful calls. 


Business managers will get real-time access to call center statistics highlighting how top agents are making successful calls. Hence, they can duplicate audio patterns and phrases to increase success rates. 

Website traffic

Google Analytics is one of the most popular business intelligence tools. Over 2.5 million websites in the United States use Google Analytics. This business intelligence tool provides a visual analysis of the following metrics:


  • Time spent on page
  • Type of traffic
  • Bounce rate
  • Web page performance
  • Numbers of users and sessions


With the information from these metrics, business managers can make informed decisions about website tweaks, web content updates, and more. 

The future of business intelligence

Making business decisions with accurate figures in mind will reduce mistakes and the wastage of resources. As a result, the future of business intelligence is indeed very bright in every industry. 


You can expect automation and collaboration software to integrate with business intelligence tools for faster results. The future of business intelligence is intertwined with artificial intelligence. Thanks to AI, BI tools are expected to become more intuitive, providing answers to queries differently based on the unique needs of the analyst.

What Is Data Analytics?

Data analytics is analyzing raw data sets to uncover patterns, find trends, answer questions and isolate valuable insights. As a broad field, data analytics involves the use of different techniques and specialized software. It makes the most of machine learning and automation software to turn raw data into actionable information.


The application of data analytics can help organizations understand their market, evaluate marketing campaigns, create relevant content and develop better products.

How does data analytics work?

There are various methods and processes under data analytics

Data mining 

Some experts refer to data mining as text analysis. In this process, you’re trying to pick a pattern or trend from bulk data.

Descriptive analysis

The descriptive analysis provides insight into past performances. For any situation, it answers the question — what happened? 

Diagnostic analysis

This type of analysis looks to uncover the problem in a system.

Prediction analysis

Prediction analysis looks to answer questions about the future. It analyses historical trends to predict whether or not they’ll happen again in the future. Prediction analysis makes use of machine learning methods like decision trees, neural networks, and regression.

Prescriptive analysis

Prescriptive data analysis uses previous research to analyze and proffer a solution. 

Examples of data analytics

Since data analytics is a broad field, it offers application in varying areas:


The Food and Drug Administration uses data analytics to improve the situation of food-related illnesses. By deploying big data tech across labs in the country, the FDA can study patterns relating to food-borne diseases. Thanks to real-time analysis, the FDA can respond faster to curb the spread of contaminated food products.

Preventing hacks

Data analytics can help cybersecurity professionals protect businesses from hackers. By analyzing data sets, experts can detect cyber threats easily and create strategies for dealing with these threats.

Product Updates

Prescriptive analysis of data sets can give product developers valuable insights into what customers want. Data analytics offers a closer look at customer budgets, features that are most important to customers, and purchasing behaviors. With this info, product managers can update current product offerings or design new products to cater to customer needs.

The future of data analytics 

Experts say the data analytics industry will reach revenues of over $250 billion by the year 2022. Over the next few years, you can expect a more comprehensive adoption of data analytics by business users. With advancements in machine learning and artificial intelligence, companies will have to rely more on large data networks to provide specialized services.

Final Thoughts: Business Intelligence vs. Data Analytics

Business intelligence and data analysis both depend heavily on data for success. Business intelligence focuses on past occurrences useful to making future decisions. Data analysis uses algorithms to analyze data sets.


You can first use data analytics to sort, clean, and analyze data sets. Then, use business intelligence to make use of the analyzed data based on facts and previous experience. Incorporating both in your business strategy will help you make better-informed decisions and achieve better results. As for the degree, you can choose whichever you believe leads you closer to your dream career. However, you can always get a Master’s degree in Big Data Analytics.




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