Business Analyst vs. Data Scientist

business analyst vs data scientist

Did you know that data has now surpassed oil as the world’s most valuable traded commodity? Millennials are at the forefront of a new generation of professionals who are harnessing the power of big data to help shape business strategy and give companies a competitive edge.

The need for unique data science and analysis skills has created a growing demand for specialists in this field. According to a report from Deloitte Access Economics, Australian workers with big data skills are expected to earn almost $20,000 more on average by 2022 than they do today.

So, let’s take a deep dive into two career paths within the evolving big data and analytics environment: business analyst vs data scientist.

How do these roles differ? What do they involve? And which career path will suit your character and skillset?

Both roles involve gathering data, modelling and working with information to draw insights, but there are also some key differences.

Data science is the science of data study using statistics, algorithms, coding and technology.

But what if you’re interested in data analytics, but not a coding or maths whiz?

Business analytics might be a better fit for you. This branch focuses on delivering data-driven business decisions and operational insights to different business units using smaller, and more targeted sets of data.

The latest Institute of Analytics Professionals of Australia (IAPA) Skills and Salary Survey shows that an analytics professional with business-focused insights and a good breadth of soft skills (including communication, presentation and business leadership) may actually be more employable than one with only a range of technical skills.


We break it down further for you here:



What they do

A business analyst takes the reports produced by data scientists or other number-crunching professionals, assesses it for business implications, and recommends solutions and actions as a result.

The data is largely used to help decision-makers understand the past and current performance of a company and be able to predict and forecast the business environment to solve problems or improve future performance.

A data scientist is like a data detective, looking for patterns and information about consumer behaviour or systems within data. It is their job to sort through data accumulated by the company, and to detect and report what kind of insights the data may contain.

Data science combines data with algorithm building and technology to answer a range of questions. Therefore, data scientists develop more technical skills in collecting and analysing data.

Day-to-day tasks

A business analyst takes on a range of different tasks, across a variety of areas. Some of these might include:

  • Analysis of business needs
  • Strategy development
  • Defining a business case
  • Communicating with stakeholders
  • Project management and development
  • Data modelling and data visualisation
  • Testing and validating solutions

A data scientist will perform more technically and mathematically oriented tasks on a day-to-day basis. Some of these will include:

  • Programming and data pre-processing
  • Report writing
  • Modelling and prediction
  • Analysis of data patterns, trends and statistics
  • Coding
  • Visualisation and interpretation of data
  • Developing and applying models to mine data stores

Key skills

A business analyst needs to be confident in a broad range of skills. These will encompass ‘soft skills’ and business management skills, as well as a technical understanding of data and data collection technologies. Key skills for this role include:

  • Deep understanding of business environment and markets
  • Change management skills
  • Project management and communication skills
  • Critical thinking, strategic analysis and creative thinking skills
  • Foundational data science skills
  • Knowledge of tools like MS Excel, MS Visio, SWOT

A data scientist will need a more technical skill set, that requires knowledge and competence in certain programs and technologies, as well as data analysis skills. Some of these skills include:

  • Thorough knowledge of statistics and other mathematical concepts
  • Experienced with various tools like Python, R, SAS, and well versed with SQL and NoSQL.
  • Knowledge of machine learning algorithms
  • Possession of big data tools like Mahout, and Spark
  • Programming, coding and modelling skills


A business analyst role does not generally involve much coding. You’ll gain familiarity with statistics and the fundamentals of data analysis. A strong background in mathematics is unnecessary.

A data scientist will require a strong background in math and technical skills in several common coding languages including Python, SQL and Java.

Personality traits for success

According to Modern Analyst, here are 6 personality traits essential to being a successful business analyst:

  1. Engaging communicator: to put forward a business case, interest stakeholders, motivate a team, engaging communication is essential
  2. Keeping a cool head during conflict: helps when managing different stakeholders with conflicting interests
  3. Multi-disciplined: must have a grasp of the tech and the business side of the role
  4. Inquisitive: you can only find the best solutions through asking questions
  5. Strategic thinking: to effectively turn data into company strategy

According to Charles Rice, Data Scientist (writing for Dataquest), here are 5 essential personality traits that make for a great data scientist. They are:

  1. Curiosity: your job is to discover, uncover and reveal hidden messages
  2. Clarity: be able to clearly articulate your findings and be very details focused
  3. Creativity: thinking differently helps to uncover hidden secrets in data you wouldn’t see otherwise
  4. Skepticism: no model is perfect, work with what you have

If you’re thinking of getting into the world of big data, you might want to check out the NEW range of business analytics courses at KBS including a Master of Business Analytics.

business analyst vs data scientist