What is big data?

The term “big data” refers to the massive amount of data available to organizations that — because of its volume and complexity — is not easily managed or analyzed by many business intelligence tools. Basics of big data include the volume of the data collected, the speed at which that data becomes available to an organization for analysis, and the complexity (or varieties) of that data.

How big is big data?

According to Forbes, there are 2.5 quintillion bytes of data created every day. Because big data is so big, new terminology is required to define the size of that data. Big data consists of petabytes (more than 1 million gigabytes) and exabytes (more than 1 billion gigabytes), as opposed to the gigabytes common for personal devices.

How can you access big data?

As big data emerged, so did computing models with the ability to store and manage it. Centralized or distributed computing systems provide access to big data. Centralized computing means the data is stored on a central computer and processed by computing platforms like BigQuery, Snowflake, Azure, or AWS.

Distributed computing means big data is stored and processed on different computers, which communicate over a network. A software framework like Hadoop makes it possible to store the data and run applications to process it.

There are benefits to using centralized computing and analyzing big data where it lives, rather than extracting it for analysis from a distributed system. Insights are accessible to every user in your company — and integrated into daily workflows — when big data is housed in one place and analyzed by one platform.

Characteristics of big data

Big data is different from typical data assets because of its volume complexity and need for advanced business intelligence tools to process and analyze it. The attributes that define big data are volume, variety, velocity, and variability (commonly referred to as the four v’s).


The key characteristic of big data is its scale — the volume of data that is available for collection by your enterprise from a variety of devices and sources.


Variety refers to the formats that data comes in, such as email messages, audio files, videos, sensor data, and more. Classifications of big data variety include structured, semi-structured, and unstructured data.

  • Structured data usually refers to data that adheres to a defined structure or model, which makes it easier to analyze. Examples of structured data can include spreadsheets or a list of customer addresses.
  • Semi-structured data does not conform to a defined data model, but the data have semantic tags that make data organization and search easier. An example of semi-structured data is HTML code.
  • Unstructured data is data that is not organized by a predefined model. Examples of unstructured data can include emails and satellite images.


Big data velocity refers to the speed at which large data sets are acquired, processed, and accessed.


Big data variability means the meaning of the data constantly changes. Therefore, before big data can be analyzed, the context and meaning of the data sets must be properly understood.

Examples and applications of big data

The varied and high-volume, high-velocity big data your enterprise manages is a vital asset, one that can drive enhanced decision-making for improved business outcomes. Harnessing big data through effective data analytics provides many competitive advantages. Applications of big data include:

Hyper-personalization in retail with big data

Insight from big data can help retail enterprises better understand their customers’ preferences and behaviors. With that understanding, a retailer can hyper-personalize marketing initiatives and shopping experiences that redefine the customer experience.

Streamlining process in finance with big data

Applications of big data can help firms make the most of their financial data to improve operational efficiencies by streamlining the time and processes to actionable insights. This streamlining minimizes bottlenecks and allows more time for identifying new revenue opportunities.

Scaling expansion with big data

There are insights hidden in big data. Those insights help companies enhance performance, boost competitiveness, and effectively adjust the business model for successful expansion into new markets.

Why is big data important?

Data can be a company’s most valuable asset. Using big data to reveal insights can help understand the areas that affect your business — from market conditions and customer purchasing behaviors to your business processes. These understandings help drive impactful decision-making.

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