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.

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.

Applications of Business Analytics 

The application of business analytics is diversified in several fields: 

Finance

The financial sector will benefit greatly from business analytics. Business analytics can be implemented to great effect in a number of departments, including banking and investing, financial strategy, managing portfolios, budgeting, and forecasting. When developing a new product or monitoring an existing one to determine a future course of action, business analysts employ data mining tools and statistics on the financial data that is currently accessible.

Agriculture

Agriculture requires a significant amount of attention to focus on greater investment and growth. A business analyst can ensure timely crop supply, crop production, seed quality and quantity; their predictions can manage the impact of climate change, monsoon changes, rainwater storage, crop damage, fertilizer needs, wind direction, flood risk management, and other factors. 

Stock Market 

Business analysts boost the organization’s performance in the areas of business operations and revenue by evaluating market volatility and reporting influencing price or variation in stock trends. Many business analysts are employing machine learning algorithms and natural language processing techniques to forecast the rise or decline of stocks.

Fraud Detection

Business analytics is an effective tool for identifying and stopping fraud. By examining data trends and anomalies, organizations can spot suspicious activity, fraudulent transactions, and possible security breaches. To protect their business processes and financial resources, organizations can use analytics to build fraud detection models, track in-the-moment transactions, and place preventative measures in place.

Applications of Data Science

Certain applications of data science include the following: 

Financial Industry

Data science has a significant impact on the financial industry. Thus, to make strategic decisions for the organization, Financial Industries need to automate the potential of loss analysis. Additionally, Financial Industries employ data science analytics technologies to make future predictions. It enables businesses to forecast stock market movements and client lifetime value. 

Genetics and Genomics

Data Science applications offer enhanced levels of therapeutic customization using genetics and genomics research. By combining multiple types of data with genomic data using data science tools, disease research can gain a greater knowledge of the role that genes play in how certain diseases and treatments affect people. 

Transportation Industry

Data science is being used in logistics and transportation industries to enhance travel routes, reduce transportation costs, and increase operational efficiency. Organizations could enhance fleet management, supply chain visibility, and customer service via real-time tracking and delivery efficiency by evaluating data from GPS, sensors, and logistics systems.

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:

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1. Scope 

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. 

 2. Responsibilities

 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.

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 3. Skills 

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).

 4. Tools 

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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Difference Between Data Analyst and Business Analyst

Professionals that analyze data for patterns, indications, and insights that guide business decisions are known as data analysts. They play an important role in transforming unprocessed data into useful information. 

On the other hand, business analysts concentrate on comprehending business requirements, detecting issues, and coming up with solutions to improve procedures and promote organization expansion. 

Unlike data analysts, who are largely focused on data processing, analysis, and visualization, business analysts place a greater emphasis on understanding business demands, optimizing procedures, and promoting strategic goals.

When there is the question of data analyst vs. business analyst salary, data analysts earn up to $72,500 per year, whereas business analysts earn up to $78,500 per year. Depending on the company or organization, the region, and other variables, the salary may change.

Conclusion

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.

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