Data is the new currency of the tech and business worlds. However, data is nothing in itself – it requires advanced technologies to be processed, analyzed, and interpreted to lead to actionable business insights. Since the data generated today is highly complex, varied, and massive, traditional data processing techniques no longer suffice.
This is where Data Science and its related technologies, like Business Analytics, come into the picture. Although both the terminologies – Data Science and Business Analytics – are often used interchangeably (since both deal with data), they are innately different.
Today’s post will highlight the key differences between these two fields dominating the industry, thereby hoping to offer some clarity to the Business Analyst vs. Data Scientist debate.
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Business Analytics vs. Data Science
To understand the difference between the Business Analyst and Data Scientist, you must first understand the domains of Business Analytics and Data Science.
What is Business Analytics?
Business Analytics (BA) refers to the iterative and systematic exploration of data with an exclusive focus on statistical analysis. It encompasses a host of statistical and analytical methods and technologies used for collecting, organizing, processing, analyzing, and interpreting business data to monitor the performance of a business in the past and design actionable business solutions for the present and future. Read the impact of MBA Business Analytics.
Three Kinds of Business Analytics
- Descriptive Analytics – This branch tracks the key performance indicators or KPIs of a business to understand its present state or performance.
- Predictive Analytics – It tracks and analyzes the latest data trends to evaluate future possibilities.
- Prescriptive Analytics – It draws on the past performance of a business to create data-driven recommendations as to how similar situations should be handled in the future.
What is Data Science?
Data Science is an interdisciplinary area of study that uses a combination of mathematics, statistics, computer science, information science, data analysis, Artificial Intelligence, and Machine Learning, to make sense of vast volumes of complex datasets. Data Science explicitly deals with Big Data that can be structured, semi-structured, and unstructured.
5 Stages of the Data Science Life Cycle
The Data Science life cycle comprises of five stages:
- Data acquisition
- Data maintenance
- Data processing
- Data analysis
- Data visualization
Now that you know what lies at the core of Business Analytics and Data Science, we can engage in a detailed discussion of the difference between Business Analyst and Data Scientist.
Business Analyst vs. Data Scientist
Business Analysts and Data Scientists have their unique roles and responsibilities in their niche domains. While they aim to promote business growth through data-driven decision making, their approach to data and solving business challenges is different. Read more about the job roles of business analyst.
A Business Analyst is a specialist of sorts who approaches and evaluates a business model just as a specialist doctor examines a patient. Business Analysts leverage different statistical analysis techniques like predictive analytics and exploratory analysis to understand the data at hand and predict the possible outcomes of business decisions.
They practically deal with the structured historical data of a business to understand how it performed over the years. Also, since Business Analysts deal specifically with business models, they must possess an in-depth understanding of various business models and their corresponding market aspects (demographics, location, competitors, etc.).
Data Scientists are different from Business Analysts in the sense that they are not focused on a particular field of business data. Unlike field experts (in this case, Business Analysts), Data Scientists have to analyze and interpret an organization’s data as a whole, including the current market trends as well. Data Scientists have to squeeze in the entire volume of data of a business into a mathematical/statistical model that will serve as the foundation for future predictions. Read more about the career scope of data scientists.
Below, we’ve highlighted the fundamental difference between Business Analyst and Data Scientist according to four core aspects:
Data Science is a broad umbrella that encompasses various other domains, including Artificial Intelligence, Machine Learning, Deep Learning, Data Analytics, and Business Analytics. It uses a combination of mathematics, statistics, computer science, information science, data analysis, and Machine Learning to uncover hidden patterns and insights from within large datasets. Data Scientists use those insights to influence business decision-making.
On the contrary, Business Analytics is more inclined towards statistical and quantitative measures for gaining insights from structured datasets. Business Analysts use a wide range of statistical and analytical methods to understand the performance of a business and promote fact-based management for decision making.
The responsibilities of a Business Analyst include:
- To create detailed business analysis, outlining problems, opportunities, and probable solutions for businesses.
- To quantify the scope of a business and communicate with the business departments, consumers, and all the stakeholders to draft a vision for the project at hand.
- To determine project requirements and assist businesses in implementing necessary technological solutions to meet those requirements.
- To discuss the project status, application requirements, and predicted growth of the business and to communicate any findings with the business/management team and stakeholders.
- To prepare detailed reports using graphs, charts, and other visualization tools.
The responsibilities of a Data Scientist include:
- To perform data mining and data pre-processing to clean and organize the data.
- To design and build predictive models that can deliver accurate forecasts of future events based on historical data.
- To improve and upgrade machine learning models and optimize their performance.
- To build automated anomaly detection systems and track the performance of the same.
- To develop processes, methods, and tools for data analysis and monitoring model performance without compromising on data accuracy.
- To analyze existing databases and simplify and enhance them to boost product development, marketing techniques, and business processes.
- To develop custom data models and ML algorithms.
Skill requirements of a Business Analyst –
- Strong foundation in mathematics and statistics.
- Extensive knowledge of systems engineering.
- Must possess excellent communication skills (both written ad verbal).
- Must possess technical, logical, analytical, and problem-solving skills.
Skill requirements of a Data Scientist –
- Extensive knowledge of mathematics, statistics, and probability concepts.
- Experience in data extraction, data wrangling, data transformation, data exploration, and data visualization.
- Experience in working with both ML and Deep Learning algorithms.
- Proficiency in coding (at least in two major programming languages).
Since Business Analysts explicitly deal with statistical concepts and approaches to gaining insights from data, they must be proficient in using tools like regression, classification, time series, clustering, and forecasting, among other things. Apart from statistical tools, Business Analysts must also be handy with data visualization tools like Google Docs, Google Sheets, MS Word, MS Excel, MS Office, Trello, Balsamiq, etc.
Data Scientists must be well-versed in multiple programming languages, including Java, Python, R, Scala, SQL, MySQL, and NoSQL. They must also know how to leverage various ML algorithms and work with Big Data tools like Spark, Hadoop, Flume, Pig, Hive, etc.
These are the four core points of difference Business Analyst and Data Scientist. Both job profiles are highly trending in the job market now, and both fetch high-end salary packages. However, Data Scientist leads with an average annual salary of $1,20,495 in the US, whereas the average salary of a Business Analyst in the US $76,109.
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Companies that are data-oriented, usually employ both Business Analysts and Data Scientists to ensure all-round growth of the business, and this is precisely the way to go. While Business Analyst can handle specific regions of business, Data Scientists can design actionable solutions to increase the overall productivity and business performance.
If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science and get a job on top firms.
How is a business analyst different from a data analyst?
Analyzing information to find patterns and insights that can then be utilized to make educated organizational choices is what data analytics is all about. Business analytics is concerned with evaluating various forms of data in order to create realistic, data-driven business choices and then putting those conclusions into action.
Is it necessary for me to learn data science in order to work in AI?
Artificial Intelligence (AI) is a collection of mathematical techniques that allow robots to comprehend and analyze the relationships between diverse data pieces. As a result, understanding data science principles and ideas in programming and mathematics is critical for AI engineers.
Why do businesses require business analysts?
Business analysis is used to identify and express the need for change in how firms function, as well as to assist organizations in putting that change into action. Business analysts (BAs) use data analytics to bridge the gap between IT and the business by analyzing processes, defining requirements, and delivering data-driven suggestions and reports to executives and stakeholders. Business analysts are valuable members of a team since they may help reduce project costs. Although it may appear that employing and paying a business analyst would cost more money up front, they can help to reduce the overall cost of the project they are working on in the long run.
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