Big Data sparked a revolution in the IT sector and is now permeating virtually every industry. Different types of Big Data available today serve as a driving force behind the success of businesses and organizations around the globe. One way that Big Data is used in business is to help companies learn more about consumer preferences for their products and services.
Big data is not a difficult concept to grasp. Big Data, as the name suggests, focuses on massive amounts of data that are too complex to be managed with conventional computer programs. Big data volumes are growing exponentially every minute, posing new challenges for analysis.
Indeed, Big Data is all about sheer quantity. While the quantity of data is important, what really matters is what you do with it. For that reason, let’s take a closer look at the applications of Big Data, its types, and its classifications in modern business.
Profits from Big Data in Business
Big Data is crucial because it provides answers to many common issues that businesses face. Big Data, when applied, can gather all consumer information that can be used as a guide for producing goods or services that are tailored to the specific needs and preferences of those who use them. No longer concerned with market segmentation, Big Data is instead concerned with hard facts from the field.
Company can easily create effective marketing strategy and reach every consumer in accordance with market segments with the help of available data analysis. This is also unlikely to increase the market’s volatility.
Aside from these aforementioned benefits, Big Data can also aid businesses in identifying the root causes of failure and uncovering novel insights that can propel business growth. Big data can also be used to detect harmful behaviors and strategies that could affect a company’s stability.
Three types of big data
There are three distinct types of Big Data, each with their own unique set of purposes, structural characteristics, and methods of analysis. Read on for more details down below.
“Terstructed Data” refers to information that has already been stored in a structured manner. This information is typically compiled using a spreadsheet program like Microsoft Excel. The data is easily accessible and analyzed because it is derived from a variety of databases using a straightforward search algorithm.
Can also originate from other statistical data captured by the server, application, or while traversing the platform.
All of the information gathered during computer or internet use is typically contained in human-created databases. For instance, when a user clicks on an online advertisement or even just casually browses a particular e-commerce website, that action becomes information that a business can use to gain insight into that user’s likely purchasing habits. Other examples of structured data include sales data for a company, employee data, and customer information.
the data is not properly structured
This kind of information lacks structure and a transparent storage format. Therefore, reading and analyzing the data is not simple. This information typically has a large volume or data size. It requires preliminary manual processing in order to conduct any meaningful analysis. Unstructured data can originate from various places and consist of a variety of file types, including text, images, videos, and audio.
As an example, consider the number of likes, followers, comments, retweets, shares, images uploaded, and other digital activities that take place within a user’s account when they participate in social media. Examples of machine-generated unstructured data include satellite imagery, scientific data from a variety of experiments, and radar data captured using a wide range of technologies.
the Data Semi Terstruktur
Because a large portion of the data is only partially structured, the boundary between unstructured and structured data appears murky in this set of data. These data have yet to be classified, but they contain valuable information. Example: NoSQL documents because they contain query terms that can be used to quickly sort through data. Examples of files that can be imported into this data type include XML, JSON, and CSV.