Data Science vs Business Analytics as a domain of work is one confusion that every student of data science and analytics struggles with, and understandably so. These terms are often used interchangeably in popular discourse when in reality, there are fundamental differences between these two domains.
In this article, let’s break down the difference between data science and business analytics to help you understand each better.
Let’s start by understanding the problems that business analysts and data scientists solve.
Business Analysts vs Data Scientists – The Types of Problems They Solve
Here’s an interesting example to understand this.
Suppose you manage a bank – you are responsible for implementing two important projects. With you is a team of data scientists and business analysts. The two projects are:
- Strategise a business plan to identify the number of employees required to do business worth $XXXX.
- Develop a model to identify fraudulent or potentially fraudulent transactions in the system.
Which one do you think should be mapped to which team?
If you think deeply, you’ll realise that the ask of the first problem is more about making business assumptions and modifying the strategy by making macro changes. To do this successfully clearly requires good business understanding and decision making skills. On the other hand, the second is about finding patterns from data and making meaningful decisions.
Thus, while the first project maps rightly to the business analysis team, the second one to the data science team.
With that settled, let’s now dive deeper into both of these domains and understand the skills required to excel in them.
Business Analytics
The role of Business Analytics is to act as a gap between business operations and IT by using analytics techniques and providing data-driven suggestions. As a result, business analysts must have a good business understanding and necessary data skills – like statistics, computer science, programming, etc.
What does a Business Analyst do?
A business analyst acts as a mediator between IT and business domains. Their goal is to find the best ways to improve processes and enhance productivity by using data, technology, and analytics.
Skills required for Business Analytics
Here are some important skills required if you wish to excel in Business Analytics:
- Data interpretation: Businesses deal with an ever-increasing pile of data. Business analysts must understand and interpret this data, clean it accordingly, and find insights from it.
- Storytelling and visualisation: Communicating the findings is another important task of business analysts. They act as a bridge between IT and business and should be able to communicate their conclusions seamlessly to all the parties involved. This includes using visual aids like charts, graphs, and so on.
- Analytical reasoning: Business analysts need to be quick decision-makers, which requires critical thinking, logical thinking, analytics, etc. The reasoning abilities come in handy in day-to-day operations when business analysts deal with and make sense of data.
- Statistical and mathematical skills: The ability to properly describe the data is important for business analytics. This requires knowing relevant statistical and mathematical tools. This skill also comes in handy during scenarios when they are needed to model, infer, estimate, or forecast based on the current data.
- Communication skills: Both verbal and written communication skills are important for a business analyst. Since they fill the gap between two important domains, they act as primary communicators and information providers. In such a scenario, it becomes more important to be clear and concise in your communication.
Data Science
Data science is an umbrella term that includes algorithms, statistics, computer science, and allied technology to take a deep dive into big data and find patterns from it. The goal of data science is to make informed, data-backed predictions by studying previous trends, habits, etc.
What does a Data Scientist do?
Data scientists work with different algorithms – ranging from native algorithms to machine learning algorithms to business data and identify patterns. These patterns are useful for predicting future behaviour or outcome. They also create different hypotheses, test them based on the available data, and accept or reject them based on the test results. The overall goal is to make better predictions that lead to overall business goals.
Skills required for Data Science
The primary skills required for a successful career in data science include –
- Statistics and statistical analysis: Since hypothesis formation and testing are important parts of this role, data scientists must be hands-on with different statistical tests, likelihood estimators, etc.
- Programming and computer science: Computer science skills are extremely relevant for data scientists since they work with different algorithms. It would be good to be able to optimise these algorithms or study them deeply from a computer science perspective. Further, they need programming skills to deal with business data and find patterns. Some important programming languages include – Python and R.
- Machine learning: Data scientists must be familiar and even hands-on with machine learning. This includes working with different ML algorithms and analysing and optimising them as and when required. Machine learning has helped data scientists uncover a lot more from data than ever before, making it an irreplaceable tool in a data scientist’s toolkit.
- Data visualisation: At the end of the day, data scientists, too, are required to communicate their findings. This requires having data visualisation skills to convert technical data into easily understandable information.
Business Analytics vs Data Science – A Comprehensive Comparison
| Business Analytics | Data Science |
| Statistical study of business, business goals, business data to gain insights and develop better strategies and processes. | Study of data using methods derived from computer science – like algorithms, mathematics, and statistics – to find patterns and make future predictions. |
| Deals primarily with structured data. | Works with both unstructured and structured data. |
| This is more statistics and analytics oriented – it does not require much programming. | Heavily relies on programming to create models which identify patterns and derive insights. |
| The entire analysis is statistical. | Statistics is just one part of the entire process and is performed at the end – after programming the required models. |
| Mostly important for the following industries – healthcare, marketing, retail, supply chain, entertainment, etc. | Mostly important for the following industries – e-commerce, manufacturing, academics, ML/AI, fintech, etc. |
Career Paths in Business Analytics and Data Science
Business Analysts tend to progress in more business-oriented strategic roles, which also involve entrepreneurship. Contrarily, data scientists are more into research and programming, which makes them better suited for being project managers or head data scientists.
Here is a concise table listing the different career options available in Business Analytics and Data Science field. Please note that the job roles are increasing in their level of position from top to bottom.
| Data Science | Business Analytics |
| Data Scientist | Business Analyst |
| Sr. Data Scientist | Sr. Business Analyst |
| Chief Data Scientist | Analytics Manager |
| Data Science Lead | Analytics Lead |
| Product roles/entrepreneurship | Organisational leadership roles |
Conclusion
Both Business Analytics and Data Science are extremely inviting and innovative fields. If you are interested in understanding data, you will find yourself satisfied in either of these fields. However, there are subtle differences between the two – we hope we clarified that for you in this article!
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