Data Science vs. Business Intelligence

Data Science or Business Intelligence? You’ve probably heard these terms in meetings, articles, LinkedIn... but do you really know what they mean? While both share the mission of turning data into decisions, their approach and tools are very different. 

Here’s everything you need to know to understand who does what and how they impact the business world. 

Welcome to the world of data 

The evolution of data analysis has led to the development of two interconnected disciplines: data science and business intelligence (BI). 

While both focus on using data to make informed decisions, their approaches, tools, and objectives vary greatly. 

What is Business Intelligence (BI)? 

The present and past of business analysis 

BI is like that friend who organizes the party but ensures everything stays under control. Its goal is to analyze historical and current data to make informed decisions. Tools like Power BI and Tableau are its best allies, offering dashboards and detailed reports to light the way. 

Practical BI use cases 

• Tracking sales and performance. 

• Analyzing KPIs to improve productivity. 

• Identifying consumption trends in real-time. 

What is Data Science? 

The magic behind predictions 

If BI organizes the party, Data Science predicts which music will be a hit. Here, artificial intelligence, machine learning, and advanced statistical models come into play. Data scientists work with unstructured data, uncovering patterns and building predictions that can change a company’s direction. 

Practical Data Science use cases 

• Sales prediction models. 

• Algorithms for service personalization (like Netflix or Amazon). 

• Sentiment analysis on social media. 

Key differences between Data Science and BI 

Feature	Business Intelligence	Data Science
Purpose	Descriptive analysis.	Predictive and prescriptive analysis.
Data	Structured.	Structured and unstructured.
Tools	Tableau, Power BI.	Python, R, TensorFlow.
Users	Analysts, business teams.	Data scientists, engineers.

Which one does your company need? 

The answer depends on your objectives. If you aim to optimize what you already have and understand the present, BI is your ally. But if you want to innovate, anticipate the future, and leverage complex data, Data Science is your best bet. 

Practical Applications 

Data Science Use Cases 

• Sales Prediction: Using predictive models to forecast future sales based on historical data and market trends. 

• Sentiment Analysis: Analyzing social media comments to understand customer perceptions about a product or brand. 

BI Use Cases 

• Financial Reporting: Creating detailed reports on the company’s financial status, identifying trends and areas for improvement. 

• Customer Analysis: Identifying customer behaviors and purchasing trends to improve marketing strategies. 

It’s not a competition, it’s teamwork 

Though their approaches differ, Data Science and BI work best together. Combining both disciplines can elevate your company to a new level, offering a comprehensive view of the past, present, and future. 

Why study BI and Data Science? 

At La Salle Campus Barcelona you have the opportunity to specialize in these key areas with programs designed for the future.   

The Degree in Business Intelligence and Data Analytics prepares you to master the tools and techniques needed to convert data into strategic decisions, while the Master in Data Science delves into artificial intelligence, machine learning and big data, training experts capable of leading digital transformation projects.   

A perfect combination to stand out in an increasingly competitive labor market.  

Bachelor’s in Business Intelligence and Data Analytics 

• Strategic vision: Learn to interpret data to make evidence-based decisions and enhance business performance. 

• Cutting-edge tools: Master software like Power BI, Tableau, and relational databases. 

• Employability focus: The program includes internships at leading companies, directly connecting you to the job market. 

Master’s in Data Science 

• Specialization in AI: Learn to build and train advanced predictive models with machine learning and deep learning. 

• Big data expertise: Explore how to manage and analyze large volumes of unstructured data. 

• Leadership preparation: Become a leader capable of driving digital transformation projects.