Data Science vs Business Analytics

In a sea of business buzzwords, oftentimes it can be easy to equate seemingly similar concepts or use two terms interchangeably, and this is certainly the case for Data Science and Business Analytics. While the differentiation of these two terms might not seem of the utmost importance, for those mapping out their careers, this distinction could make a huge impact. Undoubtedly, the scope of opportunity for each is vast. 

Valuates Reports cites that the global Big Data and Business Analytics market size was valued at $198.08 billion in 2020 and is projected to reach $684.12 billion by 2030, growing at a rate of 13.5 percent from 2021 to 2030. This means we can expect the demand for professionals in these two disciplines to continue to grow as well.

But what exactly defines each discipline? What are the similarities? What are the differences? And which types of individuals might be better suited for each?

What Is Business Analytics?

Simply put, Business Analytics is the statistical study of business data to gain insights. Business analysts seek to bridge the gap between information technology and business management by using analytics to provide data-driven recommendations. This requires a foot in both worlds, harnessing a deep understanding of business alongside an equally deep understanding of data and statistics. Business analysts must fill multiple roles, acting as a communicator, a facilitator, and a mediator. The ultimate goal of the business analyst is to determine KPIs (Key Performance Indicators), surface insights, and solve a problem within the company using insights gleaned from relevant data.

What Is Data Science?

A data scientist is a person who utilizes Machine Learning algorithms to create a model from data that ultimately helps to make a business more efficient. There are both technical and soft skills associated with Data Science. Not only do data scientists have to wrangle data and utilize algorithms, but they also need to be able to communicate the process of establishing the problem statement, developing it, and sharing that respective solution to the business. The end goal is to make the business more efficient through more accurate and less expensive solutions. In other words, they seek to improve the overall business through technical expertise.

Where Do These Domains Overlap?

While Data Science and Business Analytics are unique fields, there is a great deal of overlap between them.

Both draw insights which require a deep dive into data to solve business problems and the application of analytical reasoning to guide research and strategy. There can often be similar tools employed as well, including visualization tools such as Tableau, that aid during various stages of work. Communication practices and processes can look a lot alike, as well, working with stakeholders across many functions of a company to go over the business problem, review solutions, and present results.

Both roles share an overarching goal: use data to improve the business. 

However, it is the “how” that differentiates them.

Where Do These Domains Diverge?

The biggest difference is the scope of the problem addressed.

While a business analyst will typically focus on finding trends in data and then developing ways to leverage that information to improve a company’s operations or bottom line, data scientists tend to look more at what is driving those trends.

For example, if a company is looking to build a business plan to tap into a new market segment, you would probably need experts in both Business Analytics and Data Science. In this scenario, the company’s data scientist would work more on the front end of the data collection and initial analysis, processing vast amounts of behavioral data from customers and understanding hidden patterns, which would require technical expertise of problem formulation and algorithms. The company’s business analyst would then extract the pertinent information from the uncovered patterns or trends and translate the right analytical models to present that information to business leaders, aiding them in using this data to drive the direction of a company and incorporate macro changes into the strategy.

There is also a difference in the types of data in which each job function operates. 

Business Analytics works predominantly with structured business data, meaning that data is highly specific and stored in a predefined format (meaning patterns are more easily searchable and more easily translated to provide solutions to specific business problems and roadblocks). Working within this data set, business analysts are more easily able to tell a story through data, able to focus their analyses and interpretations to be specific to the business, and provide an accessible way to see and understand trends, outliers, and patterns.

Data Science, on the other hand, works with both structured and unstructured data. Unstructured data is a heterogeneous combination of many varied types of data that are stored in their native formats, not structured via predefined data models or schema. Mining unstructured data requires a combination of traditional analytics practice with good computer science knowledge, the sweet spot for data scientists. There is also a much larger range of unstructured data to navigate through, so data scientists are responsible for solving for a broader perspective.

Albers School of Business and Economics: Where Business Intelligence Meets Business Action

For those professionals who read through this piece and may feel that Business Analytics would be the better path to explore, will help jumpstart your best career, equipping graduates with the knowledge and practical skills to join the big data revolution and become a valuable asset in helping businesses continue to hone that competitive edge. The program focuses on the very critical skills and abilities addressed in this piece that are highly sought after in today’s data-driven business landscape. 

Through hands-on experience utilizing real-world data, equips students with the technical knowledge they need to identify complex business problems and turn raw data into usable ideas, all while acquiring the know-how and confidence to effectively translate those ideas into actionable, effective steps.

The program, the first of its kind in the Pacific Northwest, was ranked 26th in the nation by U.S. News and World Report’s 2023 Best Graduate Schools Rankings.

Seattle U Is Ushering In A New Era Of Big Data Leaders

For those who read through this piece and felt particularly drawn to the responsibilities of a data scientist, will place you at the forefront of this exciting field by providing a potent combination of front-line technological expertise and the management and leadership skills necessary to implement high-level, data-driven decisions. This interdisciplinary program offers a curriculum based on theoretical foundations and practical applications, based in applications and theory from statistics, computer science, and data analysis. This program ensures that leaders graduate with a larger context of data science methodology to ensure that students develop the necessary skills to apply their knowledge in an exciting and rapidly growing market.

Enrolling in Seattle University’s College of Science and Engineering’s Master of Science in Data Science also gives graduates the added opportunity to expand professional connections with students and professionals around the country.

Students and professionals make a wise choice in pursuing either career. It’s simply a matter of choosing the career path that best fits your goals and skills. And, whichever path you choose, discover how the Albers School of Business and Economics can help you take your career to the next level. We encourage you to explore that will aid professionals as they decide the next best steps for their professional lives.