The difference between data science and business intelligence

Business intelligence jobs came into existence way before the field of data science emerged. Previously, the roles of both jobs were quite similar. However, as the terms were further defined and elaborated, the functions distinguished as well. The similarity between the two sometimes causes an overlap among the positions depending on where you are working.

Today’s discussion is all about data science vs. business intelligence. This article will clarify all the misconceptions and confusions regarding the similarities and differences between the two. Read on to find out more. 

Data Science – the Basics

Before highlighting the similarities and differences between both, we need to know some tidbits of data science and business intelligence. Let’s begin with data science first.

As a data scientist, you need to understand how to obtain data from distinctive sources and create a dataset with Python and SQL. Besides, having the ability to develop a case and problem statement with shareholders is also a part of the job description. 

Moreover, data science involves thorough and investigative data analysis, usually with Pandas, and uses software like TensorFlow, Keras, and scikit-learn for model exploration and tentative comparison. 

How Is It Similar to Business Intelligence?

On the other hand, business intelligence jobs include understanding and obtaining data from different sources and discussing their results and impact, just like in a data science job. Besides, they also finalize the models and deploy the essential factors to see the final results – quite similar to the position of data scientists, right?

Well, this is a short snippet of what you can expect in data scientist jobs. 

What’s the Difference?

The significant difference that we can highlight is the emphasis on the comparison, final model, model exploration, and implementation. While data science mainly focuses on machine learning operations and algorithms. 

Thus, we can say that the main difference between the two is that the business intelligence professionals will be involved in forecasting, prediction, and regression analysis. 

Besides this, they also emphasize Python and various other programming languages where object-oriented concepts are used in data science. 

Business Intelligence – Things You Need to Know

You might be caught up with the idea of data science till now. Let’s discuss a bit about business intelligence to get a better grasp of the difference between the two. 

Business intelligence analysts form a use case with the shareholder. Using VLOOKUPs, SUMIFs, and Excel to execute the data and SQL to gather the information is involved in their job descriptions. Moreover, they also analyze the necessary information with complex probe functions. Finally, they display the results in different visual tools like Tableau or Looker and discuss them with the executives and shareholders.

Business intelligence analysts are more involved in directly tackling the clients and shareholders and presenting the data in the form of visualizations. Which a data scientist doesn’t do. 

The Similarities

The field has been around for a much longer time and overlaps with data science jobs as well. The most significant similarity is the aim of both the roles. Both the positions thrive on forming the use case and evaluating the results. The methods, however, can be different. For instance, business intelligence professionals emphasize more on SQL, Tableau, Excel, etc. 

The Major Differences

The tools of both roles overlap; however, one key difference between business intelligence and data science is that there is no deployment of machine learning algorithms in business intelligence. 

Applying for either position requires a Master’s degree. However, you need to have additional certificates to better grasp concepts like SQL, Python, SUMIFs, etc. Moreover, working as a data scientist and a business intelligence analyst for a specific duration can help you understand better. 

Summary

Despite so many similarities, both fields are quite different. This is why it is necessary to figure out the ins and outs of every position and its implementation in various fields.

Yes, the aims might seem similar in terms of insights, data, and results. But the methods applied in both cases are pretty different. Previously, the scope of business intelligence was more than data science, and the differences weren’t as clear. But gradually, the advancement has led to highlighting the similarities and differences between the two. Hence leading towards two different job positions.

But the good thing is, just like business intelligence, data science is a high-paying field too. So, no matter which file you pursue, you will surely be making good money. 

For more informative and knowledgeable content, stay connected.