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Characteristics of Big Data: Types & 5V’s

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31st Aug, 2022
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Characteristics of Big Data: Types & 5V’s

Introduction

The world around is changing rapidly, we live a data-driven age now. Data is everywhere, from your social media comments, posts, and likes to your order and purchase data on the e-commerce websites that you visit daily. Your search data is used by the search engines to enhance your search results. For large organizations, this data is in the form of customer data, sales figures, financial data, and much more.

You can imagine how much data is produced every second! Huge amounts of data are referred to as Big Data. 

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Let us start with the basics concepts of Big Data and further proceed to list out and discuss the characteristics of big data.

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Read: Big data career path

What is Big Data?

Big Data refers to the huge collections of data that are structured and unstructured. This data may be sourced from servers, customer profile information, order and purchase data, financial transactions, ledgers, search history, and employee records. In large companies, this data collection is continuously growing with time.

But the amount of data a company has is not important, but what it is doing with that data. Companies aim to analyze these huge collections of data properly to gain insights. The analysis helps them in understanding patterns in the data that eventually lead to better business decisions.

All this helps in reducing time, efforts, and costs. But this humongous amount of data cannot be stored, processed, and studied using traditional methods of data analysis. Hence companies hire data analysts and data scientists who write programs and develop modern tools. Learn more about big data skills one needs to develop.

Characteristics of Big data with examples will help you understand the various characteristics properly. Many Big Data characteristics have been discussed below precisely:

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Types of Big Data

Big Data is present in three basic forms. They are – 

1. Structured data

As the name suggests, this kind of data is structured and is well-defined. It has a consistent order that can be easily understood by a computer or a human. This data can be stored, analyzed, and processed using a fixed format. Usually, this kind of data has its own data model.

You will find this kind of data in databases, where it is neatly stored in columns and rows. Two sources of structured data are:

  • Machine-generated data – This data is produced by machines such as sensors, network servers, weblogs, GPS, etc. 
  • Human-generated data – This type of data is entered by the user in their system, such as personal details, passwords, documents, etc. A search made by the user, items browsed online, and games played are all human-generated information.

For example, a database consisting of all the details of employees of a company is a type of structured data set.

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2. Unstructured data

Any set of data that is not structured or well-defined is called unstructured data. This kind of data is unorganized and difficult to handle, understand and analyze. It does not follow a consistent format and may vary at different points of time. Most of the data you encounter comes under this category.

For example, unstructured data are your comments, tweets, shares, posts, and likes on social media. The videos you watch on YouTube and text messages you send via WhatsApp all pile up as a huge heap of unstructured data.

3. Semi-structured data

This kind of data is somewhat structured but not completely. This may seem to be unstructured at first and does not obey any formal structures of data models such as RDBMS. For example, NoSQL documents have keywords that are used to process the document.

CSV files are also considered semi-structured data.

After learning the basics and the characteristics of Big data with examples, now let us understand the features of Big Data.

Read: Why to Become a Big Data Developer?

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Characteristics of Big Data

The primary characteristics of Big Data are –

1. Volume

Volume refers to the huge amounts of data that is collected and generated every second in large organizations. This data is generated from different sources such as IoT devices, social media, videos, financial transactions, and customer logs.

Storing and processing this huge amount of data was a problem earlier. But now distributed systems such as Hadoop are used for organizing data collected from all these sources. The size of the data is crucial for understanding its value. Also, the volume is useful in determining whether a collection of data is Big Data or not.

Data volume can vary. For example, a text file is a few kilobytes whereas a video file is a few megabytes. In fact, Facebook from Meta itself can produce an enormous proportion of data in a single day. Billions of messages, likes, and posts each day contribute to generating such huge data.

The global mobile traffic was tallied to be around 6.2 ExaBytes( 6.2 billion GB) per month in the year 2016.

Also read: Difference Between Big Data and Hadoop

2. Variety

Another one of the most important Big Data characteristics is its variety. It refers to the different sources of data and their nature. The sources of data have changed over the years. Earlier, it was only available in spreadsheets and databases. Nowadays, data is present in photos, audio files, videos, text files, and PDFs.

The variety of data is crucial for its storage and analysis

A variety of data can be classified into three distinct parts:

  1. Structured data
  2. Semi-Structured data
  3. Unstructured data

3. Velocity

This term refers to the speed at which the data is created or generated. This speed of data producing is also related to how fast this data is going to be processed. This is because only after analysis and processing, the data can meet the demands of the clients/users.

Massive amounts of data are produced from sensors, social media sites, and application logs – and all of it is continuous. If the data flow is not continuous, there is no point in investing time or effort on it.

As an example, per day, people generate more than 3.5 billion searches on Google.

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4. Value

Among the characteristics of Big Data, value is perhaps the most important. No matter how fast the data is produced or its amount, it has to be reliable and useful. Otherwise, the data is not good enough for processing or analysis. Research says that poor quality data can lead to almost a 20% loss in a company’s revenue. 

Data scientists first convert raw data into information. Then this data set is cleaned to retrieve the most useful data. Analysis and pattern identification is done on this data set. If the process is a success, the data can be considered to be valuable.

Knowledge Read: Big data jobs & Career planning

5. Veracity

This feature of Big Data is connected to the previous one. It defines the degree of trustworthiness of the data. As most of the data you encounter is unstructured, it is important to filter out the unnecessary information and use the rest for processing.

