Defintion of Big Data, Defintion, Characteristics & Methods

Writer : Mis. Rossela Ilkan

When it comes to large amounts of data that cannot be analyzed using traditional methods, the term "big data" is used. An organization's raw material for running analytics and extracting insights that can help them craft better business strategies is often big data. It's more than just a byproduct of the technological processes and applications that have led to it. In today's world, data is the most valuable asset.

Traditional structured data, unstructured data, and semi-structured data are all examples of big data. The user-generated data on social media is an example of unstructured big data. Structured data, on the other hand, is more easily processed using specialized tools and techniques.

Today's informational deluge has produced a byproduct known as "big data." Every aspect of our lives, from how many steps we take to our financial histories, is a piece of data that contributes to the ever-growing mountain of big data.

In 2017, it was estimated that 3.8 billion people, or about 47% of the global population, were online. Over the past few years, the number and variety of smart electronic devices has exploded. According to the latest estimates, our daily output has grown to a staggering 2.5 quintillion bytes.

In light of the ever-increasing number of internet users, data never sleeps.

The following statistics provide some context for the enormity of the Big Data maelstrom. Every minute, something like this happens in the virtual world. The math is up to you.


  • Requests for weather forecasts total 18,055,555, according to weather broadcasters.
  • Users of Skype make 176,220 phone calls and post 49,380 pictures on Instagram.
  • In total, Netflix subscribers watch 97,222 hours of video.

Big Data's characteristics

It's universally agreed upon that the five V's of Big Data:

  1. Volume
  2. Velocity
  3. Variety
  4. Veracity
  5. Value

1. Volume

The broadest base of a pyramid of big data is volume. With more than three million new pieces of data being collected every day in 2012, the volume that companies around the world are managing has risen dramatically. At Antonio de Nebrija University's MBA program, a professor estimates that this volume has doubled every 40 months since 2012.

2. Velocity

When we talk about "velocity," we're talking about the rate at which data is generated.

The velocity, not just the volume, of big data can be a valuable asset for organizations. A competitive advantage for businesses is to have data that is as close to real-time as possible if they want to get actionable and valuable insights from it.

In the case of a food delivery service, this could mean purchasing a Google Ads campaign 45 minutes into the start of a major sporting event, for example. The same information will be obsolete in a few hours.

Rapid data is driven by RFID tags, smart meters, and a wide range of sensors.


3. Variety

Variety refers to a company's ability to obtain large amounts of data from a wide range of sources and in a wide range of formats. This includes smartphones, in-house devices, social media chatter, stock ticker data, and data from financial transactions. The source must be relevant to the type of business for which the data is collected. A retailer, for example, must pay attention to what customers are saying about its newly launched clothing line on social media. Following social media is of less importance to a manufacturing company.

Customers' profiles and personas can also benefit from a wide range of data. For example, knowing not only how many people open a newsletter, but also why they opened it and the distinctive characteristics of the audience, would be beneficial to a business.


4. Veracity

Using Veracity challenges the validity and reliability of data. The most reliable data is that which has been thoroughly vetted. It is impossible to have complete confidence in an organization's data unless it is linked, cleaned, and transformed across multiple systems. In order to maintain control over their data, they require hierarchies and multiple data linkages.

5. Value

It's at the top of the pyramid that value is found, the ability to extract useful business insights from the avalanche of information.

How many new members will join your website, how many insurance policies will be renewed by your customers, and so on are examples of value. Value comes from knowing who your best customers are and who will never return to your business.

The ability of companies to profit from the insights provided by big data is a key differentiator. In the process, they learn more about their customers and create more relevant products and services.


Big Data Types and Sources: A Comprehensive Guide

Streaming Data

This is the information that is generated by the Internet of Things and other connected devices. Flowing data is data that enters systems sequentially. It can come from a wide range of connected devices, including smartphones, wearables, smart cars, industrial equipment, and medical devices, all of which can be integrated into IT systems. On a first-in or continuous basis, streaming data can be analyzed to determine whether or not it should be stored for further analysis or discarded.

Information Obtained Through the Use of Social Media:

In addition to images, videos, GIFs, and voice comments (text), social media data includes the millions of daily interactions on social media platforms such as Facebook, Instagram, YouTube. Sales, support, and marketing campaigns benefit greatly from this. An additional processing step is required because most of the data is in unstructured or semi-structured form.

Data That Is Easily Accessible portals run by major governments around the world are just one example of the massive amount of open data available.

The rest of the big data comes from various sources, including the cloud, data lakes, vendors, suppliers, and clients.


