The difference between Business Analysis and Data Science

Data Science vs Business Analytics, often used interchangeably, are very different domains. A layman would probably be least bothered with this interchangeability, but professionals need to use these terms correctly as the impact on the business is large and direct. In this article, we will elaborate on the difference between the two.
Simply put, Data science is the study of Data using statistics which provides key insights but not business changing decisions whereas Business Analytics is the analysis of data to make key business decisions for the company.

Data Science and Business Analytics are unique fields, with the biggest difference being the scope of the problems addressed. Simply put, The science of data that uses algorithms, statistics, and technology is known as Data Science. It provides actionable insights on a range of structured and unstructured data solving a broader perspective such as customer behaviour. 

On the other hand, the statistical study of mostly structured business data is known as Business Analytics. It provides solutions to specific business problems and roadblocks. 
These two terms are interchangeably used in either of the above scenarios, i.e., a business analytics problem could be wrongly addressed to be solved with the help of Data Science. The implications of carelessly using the term ‘Data Science’ in this context could be adverse because the tools and techniques used in Business Analytics are different than Data Science and using wrong tools to assess a data set will yield imperfect and undesirable results. 
Data Science is an umbrella term for all things dedicated to mining large data sets. An intersection of programming, statistics, and data analytics, Data Science is not limited to only statistical or algorithmic aspects. Business Analytics is the end-product of data science. It includes two broad categories, that are Statistical Analysis and Business Intelligence. 

“Business Analytics” and “Data Science” – these two terms are used interchangeably wherever I look. But there’s one indisputable fact – both industries are undergoing skyrocket growth.

Today, the current market size for business analytics is $67 Billion and for data science, $38 billion. The market size in 2025 is expected to reach $100 Billion and $140 billion respectively. This means we can expect a surge in demand for these two profiles very soon.

I have come across a lot of aspiring analytics professionals who want to choose “Business Analytics” or “Data Science” as their career, but they’re not even sure about the distinction between these two roles. Before diving into your own choice, you should be clear about which path you want to take, right? It could be a career-defining choice!

Here’s what I suggest. You can enroll in the free Introduction to Business Analytics course, where Kunal Jain, CEO, and founder of Analytics Vidhya, explains the difference between these two roles and also introduces a methodology to decide which path to choose (Business Analytics or Data Science) based on multiple factors like education, skills, and others.

1) Business Analyst vs. Data Scientist – A Simple Analogy
Let us take an example of an exciting electrical vehicle startup. This startup is now big for creating job families. And, they have decided to create three job families, one is a scientist, and the other two are an engineer and a management professional. Now I want you to take time and imagine what kind of role they play in the company.

We can infer their role from the general level of understanding:

Scientist – Work on complex, distinct problems such as finding a solution to build an efficient battery, or how to improve the design of the vehicle. While these problems may not give direct gain to the company but are crucial for advanced developments. And, in the future, these developments can help startups have non-linear (exponential) growth.
Engineer – Take these developments and apply industry techniques to transform them into production. For example, making an assembly line to manufacture these vehicles using the right machinery.
Management – Run the business and solve business-related problems on a day-to-day basis. For example, to find the right market to open a store for the vehicle. Decisions regarding the sales and marketing of these products and many others.
Now, let’s take these roles and convert it to data-based profiles.

Data-Based Functions:
Business Analytics Vs Data Science

Data Scientist –  He Works on complex and specific problems to bring non-linear growth to the company. For example, making a credit risk solution for the banking industry or use images of vehicles & assess the damage for an insurance company automatically.
Data Engineer – He would Implement the outcomes derived by the data scientist in production by using industry best practices. For example, Deploying the machine learning model built for credit risk modeling on  banking software.
Business Analyst – Run the business and take decisions on a day-to-day basis. He’ll be communicating with the IT side and the business side simultaneously.
This is a very basic analogy that you need to keep in mind to differentiate the role of Data Scientist, Business Analyst, and Data Engineer.

Caution: These terms are losely used in the industry. The exact role can depend on the maturity of your organization in data initiatives.

Now that we have our basic analogy clear, let us see the kinds of problem solved by data scientists and business analysts.

Types of problems solved by Business analyst and data scientist
To understand the difference between a business analyst and a data scientist, it is imperative to understand the problems or projects they work on. Let us take up an interesting example. Imagine that you are a manager of a bank and you decide to implement two important projects. You have a team of a data scientist and a business analyst. How will you do the project mapping job? Below are two problem statements:

Build a business plan to decide how many employees a bank needs to do XXX business in 2021
Build a model to predict which transaction is Fraudulent
Take your time to understand the problems. What do you think, which problem is best suited for which profile?

The first problem statement requires making several business assumptions and incorporating macro changes into the strategy. This will require more business expertise and decision making, this will be the job of a business analyst.

The second problem statement requires processing vast behavioral data from customers and understanding hidden patterns. For this, the professional should have a very good understanding of problem formulation and algorithms. A data scientist will be a suitable person to tackle this kind of specific and complex problem.

Business Intelligence
Another term often confused with Data Science is Business Intelligence. It is also an umbrella term that portrays ideas and strategies to improve decision making by utilizing fact-based support systems. Modern Business Intelligence is much beyond just business reporting. It is a mature framework that encompasses intuitive dashboards, mobile analytics, what-if planning, etc. It additionally incorporates enormous back-end machinery for maintaining control around reporting.
Although it sounds similar to Data Science, it is not. The principal difference lies in the type of problems that they address. Business Intelligence deduces the new unknown values of previously known elements using a formula that is already available. On the other hand, Data Science works with unknown scenarios without any formula or algorithm in hand, to solve data queries that nobody has ever answered in the past. Data Science problems are solved by exploring data, finding the best method, building a model around it, and finally operationalizing the model. 

Conclusion
Business Intelligence is well established with deep roots in a typical corporate landscape. Corporate professionals are familiar, comfortable, and confident with the BI concepts and framework. As BI projects work on known unknowns, the projects can be planned well in advance and timelines could be efficiently followed. Also, there is minimal trial and error with several successful BI projects in a company’s kitty, who would have developed good project expertise over the years. 
There is a massive career scope in the fields of Business Intelligence and Business Analytics. Professionals who are genuinely thinking of making a shift in the BA and Data Science roles can consider upskilling with the right course. Great Learning’s PG program in Data Science & Business Analytics and helps working professionals make a smooth and successful transition. The course offers the choice of online or classroom-based learning with Dual Certificate from University of Texas at Austin, McCombs School of Business (world rank #2 in Analytics), and Great Lakes (India rank #1 in Analytics). It helps you with hands-on practical learning with case studies and projects, without the need of quitting your job. The course is also tailor-made keeping in mind the professionals from the non-IT background. With our career guidance and support, you can easily land your dream job in Business Intelligence and Business Analytics.  

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