Data and Business Analytics. These are a few buzzwords we often hear and assume are the same. Though there is an overlap between these two concepts, they are entirely different. Understanding the differences between these two can help you understand what is right for your business.
Data analysis is the process of collecting, examining, and analyzing raw data to identify patterns and make predictions based on various events. Data analysts analyze and clean data and apply statistical techniques to create mathematical models. A solid background in statistics, programming, and computing.
The most used data analysis techniques include data mining, machine learning, and big data analysis. Predictive analytics is also a technique used in business analytics and will be discussed in the next section.
Regression Analysis is a technique used to understand if there is a correlation between a dependent variable and other independent variables. They determine the relationship between the set of variables but do not tell about the cause and effect.
Sentiment Analysis is a technique where textual data is analyzed to interpret emotions. This qualitative data helps businesses understand customers' feelings about their products or service. The three main Sentiment analysis types include Fine-Grained sentiment analysis, emotion detection, and aspect-based sentiment analysis.
Time Series Analysis is a statistical technique where seasonality and cyclic patterns are observed to identify trends. They measure a variable at different points in time to forecast future activity.
Business Analytics focuses on applying statistical analysis techniques to an Organization's data to boost the company's overall performance.
A Business Analyst uses several data sets and models from multiple areas to analyze and understand what is right for their business. They work on implementing and communicating results that the data analysts have derived from raw data.
There are four major types of Business Analytics techniques—Descriptive, Diagnostic, Predictive, and prescriptive.
Descriptive Analytics explains what has happened. Often known to be the simplest form of analytics, it mainly involves conclusions from past data that can help improve business performance.
Diagnostic Analytics explains why something has happened. It uses techniques like data discovery, correlations, and drill-down to determine the root cause of an event based on probability and the likelihood of an event.
Predictive Analytics helps predict or forecast a future outcome. It uses statistics and Machine learning algorithms to suggest the most probable outcome based on past events.
Prescriptive Analytics helps evaluate all the options based on various performance metrics and investigates all the favorable outcomes based on action. This is unquestionably among the most sophisticated methods that analytics have developed.
Though Business Analytics and Data Analytics are very different, it is essential to understand what is right for your business. Business Analytics helps make informed decisions concerning spending, sales, and marketing and improves efficiency in your organization. In contrast, Data Analytics helps you to personalize the customer experience, enhance security and mitigate risks. Often, organizations misunderstand the importance of both and use one instead of the other.
EverestDX aims to help your organization understand the difference between these two and choose the right Analytics for your organization. Talk to us now!