Difference Between Business Analytics and Data Science

Every student of data science and analytics struggles, and reasonably so, with the confusion of data science vs. business analytics as a field of study. Although there are important distinctions between these two areas, both phrases are frequently employed interchangeably in popular speech.

To assist you better comprehend each, let's dissect the distinction between data science and business analytics in this post.

To begin, let's comprehend the issues that business analysts and data scientists attempt to resolve.

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:  

• Strategies 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 realize 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 visualization: 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 behavior 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 optimize 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 analyzing and optimizing 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 visualization: At the end of the day, data scientists, too, are required to communicate their findings. This requires having data visualization skills to convert technical data into easily understandable information.  

Business Analytics vs Data Science – A Comprehensive Comparison

Business Analytics

• Statistical study of business, business goals, business data to gain insights and develop better strategies and processes.
• Deals primarily with structured data.
• This is more statistics and analytics oriented – it does not require much programming.
• The entire analysis is statistical.
• Mostly important for the following industries – healthcare, marketing, retail, supply chain, entertainment, etc.

Data Science

• Study of data using methods derived from computer science – like algorithms, mathematics, and statistics – to find patterns and make future predictions.
• Works with both unstructured and structured data.
• Heavily relies on programming to create models which identify patterns and derive insights.
• 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 – e-commerce, manufacturing, academics, ML/AI, fintech, etc.

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.

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