A Data Science Career vs. a Business Analytics Career

Stylized graphic of a businessman interacting with a data analytics dashboard with an empty boardroom in the background
Stylized graphic of a businessman interacting with a data analytics dashboard with an empty boardroom in the background

In this era of Big Data (data in vast quantities and readily obtained), business analytics is front and center of most organizations in a way not often seen in the past. Along with Big Data also comes the relatively new field of data science and the resultant job title of a data scientist. For the individual interested in both business analytics and data science, the big question then is – is it worthwhile to enroll in an accredited MSBA program or one strictly focused on data science? What is the difference between the two?

Per the Bureau of Labor Statistics, careers such as operations research analysts and business analysts have a median salary of $83,390 a year, with jobs expected to grow by 27% between 2016 and 2026. The median pay for mathematicians/statisticians (the realm of data science) is $88,190 with job growth expected of approximately 33% in the same time frame. With an advanced education (such as a master’s degree) and experience, these salaries could readily go into six figures.


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What is a Business Analyst?

Business analysts typically hold a master’s degree from an accredited MSBA program, have strong analytical and statistical skills and knowledge, along with an in-depth understanding of their field of business. The business fields themselves can be extensive, including:

  • Management
  • Marketing
  • Operations
  • Finance

A business analyst role includes an array of responsibilities ranging from gaining a solid understanding of the business problem to delivering data-driven evidence to inform the decision-makers. The bottom line, according to Discover Data Science, is the business model. The business questions that arise from the model might include:

  • Are business objectives being met?
  • Are business operations smooth and efficient?
  • Do we understand who our customers are?
  • Are we getting the best return on investment?

The business analyst would then employ the use of business analytics tools (such as SAS and Tableau, among others) to analyze past performance and predict future behavior. This analysis involves looking at trends and patterns within the data, as well as exploring other factors that may influence such things as marketing effectiveness or purchasing choices. To accurately analyze these data, Forbes Tech Council highlights the importance of skills as well as the right tools. It is critical to have a strong understanding of the business model and context surrounding the issue being explored. This understanding, including knowledge of the Key Performance Indicators (KPIs) of the business, will permit the analyst to deliver the most meaningful solutions.

Teamwork is often critical in solving the business problem and evaluating the business model. The analyst will work with individuals from several units to solicit input and subject matter expertise. Along with analytical skills and business knowledge, the analyst also needs the requisite training to be a highly effective communicator. Communications will be across various levels, including teammates, internal and external stakeholders, and decision-makers. The presentations created by the analyst include summaries and are frequently highly visual so that they can be readily understood and interpreted across units.

What is a Data Scientist?

Like a business analyst, the data scientist focuses on data collection, preparation, and analysis. This individual typically has a strong background in math, statistics, and programming. The data scientist is generally working with Big Data from a variety of sources – mobile computing, web sites, social media, etc. As with the business analyst, the data scientist is sorting through the data looking for patterns and trends to develop a predictive model. A data scientist will also create simulations to test potential business solutions for overall effectiveness and cost. Data scientists also present the results, and they too are generally highly graphical presentations.

Where the Differences Lie

Likely the most obvious difference between a business analyst and the data scientist is the focus: the business analyst has the business model at the core of his or her work and thus is typically specialized. The data scientist, on the other hand, may not be limited to any particular business field or specialty. The data scientist will engage with more advanced level of programming/coding (often including machine learning). As noted, a business analyst usually works within a team. A data scientist, however, generally works independently (while consulting with various units as needed). While both must be concerned with data governance (a company’s formal agreement of how data are to be used and managed) and security, the data scientist often is tasked with data storage and architecture.

Towards Data Science outlines some specific differences in data science and business analytics as careers that are summarized in the following table:

Business Analyst Data Scientist
Solves for the unknown with known technology/solutions Solves for the unknown with yet unknown approaches/methods
Typically uses established datasets Uses raw datasets
Typically employs less coding Employs more coding

The Business Environment

The environments that the business analysts and data scientists work within can vary significantly. The corporate environment can be generally less flexible given the need for:

  • Reliability in outcomes
  • Adherence to the business model
  • Security concerns
  • Business policies and practices

A data scientist needs to operate in a relatively fluid environment, which would include:

  • Access to unprocessed data
  • Freedom to use open source applications/multiple applications
  • Ability to test various approaches

Data Science Career and Business Analytics Career: What’s in Common

Even as a data scientist is more of a mathematician and the business analyst more of an expert in their field, similarities are present such as the need for both to have:

  • Statistical knowledge/theory
  • Problem-solving capabilities
  • Critical thinking skills
  • Communications skills

Both are interested in using the available data to look beyond it. Very often, business analysts and data scientists will work together; however, it is very reasonable to look for an accredited MSBA program with data science embedded within. This would entail a program that covers:

  • Data mining
  • Programming/coding
  • Data analytics
  • Predictive analytics
  • Enterprise products
  • Project management

A robust program will also include effective reporting training and communication to technical and non-technical audiences. Also, look for leadership training – analysts no longer operate in the background of organizations. Very often, they are now found in leadership roles, from project planning and management to working directly with stakeholders and decision-makers to ensure the best use of the results. The ideal program would also be offered online. This would permit the student to continue in his or her current role while preparing to enhance their position or potentially climb to a brand-new level. Suffolk University offers a Master’s in Business Analytics Online, which covers the above requirements.


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It may no longer be necessary for a business interested in both business analytics and data science to have to choose between individuals. Online programs such as Suffolk University’s MSBA prepares future employees to offer both.