Data Processing Methods for Big Data

The first step in processing big data is to devise a strategy for doing so. Identifying and cataloging the data's sources, locations, systems, users, and owners will be the next step. The final step in enabling data-driven decision making is to set up a system for storing and managing the data. For both structured and unstructured datasets, this protocol is a useful tool to use.

If you're going to manage your company's big data, you need to think about your company's long-term goals from both a business and technology perspective.

Data can be stored locally in a traditional data warehouse or in the cloud, which has grown in popularity in recent years as an alternative option. These are more cost-effective and offer some degree of customization. Today's computing systems have the speed, power, and agility needed to process such massive amounts of information at scale. Data integration, quality control, data governance, and preparing the data for analytical tools are also important parameters.


Methods for Getting the Most Out of Big Data

Artificial intelligence and other advanced analytics are powered by big data. To maximize its potential, companies must use their gathered information in the most efficient manner possible. An essential part of big data management is investing in software that can manage and analyze large volumes of data, particularly in real time.

With MapReduce, BigTable, and the Apache Hadoop framework: There are a number of ways in which big data storage and cloud-based analytics are used to improve the efficiency of business operations, such as Hadoop and Spark. Achieving cost savings can be achieved through the use of these tools.

In addition, the high speed of tools like Hadoop and in-memory analytics helps identify newer sources of data for analysis, such as untapped resources. When it comes to making quick decisions, the ability to quickly gather and analyze data is invaluable.

Complex problems necessitate innovative solutions. Platforms need to empower organizations with intuitive, simple interfaces that can be used by even the least tech-savvy. Accurate, real-time analytics can only come from using a platform that can access all of the data in the world. Systems that can handle large amounts of data and turn it into dashboards that provide actionable insights and workflow analytics are what distinguish successful ones from those that aren't.


Insights from Big Data Analysis.

  • With the help of big data analytics, manufacturers and suppliers can better understand current market conditions, customer buying habits, product popularity, and other factors.
  • Additionally, big data helps a business understand exactly what their customers like, which demographic they belong to, as well as how to reward and foster their loyalty in order to keep their customers for the long haul.
  • A company's long-term success hinges on its ability to satisfy its customers. Big data can help marketers manage expectations and create memorable and effective marketing campaigns for a variety of customer personas.
  • As a sentiment meter, big data analytics can also be used to measure consumer sentiment towards your brand, service or product. In terms of brand management, this can be a huge help. Online visibility and popularity can be improved with the help of big data insights.
  • With the help of big data analytics, companies can stay one step ahead of the competition by constantly redeveloping and innovating their products. Using them, you can find the root cause of any issues, failures, or defects that occur.
  • Analyzing massive amounts of data allows us to spot patterns in fraudulent activity early on, preventing more serious consequences.

Benefits of Big Data Processing in the Long Term


Investing in the infrastructure required to process large amounts of data will pay dividends in the following ways:

  • Planning inventory and maximizing resources
  • A more efficient approach to managing assets
  • Improved customer, vendor, and supplier relationships as a result of a more intuitive understanding of customer profiles.
  • Their entire supply chain is better integrated
  • Improved strategic planning efficiency
  • Greater responsiveness in dealing with supply chain issues
  • Faster response times and better customer service


Examples of how Big Data is affecting various industries:

Using Big Data in Education

It's no secret that big data has had an enormous impact on the education sector.


  • Aiming to create more personalized, dynamic and interactive training and development programs
  • Expanding or contracting a course's content
  • Increasing the precision of grading systems through systemic revisions
  • Prediction of future employment and guidance in pursuing it


Using Big Data in the Insurance Industry

Individuals seeking life insurance, as well as businesses of all shapes and sizes, can all benefit from the insurance industry. That both individuals and organizations are susceptible to adversity, catastrophe, and other unpredictability. There are many different formats and sources of information that make up the insurance data, so it is always changing.

The insurance company can gather and run data on driving conditions and road safety in the country in question and adjust the premium accordingly if a customer is interested in purchasing car insurance while traveling there. A person's driving safety record can also be gathered and factored into the policy that is presented to him or her for consideration.

Insurance companies can also use big data for threat mapping in addition to risk assessment. As a result, they are able to take into account a wide range of scenarios in which a customer or company might file a claim.


Big Data in the Public Sector

Governments all over the world have found big data to be particularly useful. It is essential for addressing complex issues, ensuring governance, and influencing major events on a national and global scale.

There is a new opportunity to gather and collate accumulated data, extract useful insights and contextualize it for various organizational processes thanks to the rise of big data.



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