Business Analyst vs Data Analyst

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Business Analyst vs Data Analyst

Everywhere, big data is reshaping and powering decision-making. Data from a variety of sources is helping companies expand their reach, boost sales, run more efficiently, and introduce new products or services, from huge corporations to higher education and government agencies. Companies must employ business analytics and data analytics to make sense of all this data and become more competitive. In the dilemma of a Business Analyst vs Data Analyst, We’ll look at the aims of each function and compare duties and duties in this post to help you decide which path is best for you.

Business Analyst vs Data Analyst

Let’s look at the key differences between a Business Analyst and Data Analyst.

Differences Data Analyst Business Analyst
Objectives Overseeing the larger-scale business implications of data. Gathering and analyzing raw data in order to develop conclusions.
Education Bachelor’s Degree in Business Administration, Finance, or Accounting. Bachelor’s degree in Information Technology, Computer Science, Maths, or Statistics.
Career prospects Financial analyst, Healthcare analyst, Machine learning analyst, Social data analyst, Digital marketing analyst, Systems analyst. Business Analyst Manager,IT Business Analyst, Information security, Quantitative Analyst.
Salary 3.5 lakhs to 15 lakhs p.a. 5 lakhs p.a.

Business Analyst: An Overview

A business analyst will always be in charge of overseeing the larger-scale business implications of data. They use their study to predict long-term decisions, such as launching a new product line or prioritizing one commercial activity over another.

Business analytics can be classified into three types: descriptive, predictive, and prescriptive. These are frequently deployed in stages and, when combined, may answer or solve almost any query or problem that a business might have.

  • Descriptive Analytics provides an answer to the question, “What happened?” This sort of analytics examines previous data in order to gain insight into future planning. Executives and non-technical workers can profit from big data insights to improve business performance thanks to the broad availability of self-service data access and discovery tools and dashboards.
  • The next step on the road to insight is Predictive Analytics. It helps organizations predict the possibility of future events using machine learning and statistical methodologies. However, because predictive analytics is probabilistic, it cannot foresee the future; it can only recommend the most likely conclusion based on past events.
  • Prescriptive Analytics looks into possible actions based on descriptive and predictive analytical results. This sort of analytics mixes mathematical models and business principles to help people make better decisions by suggesting many options for different scenarios and tradeoffs.

Because business analytics necessitates large amounts of high-quality data, firms desiring correct results must first integrate and reconcile data from many systems, then decide which subsets of data to make available to the company.

Also Read: MBA in Business Analytics

Data Analyst: An Overview 

The process of gathering and analyzing raw data in order to develop conclusions about it is known as data analytics. Every company gathers vast amounts of data, whether it’s sales figures, market research, logistics, or transactional data. The ability to discover patterns in a dataset that may signal trends, risks, or opportunities is the true value of data analysis.

Businesses can use data analytics to make better decisions by modifying their processes depending on what they’ve learned. To speed up the analytical process, the most regularly utilized data analysis approaches have been automated. 

  • Data mining: It is the process of sifting through massive data sets in order to find trends, patterns, and connections.
  • Predictive analytics: It gathers and analyses historical data to assist firms in responding properly to future events such as customer behavior and equipment problems.
  • Machine learning: It is a technique for teaching computers to process data more quickly than traditional analytical methods.
  • Big Data Analytics: Data mining, predictive analytics, and machine learning methods are used in big data analytics to translate data into business intelligence.
  • Text mining: It is a technique for detecting patterns and attitudes in papers, emails, and other text-based materials.

As more companies migrate their mission-critical business apps to the cloud, they will be able to innovate more quickly with big data. Cloud technologies enable data analytics teams to store more data, access it more simply, and analyze it more easily, leading in a shorter time to value for new solutions.

Check out the blog on MBA in Data Analytics

Business Analyst vs Data Analyst: Objectives, Data, & Methodology

Objectives

Data Analyst Business Analyst
Recognize patterns in data and make accurate forecasts based on all past, current, and likely future events. Identifying trends in an organization to develop a single version of the truth that can be optimized and used to boost business performance.

Data

Data Analyst Business Analyst
Ad hoc work with data sources; as correlations are uncovered, more data sources are incorporated. Work with predetermined data sources depending on project goals as a business analyst.

Methodology

Data Analyst Business Analyst
Predictive and prescriptive analytics are used by data analysts. Establishes program or project objectives and requirements.

Which Career To Choose: Business Analyst vs Data Analyst

Educational Background

A business analyst has often earned an undergraduate degree in a business-related field. Because a business analyst uses business requirements and collaborates with the technical team to produce a business feature or package, this type of certification is perfect.

In contrast, a data analyst works with big data sets to forecast business outcomes. As a result, these individuals are typically STEM majors with a strong foundation in math, science, programming, databases, and predictive analytics.

Interest

When you’re trying to carve out a career path for yourself, this is critical to consider. Whatever role you choose, it should be in line with your passions. If numbers and statistics fascinate you, you’re more likely to excel as a data analyst. If you want to use data to solve critical business challenges, a career as a business analyst is the best choice.

A data analyst, on the other hand, is motivated by numbers. Math and statistics are their strong suits. These experts dig through data to get valuable insights from a variety of sources and complex data points.

Personal Choice

The most crucial factor in selecting the role you will play is your CHOICE. You may be a natural communicator, but your true passion is data visualization. Try to figure out where you want to be. Not only that but where do you see yourself in the following five years after starting either role?

Business Analyst vs Data Analyst: Additional Required Abilities

Business and data analysts, in addition to technical and role-specific talents, require some other qualities to be effective.

  • A business analyst must be able to do the following:
  • Take a big picture approach to a company problem or situation.
  • Collaborate with people across the organization to get the data needed to create change.
  • Create company and project plans, reports, and analyses that are straightforward and easy to understand.
  • At all levels of the organization, engage and interact with stakeholders.
  • Analytic problem solving
  • Creative thinking
  • Knowledge of chosen industry for research on data

A data analyst must be able to do the following:

  • Convert data into actionable business insights.
  • Identify and add relevant data sets on the fly.
  • Report your findings clearly and concisely.
  • As needed, create new data collecting and analysis methods.
  • Work Independently
  • Use of survey/query software and tools
  • Business intelligence and reporting
  • Data mining and visualization

Business Analyst vs Data Analyst: Salary Comparisons

It would be a mistake to argue that pay is unimportant. It is critical, but never at the expense of maximising your abilities. Both a business analyst and a data analyst can create incredible fortunes, provided they have the necessary talents and the desire to succeed.

Companies are increasingly recognizing the value of having an in-house business analyst, according to recent trends. As a result, the function of a business analyst is growing in terms of salary, with an anticipated growth rate of 19 percent over the next ten years.

In India, business analyst salaries range from around 3.5 lakhs to 15 lakhs per annum, with an average base of 7 lakhs per annum. Data Analysts, on the other hand, can earn an average of 5 lakhs per annum.

Which Career to Choose

Data analytics and business analytics goals are similar in that they both aim to optimize data to increase efficiency and solve problems, but there are some key differences. Reflect on the aforementioned points and think carefully. From the smallest startups to the largest multinational corporations, every company must use data to drive innovation and commercial growth. Whatever path you take, you’ll need to swiftly, efficiently, and securely obtain important, trustworthy data from a variety of sources.

In this blog, we discussed Business Analytics vs Data Analytics in detail. Are you planning to start a career in either Business Analytics or Data Analyst? Then, connect with us at Leverage Edu; our trained experts will help select the right course per your fit.

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