Let’s quickly cover the following topics:
Business Analytics vs Data Analytics: Overview
Just like any other team in an organization, a Business Analyst and a Data Analyst have the same end goal, i.e., to improve the performance of a business. While every team will have its own modus operandi and tools to fulfill its objectives, in the same way, Business Analysts and Data Analysts use technology and data to optimize business performance. We will take a deeper look into ‘what is the difference between Data Analytics and Business Analytics?’ in this blog. But, first, let’s get you familiar with both these technologies.
Introduction to Data Analytics
Data Analytics involves the collection and examination of raw data of a business. This raw data can be logistics, market research, sales figures, equipment performance, and transactional data—anything with the capacity to generate information can become your source of raw data. This data is then sorted to make sense and draw insights into the performance of the organization. While working with datasets, one has to remember trends, opportunities, and risks. A Data Analyst’s role is to look out for patterns in the datasets that are representative of these key terms. These insights then translate into decisions and modifications in operations for maximizing the performance of the business.
Check out this video on Difference Between Business Analyst And Data Analyst
Data Analytics typically include:
- Data mining: Sorting through datasets to identify trends, patterns, and relationships
- Predictive analytics: Aggregating and analyzing historical data for future actions
- Machine Learning: Automating processes and functions through statistical probability (or from experiences) rather than requiring explicit programming
- Text mining: Identifying patterns and sentiments in any text-based content
- Big Data Analytics: Implementing data mining, predictive analytics, and Machine Learning for Business Intelligence (BI)
Data Analytics has been hugely automated to speed up the process of sorting through a wide gamut of data. Powerful analytics platforms are now widely used as tools to quickly collect, sort, and visualize data for further analysis.
Popular Data Analytics Tools
- Google Search Operators
- Google Fusion Tables
- Tableau Public
In recent years, organizations are making the shift to cloud technology, where data can be stored and accessed more conveniently and quickly.
Introduction to Business Analytics
While Business Analytics involves the use of data much like Data Analytics, it focuses more on the application of statistical methods for the purpose of analysis. Statistics and probability are at the heart of Business Analytics. Big Data has definitely come to be considered a valuable asset today that drives business planning and strategies. Implementing Business Analytics harnesses these resources to yield optimum performances.
There are three main stages of Business Analytics:
- Descriptive analytics: Analyzes historical data to plan for the future
- Predictive analytics: Implements Machine Learning and statistics to predict future events
- Prescriptive analytics: Integrates mathematical models and business rules to offer all possible responses to different scenarios
These stages do not come, necessarily, in the above order. Some organizations may skip a step altogether. However, Business Analytics, as a whole, can benefit any department in an organization.
Here is a video on Business Analyst training for beginners:
Popular Business Analytics Tools
Some of the popular Business Analytics tools are:
- Apache Spark
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Business Analyst vs Data Analyst: Roles
A Business Analyst’s job entails coming up with business solutions to tackle current and future issues. Here is what they are responsible for:
- Defining business cases
- Identifying the requirements of a business
- Project management and development
- Confirming solutions
- Quality testing
- Reviewing work habits and interactions with colleagues
- Staying updated with technologies
Data Analysts spend their time in research and creating reports for insights. The findings are then presented to the respective teams. Their tasks include:
- Scrubbing data
- Creating and maintaining reports, including those for the internal departments and client-based ones
Business Analyst vs Data Analyst: Skills
A Business Analyst will typically have a degree in business administration, economics, finance, etc. and will have the following skills:
- Data research
- Mathematical and analytical expertise
- The identification of critical data
- Project management
- MS Excel, Word, and PowerPoint
- Strong communication skills
In the Business Analyst vs Data Analyst comparison, Data Analysts focus more on numbers. They generally have experience in computer programming, predictive analytics, modeling, etc., and they are skilled in:
- Data mining
- Machine Learning
- Data frameworks
- Agile development methodologies
- Emerging technologies
Business Analyst vs Data Analyst: Team Responsibilities
Business Analysts are responsibilities for:
- Working with large amounts of data
- Identifying areas of improvement
- Addressing business requirements
- Collaboration with internal teams and third parties
- Escalating and resolving issues
- Recommending solutions
- Analyzing data to assess emerging trends
Data Analysts are responsible for:
- Acquiring data from various sources
- Applying statistical techniques to get insights into the data
- Database management
- Improving statistical efficiency
- Identifying patterns or trends in datasets
- Filtering data
- Identifying and fixing issues in coding
- Improving processes and operations
- Collaborating with management for business requirements
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Business Analyst vs Data Analyst: Career
When deciding on Business Analyst vs Data Analyst for your career, you have to think about the following:
- Consider your educational and professional experience: If you have a business-oriented degree, you will most likely be suitable for Business Analytics. On the other hand, if you have a background in mathematics, programming, science, databases, modeling, or predictive analytics, you will do well as a Data Analyst.
- Review your interests: If you enjoy the corporate world and have an inclination toward problem-solving, you will find that Business Analytics is more up your alley. Strong communication skills will make your job easier. If you excel in number-driven problems and solutions involving statistics, programming, etc. and are greatly aware of the industry, then you will make a good Data Analyst.
- Analyze your potential career path: Although there are similarities between Business Analysts and Data Analysts, there is a difference in their career paths and financial compensation. Business Analysts have a lower starting salary as compared to Data Analysts as they don’t need such a deeper knowledge of programming.
On average, Business Analysts earn US$80,000 per year, according to Indeed. Senior Analysts, on the other hand, cap out around US$113,000 as per Glassdoor. You will need degrees or certifications to advance to analytics-driven opportunities.
Data Analysts have the potential to earn six-figure salaries despite the average salary being US$73,000 p.a. Senior Data Analysts earn up to US$119,000 annually, according to Glassdoor. There are more career path options for Data Analysts with advanced degrees, such as developer roles and Data Science roles.
Business Analyst vs Data Analyst: Comparison
Here is a side-by-side comparison between Business Analyst and Data Analyst:
This blog is an attempt to segregate the Business Analytics vs Data Analytics differences and give you the answer to the popular and frequently asked question—what is the difference between Data Analyst and Business Analyst? Both fields are in the data domain, yet each has a distinct set of roles and skills, along with different pay scales and career growth.
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