“Where there is a Data Smoke; there is a Business Fire” - Thomas Redman
Gone are the days when business leaders exceedingly relied upon their gut instincts and past experiences in taking business decisions. Decisions are increasingly being taken on the basis of hard core facts and data.
Big Data is largely being seen as the trump card which if efficiently utilized can result in unimaginable gains for business organizations. Consequently, professions associated with Data, is all set to soar to new heights in times to come.
If you too happen to be someone who is facing a professional dilemma and is not being able to choose between being a Business Analyst or a Data Analyst or a Data Scientist, this blog is for you. We do take up one of the highly debatable issues: Business Analyst vs. Data Analyst.
In this blog, we shall take up the issue of Data Analyst vs. Business Analyst. The issue will be sought to be understood through different lenses such as Business Analyst vs. Data Analyst: a Comparison of Skills, Roles and Responsibilities, Educational Background as well as Data Analyst vs. Business Analyst Salary. Consequently, we shall also try to investigate the topic of Business Analyst vs. Data Scientist.
Who are Business Analysts?
Business Analysts are professionals who are responsible for making use of data for undertaking critical business decisions. They even help in discovering innovative solutions to persistent business problems through business analytics.
They help in evaluating the efficiency of business expenses and processes, along with communicating the insights to stakeholders and other teams. They help to develop strategic business procedures and arrangements for driving business profitability.
Who are Data Analysts?
Data Analysts are qualified professionals responsible for acquiring data from different sources, cleaning and organizing it as well as conducting data analysis on it. They happen to sift through data in order to identify patterns and trends, thus converting valuable business information into actionable insights.
They make use of different Data Manipulation techniques in order to interpret complex datasets. In this capacity, they should be proficient in deriving meaning from numeric data, have a strong grip over programming languages, as well as be adept in the fundamentals of data handling. Data Analysts tend to operate on structured data for resolving quantifiable business problems.
Who are Data Scientists?
Data Scientists generally hold senior positions and are possessors of advanced degrees. They utilize more advanced data techniques and strategies like neural networks, clustering, decision trees and so on; for not only interpreting and analyzing data, but also for making predictions about the future in the form of predictive analytics.
They are highly proficient in Coding, Statistics, Mathematical Modeling as well as machine learning. Skilled with advanced programming, they are capable of creating new processes for Data Modeling within the field of Data Science.