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What is Big Data and Why is it Important?

What is Big Data and Why is it Important?

What is Big data- It refers to huge amount, difficult-to-manage data quantities structured, semi structured and unstructured that inundate organizations on a daily basis. But it’s not simply the type or quantity of data that matters; it’s also what businesses do with it. Big data can be analyzed for information that can help people make better decisions and feel more confident about strategic decision making.

It’s a data set that big and complicated that no traditional data management technologies can effectively store or process it. It is similar to regular data, except it is much larger.

Advertisements have always been focused towards various customer groups. Marketers have used TV and radio selections, survey results, and focus groups in the past to predict how consumers will react to advertising. These procedures were, at best, informed guesses.


Advertisers today buy or collect huge amounts of data to determine what people really click on, search for, and “like.” Select rates, views, and other exact indicators are used to assess the performance of marketing initiatives.

Amazon, for example, collects vast data stories on its millions of customers’ purchases, shipping options, and payment options. The corporation then sells highly targeted ad slots to very specific categories and subgroups.

Types of Big Data

what is big data

Structured data-

Structured data is any data that can be maintained, examined, and processed in a definite way. Over time, computer science skill has become more successful in inventing strategies for working with such material (when the format is fully understood in advance) and extracting value from it.

However, we are now expecting issues when the bulk of such data expands to enormous proportions, with average quantities reaching several zettabytes.

Semi structured Data-

Both types of data can be found in semi-structured data. Semi-structured data seems to be structured, but it is not specified by a table definition in a relational database management system. A data set contained in an XML file is a type of semi-structured data.

Unstructured Data-

Unstructured data is any data that has an unspecified shape or structure. Unstructured data, in addition to its large volume, presents a number of processing obstacles for extracting value from it. A heterogeneous data source including a mix of basic text files, photos, and videos is an example of unstructured data.

Organizations nowadays have a lot of data at their command, but they don’t know how to extract value from it because the data is in its raw form or unorganized format.

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Characteristics of Big Data-

what is big data


The word “Big Data” refers to a large volume of data. The size of data is extremely important in determining its value. Furthermore, whether or not a piece of data may be classified as Big Data is determined by its volume. As a result, while working with Big Data solutions, ‘Volume’ is an important factor to consider.


Variety refers to a wide range of data sources and types, both structured and unstructured. Most apps used to view spreadsheets and databases as their only data sources. Emails, images, videos, monitoring devices, PDFs, audio, and other types of data are now being incorporated in analytic programs. This wide range of unstructured data creates challenges for data storage, mining, and analysis.


Velocity means to the rate at which data is generated. The real potential in data is determined by how quickly it is collected and processed to satisfy needs.

The pace at which data pours in from sources such as business processes, application logs, networks, as well as social media sites, sensors, mobile devices, and others is referred to as Big Data Velocity. The data flow is vast and never-ending.


This refers to the inconsistency that data might display at times, obstructing the process of efficiently handling and managing the data.

Why Big Data Is Important

The importance of big data isn’t only determined by the amount of data available. How you use it determines its worth.

  • Make resource management more efficient
  • Boost operational effectiveness
  • Make product development more efficient
  • Provide new income and expansion opportunities
  • Make sensible decisions possible.

When big data and high-performance analytics are combined, you may do business-related activities such as:

  • In near-real time, determining the fundamental causes of failures, difficulties, and defects.
  • Anomalies are detected faster and more correctly than the human eye.
  • Improving patient outcomes by transforming medical picture data into insights as quickly as possible.
  • In minutes, recalculate complete risk portfolios.
  • Increasing the capacity of deep learning models to effectively detect and respond to changing factors.
  • Detecting fraudulent activity before it has an impact on your company.

