Business Analytics vs Data Science

My personal journey through university and the workforce taught me that, while business analytics shares similarities with data science, they are distinct disciplines. Moreover, the differences tend to grow over time as your career progresses.

Data science focuses on computer models, programming, and completing discrete tasks. It is technically demanding. If, like me, you don’t love working with data for months on end, it might not be for you. Are you comfortable spending many hours in the computer lab? You need that capacity to perform well both as a student and, eventually, as a practitioner.

Business analytics does have technical elements, but you also get user-friendly tools. In some roles, your main job is to convey straightforward data patterns in simple terms that everyone can understand. It offers the flexibility to focus on the decision-making side of analytics. I did that, leading me to eventually become a policy strategist and later a business owner.

Key Takeaways

To save you the trouble of reading the whole article, here are my top insights from studying and working in both fields.

Data science is technically harder. A data scientist needs programming and data wrangling skills that are hard to learn, skills which business analysts often lack. By contrast, most data scientists could quickly learn the software and tools business analysts use.
Data science requires more education. You can start in business analysis with a bachelor’s degree and probably be on your way. In data science, undergraduate studies just lay the basic foundation. Years of postgraduate studies and/or professional development are often needed before you can truly call yourself a data scientist.
Business analytics offers a more rounded career. As a business analyst, you can focus on the data and even work in the data science realm. But you can also become more involved in giving strategic advice. There’s potential to move away from the technical aspects if you develop a passion for business management.
The pay rates are closer than salary statistics might suggest. Data scientists do earn more than business analysts. But this is expected since the former is technically harder and requires more education. If you adjust for talent and skill development, the pay differences decrease. So, you don’t automatically make more money by choosing one over the other. It depends on your particular combinations of abilities and preferences.

What Is Business Analytics?

Business analytics, compared to data science, is business-focused. Business analysts solve specific questions or problems using data analysis, statistical modelling, and other quantitative methods. The goal is to help inform business decisions with well-chosen and presented data insights.

Business analytics has overlapping areas with data science, such as data analytics and marketing analytics. However, the discipline itself can be considered a subset. While a Master of Data Science can prepare you for work in business analytics, a Masters in Business Analytics might not achieve the reverse. Data science encompasses a larger field of knowledge.

I once failed as a business analyst when giving budget advice to the head of Australia’s Finance Department. He wanted my expenditure trend analysis presented on a single chart for him and the ministers to easily grasp. Unfortunately, that was technically impossible. My great insights into spending patterns were subsequently overlooked. To succeed with business analysis, you have to be able to influence decision-makers even if, frankly, that involves dumbing down your work at times.

What Is Data Science?

Data science is an amalgamation of several fields, including data mining and analysis, machine learning, natural language processing, and artificial intelligence (AI). It serves both scientific and business-oriented purposes with one primary goal: helping organisations harness and exploit their data assets.

The discipline began to take on a coherent form in 2008 when Jeff Hammerbacher and DJ Patil coined the term “data science” to describe the emerging field of study that focused on extracting hidden value from data. Since then, data’s significance for businesses has only grown. Today, data science is universally recognized as essential for unlocking the wealth in data assets.

My first job after university had a significant data science component, though the term hadn’t been coined yet. I used SAS programming to make data collected on Australia’s fisheries accessible to policy analysts and program managers. I enjoyed the job and got stuck in. However, I did notice that the role was somewhat isolating. No-one but me and two other guys had much of a clue about what I was doing. My various challenges and triumphs largely went unnoticed.

Related: Is Business Analyst an IT Job?

What Does a Business Analyst Do?

The role of a business analyst is not just about crunching numbers. It’s about understanding client needs, being resourceful, and making data work in the most impactful way. With tools like PowerPoint, Excel, Trello, and Smart Draw in our arsenal, we simplify and visualize information.

Here’s a work example. I was once tasked with recommending how to best use the numerous datasets stored across the Industry Department. The data science approach was to stocktake all the data and put in place systems to make it accessible.

I chose a different approach. I surveyed users to find out what information they needed or found useful. Then we set up information systems to answer those questions. This approach was more practical and less resource-intensive.

In essence, business analysis starts with the questions and often requires statistical analysis to inform solutions, whereas data science starts with the data. As a business analyst, you don’t have to be so methodical or sophisticated in handling data. It is a pragmatic line of work. You only dive into the data to the extent necessary to answer meaningful questions.

What Does a Data Scientist Do?

Data science is used to solve business-agnostic problems through data. A typical workday for a data scientist might involve creating a database to collect or combine various structured and unstructured data sources. They then clean, tag, and analyze these datasets to derive insights.

The work is technical, which is why their tools of the trade include complex apps like Apache Spark, SAS, BigML, and Matlab. Natural language processing and machine learning are common approaches in this role since you’re often creating algorithms and training them to perform specific functions.

As a data scientist, you’re not primarily focused on translating statistical information into digestible insights. Often, that responsibility falls to the business analyst, who takes clean but high-level intel and transforms it into facts that business executives can understand. You act more as the data keeper, organizing it, creating tools to utilize it, and identifying patterns using programming and mathematics.

