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What is Big Data? Types and Characteristics

Updated: Apr 14, 2023

Have you ever wondered how companies like Amazon, Google, and Facebook can predict your next purchase, search result, or friend suggestion? The answer lies in the power of big data. Big data refers to vast amounts of data that are too complex for traditional processing methods to handle. It encompasses a wide range of data types, including structured, semi-structured, and unstructured data. In this article, we'll explore what big data is, the different types of big data, and the characteristics that make it so powerful.

What is Big Data?

Big data refers to extremely large and complex data sets that are beyond the ability of traditional data processing tools and techniques to capture, store, manage, and analyze. Big data is transforming various industries and sectors, such as business, health care, education, government, science, and more

Examples Of Big Data

Here are some examples of big data and companies that have large amounts of data:

1. Social Media Data: This is the data generated by users on platforms such as Facebook, Twitter, Instagram, YouTube, and more. It can include text, images, videos, audio, and other types of content that users create and share online. Facebook had 2.91 petabytes of user data in 2022.

2. Machine Data: This is the data generated by sensors, devices, machines, and equipment that measure and record various physical phenomena. It can include data from satellites, radars, cameras, smart meters, wearables, vehicles, and more. According to Forbes, Google processes over 40 zettabytes of data per year.

3. Transactional Data: This is the data generated by transactions and interactions that occur between entities such as customers, businesses, governments, and others. It can include data from sales, purchases, payments, orders, deliveries, and more. According to Business Insider, Amazon had 1.5 petabytes of customer data in 2022

4. Scientific Research Data: This is the data generated by experiments and observations that are conducted by researchers in various fields such as physics, biology, chemistry, astronomy, and more. It can include data from instruments such as particle accelerators, telescopes, microscopes, and more. According to CERN, the Large Hadron Collider produces about 50 petabytes of data per year.

5. Digital library Data: This is the data generated by collections of digital documents and media that are stored and accessed online. It can include data from books, newspapers, magazines, archives, web pages, and more. According to Internet Archive, it had over 70 petabytes of web archive data in 2022.

Different Types Of Big Data

The following are the types of Big Data:

  1. Structured

  2. Semi-structured

  3. Unstructured

1. Structured

Structured data refers to data that is organized and formatted in a consistent and predefined manner, typically within a fixed field or set of fields in a record or database. Examples of structured data include birthdates, addresses, and other information that is easy to identify and categorize. Structured data is also commonly known as relational data and is stored in tables that are designed to maintain data integrity by creating a single record to represent an entity. Relationships between tables are enforced through the application of table constraints. The business value of structured data lies in its ability to be easily analyzed and utilized by organizations, leveraging existing systems and processes to drive insights and improve decision-making.


  1. Limited flexibility and scalability as it requires a pre-defined schema and a relational database to store and process.

  2. Any change in the data structure involves updating all the existing data, which can be time-consuming and resource-intensive.

  3. It can miss valuable insights that may be hidden in unstructured or semi-structured data sources.

2. Semi-structured

Semi-structured data is a type of data that is not restricted by a rigid schema for its storage and handling. Unlike relational data, it is not organized in a row-and-column format and is not limited to any specific structure. However, it does contain some features like key-value pairs that aid in distinguishing different entities from each other.

Due to the fact that semi-structured data does not require a structured query language, it is often referred to as NoSQL data. To exchange semi-structured data between systems with differing underlying infrastructures, a data serialization language is commonly used.

Semi-structured content is often utilized for storing metadata about business processes, but it can also include files containing machine instructions for computer programs. This type of data is usually sourced from external sources like social media platforms or other web-based data feeds.


  1. It is usually stored in formats such as XML, JSON, CSV, etc., which can be parsed and queried using special tools.

  2. Lack of standardization, the complexity of processing, and difficulty of integration with other types of data.

  3. Semi-structured data may not follow a common schema or syntax and may require transformation or conversion to be compatible with other data sources

3. Unstructured

Unstructured data refers to data that lacks a predefined structure or schema. It is often disorganized and irregular in nature. Examples of unstructured data include photos, videos, text documents, and log files. While metadata associated with such files may have some structure, the underlying data itself is typically unstructured. Unstructured data is also commonly referred to as "dark data" as it cannot be analyzed without the use of specialized software tools.


  1. High complexity and variability as it has no fixed format or structure and it is difficult to process and analyse.

  2. Requires advanced tools and techniques such as natural language processing and computer vision to extract meaningful information from it.

  3. It can be noisy, incomplete, inconsistent or inaccurate and may contain sensitive or personal information that needs to be protected.

Characteristics Of Big Data

Big data can be described by the following characteristics:

  • Volume

  • Variety

  • Velocity

  • Variability

Volume: This refers to the amount or size of data that is generated and stored. Big data typically involves large volumes of data that exceed the capacity of traditional data storage and processing systems.

Variety: This refers to the diversity or complexity of data types and formats. Big data can come from various sources and have different structures, such as structured, unstructured, or semi-structured data. Big data can also include text, images, videos, audio, and other types of content.

Velocity: This refers to the speed or frequency of data creation and collection. Big data can be generated and collected at a very high rate, sometimes in real time or near real time. Big data can also require fast processing and analysis to provide timely insights and actions.

Variability: This refers to the inconsistency or unpredictability of data quality and meaning. Big data can be affected by factors such as noise, ambiguity, incompleteness, or changes in context or semantics. Big data can also have different interpretations or implications depending on the situation or perspective.

Benefits of Big Data Processing

Here are some benefits of big data processing

  • Better customer insights by analyzing data from various sources, such as social media, transactions, web pages, and more. This can help optimize pricing, promotion, personalization, and customer satisfaction12

  • Increase market intelligence by analyzing data from competitors, suppliers, customers, and industry trends. This can help identify new opportunities, threats, and best practices23

  • Improve decision-making by using predictive analytics and machine learning to forecast outcomes and scenarios. This can help reduce risks, costs, and errors, and increase efficiency, quality, and innovation234

  • Enhance operational performance by analyzing data from sensors, devices, machines, and equipment that measure and record various physical phenomena. This can help monitor and optimize processes, resources, assets, and products123

  • Create new products and services by analyzing data from research and development, customer feedback, market demand, and more. This can help generate new ideas, test hypotheses, and launch innovations


Big data is a crucial component of today's digital world. Its characteristics - volume, velocity, variety, and veracity - define the challenges and opportunities of big data. By leveraging advanced technologies and analytics tools, organizations can gain valuable insights, make data-driven decisions, and drive growth and innovation. It's important for businesses to understand the types and characteristics of big data to harness its power and stay competitive in today's ever-evolving digital landscape.



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