Business Analytics vs. Data Science: What’s the Difference?

By Indeed Editorial Team

Published October 21, 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Many businesses in various industries, including retail, health care, manufacturing and finance, use data to make business decisions. Business analytics and data science are two growing fields that study and analyze data to help businesses increase their efficiency and improve their operations. If you're considering a career in business analytics or data science, you may want to learn more about how the two areas compare and contrast. In this article, we define the two fields and explore some key similarities and differences between business analytics and data science.

What is business analytics?

Business analytics is the study of data using statistical analysis to make key business decisions for a company. Professionals who work in this field use their analysis to make data-based recommendations about how to solve specific business challenges. For example, they may study a company's financial reports to determine trends and identify solutions to improve revenue. This field often uses historical data to forecast trends about a company's future growth. Using business analytics, companies can gain valuable insights to help develop their strategic goals and achieve those objectives.

Related: Learn About Being a Business Analyst

What is data science?

Data science is the study of data to learn new insights. The field uses statistics and computer science to manage, process and analyze data to predict outcomes or find solutions. Those who work in data science develop algorithms and statistical models to help them understand patterns in the data. Data science has many uses in a range of industries. For example, data scientists can analyze data to identify weather patterns. Other data scientists work in machine learning, which is an area of artificial intelligence that uses algorithms to program computers to become more accurate at simulating human behavior.

Related: Learn About Being a Data Scientist

What are the similarities between business analytics and data science?

Many people use the terms business analytics and data science interchangeably. Although they do have key differences, they also have several similarities, including:

Use of data

Both business analytics and data science center on the analysis of data. They both rely on data to develop new insights using statistical methods. While other methods of studying and reviewing data may differ between the two fields, both of them aim to find ways to organize complex data. Their work helps others understand complex information in a manageable way by defining the trends and patterns of the data.

Goals in business

Though they use different techniques to arrive at their conclusions, both data science and business analytics have similar goals in business. Both fields study and analyze data to improve business operations. For example, a data scientist can explain sales trends by analyzing a company's revenue data, and a business analyst can use that same data to make suggestions about how to drive future growth. Often, professionals in the two fields can work together to help businesses discover trends and make improvements based on data analysis.

Predictive modeling

Business analytics and data science both use predictive modeling techniques to forecast future outcomes. Predictive modeling is the process of using statistical methods to analyze past data to predict future events. While business analysts use predictive modeling primarily to forecast a company's future growth, data scientists can use this type of modeling for many applications. For example, a data scientist may use this technique to predict future trends in the weather based on historical data.

Related: Everything You Need To Know About Predictive Analytics

Strong outlook

There's likely to be a growing demand for professionals in both business analytics and data science. The U.S. Bureau of Labor Statistics (BLS) predicts employment for management analysts, an occupation that overlaps with business analytics, to grow 14% by 2030. For computer and information research scientists, including professionals working in data science, the BLS expects employment growth of 22% in the same time frame. Both rates are faster than the average of all occupations. BLS attributes the growth in these fields to an increase in data collection and a need for professionals who can use data to improve operational efficiency.

Differences between business analytics vs. data science

While there are some similarities between business analytics and data science, there are many distinctions between the two fields. These differences include:

Applications

Business analytics and data science differ in their applications of data. Business analytics focuses on analyzing statistical patterns to inform key business decisions. Professionals in this field analyze historical data to make recommendations to company leaders, managers and other stakeholders about the future of a company. Data science has broader applications in business. While data scientists also work to find patterns in the data, they focus more on understanding what causes those trends. These professionals rarely provide suggestions to companies based on their analysis of the data.

Use of coding

Data science is a combination of statistics, data inference and computer science. Professionals who work in data science require excellent coding skills to help them develop algorithms to collect, organize and manage data. They can also use coding to create algorithms to process large sets of data quickly and find connections between datasets. In contrast, it's rare for professionals in business analytics to use coding to analyze data. Instead, they use statistical methods to study data and gain insights.

Emphasis on statistics

While both fields rely on statistics for data analysis, statistical concepts are more important in business analytics. Professionals in business analytics use statistical-based methods to review business data and help them make decisions for a company. They use statistics at each stage in their analytical process. In comparison, data science begins by programming and building an algorithm to interpret the data. These algorithms help them make connections within the data and determine trends. Data scientists only use statistics at the end of their process to analyze the results of the dataset.

Types of data

Data science and business analytics differ in the types of data they require to make predictions and determine outcomes. Business analytics uses mostly structured data, which is information that exists in an organized database. Structured data typically includes numbers and text. Professionals in business analytics can use this structured data to analyze business trends and find solutions to problems. Data science, in comparison, can use both structured and unstructured data, which is data in its original format. Examples of unstructured data include social media posts or images. Data scientists can build algorithms that work with both types of data.

Tools

The fields of data science and business analytics require different tools for analyzing data. Common tools in business analytics include spreadsheet software applications to make calculations and Structured Query Language (SQL) to organize data. While data science also uses SQL to manage databases, professionals in this field require more technical tools and skills to do their jobs effectively. For example, data scientists are typically familiar with several types of programming languages, including Python and C++, to develop algorithms. They may also use machine learning libraries, which are interfaces that build models for complex tasks without the use of extensive coding.

Please note that none of the products or companies mentioned in this article are affiliated with Indeed.

Related: What Is SQL? Definition and Benefits

Careers

Both fields offer many job opportunities for professionals with an interest in data and information. Jobs in business analytics can be a good career choice for people who want to use math and statistics in a business or project management role. Some common careers for people with a business analytics background include business analysts, market researchers and quantitative analysts. Professionals who have excellent technical skills and programming knowledge can do well in data science jobs. Common career choices for professionals in data science include data scientists, machine learning engineers and data architects.

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