I ventured into data science while working in a data-intensive role at the Agriculture Department. With my computer science background and the organization’s need for someone to convert raw data into actionable statistics, I found myself in charge of vast datasets on commercial fishing. This role meant I was the primary point where data was cleaned and transformed. As a “data scientist”, I was deeply involved in coding and data transformations, spending almost no time on policy and program decision-making.

Business Analyst Skills

To succeed as a business analyst, you need to do many things at least reasonably well. Technical competence is essential, but it’s not everything. I’ve found writing skills to be crucial too. The art of writing is key to being persuasive. The briefs I wrote for ministers were most impactful when they told a concise, compelling story.

Another important skill, which grows with experience, is understanding organizational power and influence dynamics. Who are the key figures in your organization, and how do they operate? If you can align with the right people, you can transition from being a technical analyst to an influencer.

A whole new world opened up for me when I gained the trust of the head of the Industry Department. My advice became sought after, and I represented the department in high-level working groups and consultative committees. I even won a couple of awards for leadership and policy impact. I could focus on business strategy while relying on others to do the supporting analysis to underpin it.

In terms of technical skills, data visualization and storytelling are core skills for business analysts. Additionally, analytics professionals might be competent in one or more programming languages such as C, C++, and C#. A Masters in Analytics program could cover topics from programming principles and big data management to digital customer analytics.

Data Scientist Skills

To work as a data scientist, I believe the ability to program is essential. Even if AI handles much of the programming these days, coding matters. It’s not just about the coding itself but what it suggests. Coding shows you have a logical, organized mind. It indicates persistence with technical tasks and a knack for abstract concepts.

Mathematical ability is another key talent. A strong math background helps you understand complex algorithms. It aids in navigating statistical models. With solid math skills, your data interpretations are more precise. This foundation ensures better data analysis and informed predictions.

Education Differences

To become a business analyst or data scientist, a strong university education is usually needed. For business analytics, postgraduate study helps but isn’t always essential—especially for those with an undergraduate background in analytics or a related field.

Data science, by contrast, is typically taught at the postgraduate level. It demands a broad skill set, including machine learning and AI, which are complex and fast-evolving. As a result, students usually enter the field after first completing a technology-focused degree.

I was able to work in both fields thanks to a computer science-heavy undergraduate degree with majors in mathematics and economics. Still, I felt more qualified as a business analyst because the technical demands were lighter. Today, the difference in technical complexity is likely even greater, with AI and machine learning becoming central to data science.

Business analyst qualifications

Business analytics professionals usually have a bachelor’s degree in fields like business, statistics, or computer science. An ability to work with numbers is indispensable to the data wrangling and interpretation aspects of their work.

Candidates for postgraduate business analytics courses may, therefore, come from either a technology or predominantly business background. For real data enthusiasts, data analytics courses may be ideal preparation for a business analysis career.

In a master’s foundation course, such as a Graduate Certificate in Business Analytics, students from a business background may be introduced to topics such as database management, information systems, and introductory data analytics. Conversely, those with a technology background may study accounting, customer value, and financial management.

Data scientist qualifications

Data science professionals often have an undergraduate technology degree, with a major in computer science, information systems, statistics, applied mathematics, or data science.

Most data scientists also have a master’s degree. The most popular is a Master of Data Science or similar, such as a Master of Applied Data Science or Master of Data Science Strategy & Leadership. In addition, professional certifications in Neural Networks, Deep Learning, or Machine Learning may be required.

Business information systems

If you’re torn between the two lines of work, consider a Masters in Business Information Systems (MBIS). This degree offers a mix of IT and business strategy, suitable for various undergraduate backgrounds. It’s less technical than a data science program but provides essential skills for roles in both analysis and information systems management for business, offering a flexible career path.

Job and Salary Comparisons

Typically, business analytics professionals work as “business analysts”, while those in data science are termed “data scientists”. The work can overlap and the jobs have other titles of course. Often, a careful examination of job duties is needed to really appreciate what a particular position entails.

In the U.S., data scientists earn an average of $126,565 annually, reflecting their specialized skills and training. In comparison, business analysts have an average salary of $86,618 with a $3,500 bonus, while senior business analysts earn around $103,299 with a $3,000 bonus, according to Indeed.com.

This is not to say that a given individual will earn more by entering the data science profession. For example, someone with a flair for business strategy and communication who has leadership qualities could have more success in business analytics.

Business analytics jobs

Business analysts potentially have a track into business leadership roles and data-focused positions. Potential career paths for analysts include:

Operations Research Associate
Quantitative Analyst
Business Analyst
Market Research Fellow
Business Performance Analyst
Strategy Leader

Data science jobs

Data scientists have numerous career options in theoretical fields with a research focus and applied roles within organisations and corporations. Some of the career options for data scientists include:

Machine Learning Engineer
Data Scientist
Applications Architect
Data Engineer
BI Developer