In the world we are living in, a data-driven world, many organizations left and right are looking for methods to extract insight on intense levels of data that most are just calling “data”. Data Science and Business Analytics have become two professions that are considered staples in the emergent data landscape. Both are very valuable in searching for decisions, strategy, and competitive advantage.
So the two questions are:
- Which is better?
- Which fits into your career or organisation?
Let us together make simple distinctions between Data Science and Business Analytics and de-complexify their differences, advantages, career roles and use cases to explore which one wins in the end depending on your context.
What is Data Science?
Data Science is an academic discipline that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
Data Science is an interdisciplinary field and draws from statistics and ML. Data science touches on most if not all of the computer science disciplines, and certainly data engineering.
Basic Fundamentals of Data Science
- Machine Learning & AI
- Big Data
- Predictive Modeling
- Data Engineering
- Data Visualisation
A data scientist is:
- Building predictive models
- Creating different types of automation
- Accessing large and complex datasets
- Asking a future question: “What will likely happen?”
- Associated with this question: “Why is it happening?”
What is Business Analytics?
Business analytics (BA) occurs when an individual uses data to answer a business question. BA is primarily used with historical data and visualization to find trends, patterns, and actionable insights.
Core Components
- Descriptive and Diagnostic Analytics
- Visualization and Reporting
- Excel, SQL, Tableau/Power BI
- Business Intelligence
- Measurement of KPIs
A business analyst is:
- Focused a bit more on what happened and why it happened
- Aiming to get the business to operate more efficiently
- Creating a guide for executives
Key Differences: Data Science vs Business Analytics
| Criteria | Data Science | Business Analytics |
|---|---|---|
| Goal | Predictive analytics – Identify future outcomes and automate decision making | Improve the current state business processes and business decisions |
| Focus | Algorithms, automation, data products | Business strategy and performance |
| Tools Used | Python, R, Hadoop, Spark, TensorFlow | Excel, SQL, Tableau, Power BI |
| Skills Required | Coding, Math, ML, Data Wrangling | Data analysis, data visualization, business knowledge |
| Data Type | Structured + Unstructured (i.e. text, images) | Structured (i.e., spreadsheets, databases) |
| Output | Models, APIs, automated systems | Dashboards, reports, strategy documents |
Opportunities
Data Science Careers
- Data Scientist
- Machine Learning Engineer
- Data Engineer
- AI Specialist
- Quant Analyst
Salary Range: ₹10-25 LPA (India) / $100-150K (US) depending on the position and experience.
Business Analytics Careers
- Business Analyst
- Data Analyst
- BI Analyst
- Product Analyst
- Marketing Analyst
Salary Range: ₹5-12 LPA (India) / $70-110K (US)
Both career opportunities have good growth potential in their respective fields, but Data Science positions tend to have higher salaries because of the technical level of complexity and because these roles are in high demand across the emerging technology space such as AI and robotics.
Business Analytics or Data Science: Which is Better for Business?
It depends on what the business requires:
- If a business needs to predict consumer behaviour, build recommendation engines, or detect fraud in real-time, then Data Science is the better choice.
- If the business wants to analyze sales performance, create operational efficiencies in the supply chain, or make strategic management-related decisions based on the company’s current key performance indicator (KPI), then Business Analytics is preferred.
Analogy:
- Think of Business Analytics like the GPS that tells you where you are and recommends ways to streamline your route.
- Data Science is like an autonomous vehicle system that predicts traffic patterns and determines the optimal path for you.
Learning Curve / Education
Data Science
- Requires familiarity with coding (Python/R)
- Requires a good math/statistics background
- The courses are more technical and rigorous
- Best for IT, engineering, and math graduates
Business Analytics
- Can be done without coding skills (easier to get started)
- Business concepts and story-telling emphasis
- Suitable for commerce, economics, or management grads
Ultimately: If you came from a non-technical background, Business Analytics is an easier entry point.
Real World Use Cases
Data Science
- Netflix uses AI to recommend which Netflix show to watch next
- Amazon uses machine learning to optimize their supply chain logistics
- Banks: Analytics can reduce the number of acceptances and rejections of customers
Business Analytics
- A retail company analyzes customer traffic and sales to strategically place products in store
- HR teams gather employee performance and attrition data to create dashboards
- Finance teams look at previous spending habits for the purpose of budgeting
So, Who Wins?
The truth is – there are no winners.
Both Data Science and Business Analytics are equally important but in different contexts.
- If you’re someone who is curious, likes coding, and wants to work on something that sounds futuristic, Data Science will be your career.
- If you’re someone that likes to solve business problems, analyze trends, and advise companies on strategy, Business Analytics will be your career.
Both types of businesses will see the most success if they have the best of both worlds and leverage the two fields together. Business Analysts identify the problem, and Data Scientists offer intelligent solutions.
Closing Thoughts
In the increasingly digital economy, rather than “Data Science vs Business Analytics: which one wins?”, the more relevant question is:
“How can we win smarter using the two together?”
Choose your path based on what you are naturally good at and enjoy doing.
Remember:
- Data means nothing without interpretation
- Insight means nothing without execution
Both fields have amazing and lucrative futures whether you’re trying to change things from a technical or strategic point of view!