Work Of Big Data

Businesses must evaluate how big data travels among a variety of locations, sources, systems, owners, and users before putting it to use. To take control of this “big data network,” which contains traditional, structured data, unstructured and semi structured data, there are five significant steps to follow:

Five Steps to manage Big Data :-

what is big data

Set a big data strategy

Its strategy is a plan that helps you manage and enhance the way you acquire, store, manage, share, and use data both inside and outside your company. In the face of an excess of data, a big data approach sets the scene for corporate success. It’s critical to think about current – and future – business and technological goals and efforts when formulating a strategy. Big data, rather than being a consequence of applications, should be treated as any other significant corporate asset.

Identify big data sources

  • Wearables, smart automobiles, medical gadgets, industrial equipment, and other linked devices send streaming data to IT systems via the Internet of Things (IoT). You can examine this huge data as it comes in, determining which data to preserve and which to discard, as well as which data requires additional investigation.
  • Interactions on Facebook, YouTube, Instagram, and other social media platforms generate social media data. This includes massive volumes of big data in form of pictures, videos, audio, text, and sound – all of which may be beneficial for marketing, sales, and customer service. Because this material is frequently in unstructured or semi structured formats, it presents a distinct consumption and analytical problem.
  • Massive quantities of open sources of data.
  • Data lakes, cloud data sources, vendors, and customers may all provide more big data.

Access, manage and store the data

Modern computer systems provide the speed, power, and flexibility required to retrieve large volumes and types of big data fast. Companies require ways for integrating data, constructing data pipelines, assuring data quality, providing data governance and storage, and preparing data for analysis in addition to dependable access. Some big data may be kept on-site in a typical data warehouse, but cloud solutions, data lakes, data pipelines, and Hadoop are also versatile, low-cost choices for storing and processing large data.

Analyze the data

Organizations may leverage all of their big data for analysis using high-performance technologies like grid computing or in-memory analytics. Another strategy is to identify which data is useful before examining it. Big data analytics, in any case, is how businesses extract value and insights from data. Big data is increasingly being used to fuel sophisticated analytics projects like artificial intelligence (AI) as well as machine learning.

Make intelligent, data-driven decisions

Well-managed, reliable data leads to reliable analytics and judgments. To stay competitive, organizations must harness the full potential of big data and operate in a data-driven manner, relying on facts offered by big data rather than gut instinct to make choices. The advantages of being data-driven are obvious. Organizations that are data-driven perform better, have more predictable operations, and are more lucrative.

Advantage And disadvantages of big data-

Advantages of Big DataDisadvantages of Big Data
Innovative solutions are derived from big data analysis. Big data analysis aids in purchase behavior and targeting. It assists in the optimization of corporate activities.Its storage in traditional storage can be expensive.
It contributes to the advancement of science and research.A significant amount of large data is unstructured.
The availability of patient records enhances healthcare and public health.Privacy standards are violated by big data analysis.
Financial trading, sports, polling, security/law enforcement, and other applications benefit from it.It may be used to manipulate client information.
Anyone may use surveys to gain access to a wealth of information and provide answers to any question.It might increase social divisions.
Additions are made every second.In the near term, big data analysis is useless. To reap the benefits, it must be studied for a longer period of time.
One platform can hold an infinite amount of data.Big data analysis results might be confusing at times.


Big data analytics may help you make better and quicker decisions, model and forecast future events, and improve your business intelligence. Utilize open source software like Apache Hadoop, Apache Spark, as well as the Hadoop as cost-effective, versatile data processing and storage technologies built to manage the volume of data being created today as you construct your big data solution.

What is Big Data and Why is it Important?

Big data, especially from new data sources, is simply a term for larger, more complicated data collections. These data sets are so large that they just cannot be handled by conventional data processing tools. However, these enormous amounts of data may be leveraged to solve business issues that were previously impossible to solve.

What are the top 5 features of big data?

The 5 basic features of big data are velocity, volume, value, variety, and veracity. These traits are often known as the “5 V’s of big data.” Data scientists may get more value out of their data by understanding the five V’s, which also helps their company become more customer-focused.

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