Read: Big data jobs and its career opportunities

Veracity is one of the characteristics of big data analytics that denotes data inconsistency as well as data uncertainty.

As an example, a huge amount of data can create much confusion on the other hand, when there is a fewer amount of data, that creates inadequate information.

Other than these five traits of big data in data science, there are a few more characteristics of big data analytics that have been discussed down below:

1. Volatility 

One of the big data characteristics is Volatility. Volatility means rapid change. And Big data is in continuous change. Like data collected from a particular source change within a span of a few days or so. This characteristic of Big Data hampers data homogenization. This process is also known as the variability of data.

2. Visualization 

Visualization is one more characteristic of big data analytics. Visualization is the method of representing that big data that has been generated in the form of graphs and charts. Big data professionals have to share their big data insights with non-technical audiences on a daily basis.

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Fundamental fragments of Big Data

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Let’s discuss the diverse traits of big data in data science a bit more in detail!

  • Ingestion- In this step, data is gathered as well as processed. The process further extends when data is collected in batches or streams, and thereafter it is cleansed and organized to be finally prepared.
  • Storage- After the collection of the required data, it is needed to be stored. Data is mainly stored in a data warehouse or data lake.
  • Analysis- In this process, big data is processed to abstract valuable insights. There are four types of big data analytics: prescriptive, descriptive, predictive, and diagnostic.
  • Consumption – This is the last stage of the big data process. The data insights are shared with non-technical audiences in the form of visualization or data storytelling.

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Conclusion

Big Data is the driving force behind major sectors such as business, marketing, sales, analytics, and research. It has changed the business strategies of customer-based and product-based companies worldwide. Thus, all the Big Data characteristics have to be given equal importance when it comes to analysis and decision-making. In this blog, we tried to list out and discuss the characteristics of big data, which, if grasped accurately, can fuel you to do wonders in the field of big data!

If you are interested to know more about Big Data, check out our Advanced Certificate Programme in Big Data from IIIT Bangalore.

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Rohit Sharma

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Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.
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Frequently Asked Questions (FAQs)

1Why can't we use standard data management tools for Big Data?

We know that massive, complicated, structured, and disorganized information produced and transported swiftly from various sources is referred to as Big Data. Numbers, text, video, images, audio, and text are only some of the sources and formats of Big Data. It is an extensive collection of valuable data that businesses and organizations have to manage, keep, access, and analyze. Managing these data on standard data tools is not possible as these tools are not designed to address this degree of complexity and volume. We must use Big Data software as these systems are designed to deal with large volumes of data arriving at high rates and in various formats.

2What is a CSV file?

A CSV or a Comma Separated Value file is a simple file containing a list of data that have been separated by using commas. Such files are used by different applications to frequently transfer data between apps. They are also known as Comma Delimited Files or Character Separated Values. They usually use commas to split or delimit data, although they sometimes use other characters like semicolons on occasion. It is based on the concept that you can export complex data from one program to a CSV file. This CSV file can be then input into another application. CSV files are challenging to work with since they might have hundreds of lines, many items per line, or long strings of text.

3How are different industries making use of Big Data?

Various sectors have incorporated Big Data into their systems to enhance operations, provide better customer service, create targeted marketing campaigns, and participate in other activities that will raise revenue and profitability. Big Data has aided businesses in identifying consumer buying behaviors, providing targeted marketing to clients, and identifying new customer prospects. Big Data also helped transportation sector optimization technologies and gave companies user demand forecasting. It has also aided in monitoring health issues via wearable data and provides real-time route mapping for driverless cars. Big Data has also helped in the streamlining of media and the provision of predictive inventory ordering.

4Why can't we use standard data management tools for Big Data?

We know that massive, complicated, structured, and disorganized information produced and transported swiftly from various sources is referred to as Big Data. Numbers, text, video, images, audio, and text are only some of the sources and formats of Big Data. It is an extensive collection of valuable data that businesses and organizations have to manage, keep, access, and analyze. Managing these data on standard data tools is not possible as these tools are not designed to address this degree of complexity and volume. We must use Big Data software as these systems are designed to deal with large volumes of data arriving at high rates and in various formats.

5What is a CSV file?

A CSV or a Comma Separated Value file is a simple file containing a list of data that have been separated by using commas. Such files are used by different applications to frequently transfer data between apps. They are also known as Comma Delimited Files or Character Separated Values. They usually use commas to split or delimit data, although they sometimes use other characters like semicolons on occasion. It is based on the concept that you can export complex data from one program to a CSV file. This CSV file can be then input into another application. CSV files are challenging to work with since they might have hundreds of lines, many items per line, or long strings of text.

6How are different industries making use of Big Data?

Various sectors have incorporated Big Data into their systems to enhance operations, provide better customer service, create targeted marketing campaigns, and participate in other activities that will raise revenue and profitability. Big Data has aided businesses in identifying consumer buying behaviors, providing targeted marketing to clients, and identifying new customer prospects. Big Data also helped transportation sector optimization technologies and gave companies user demand forecasting. It has also aided in monitoring health issues via wearable data and provides real-time route mapping for driverless cars. Big Data has also helped in the streamlining of media and the provision of predictive inventory ordering.

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