Big data refers to data that is so large, fast, or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around for a long time. But the concept of big data gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three V’s:


Organizations collect data from a variety of sources, including transactions, smart (IoT) devices, industrial equipment, videos, images, audio, social media, and more. In the past, storing all that data would have been too costly – but cheaper storage using data lakes, Hadoop, and the cloud have eased the burden.


With the growth in the Internet of Things, data streams into businesses at an unprecedented speed and must be handled in a timely manner. RFID tags, sensors, and smart meters are driving the need to deal with these torrents of data in near-real time.


Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, emails, videos, audio, stock ticker data, and financial transactions.

How Big Data Works

Big data can be collected from publicly shared comments on social networks and websites, voluntarily gathered from personal electronics and apps, through questionnaires, product purchases, and electronic check-ins. The presence of sensors and other inputs in smart devices allows for data to be gathered across a broad spectrum of situations and circumstances. Big data is most often stored in computer databases and is analyzed using software specifically designed to handle large, complex data sets. Many software-as-a-service (SaaS) companies specialize in managing this type of complex data.

Types Of Big Data

1. Structured

Any data that can be stored, accessed, and processed in the form of a fixed format is termed ‘structured’ data. Over the period of time, talent in computer science has achieved greater success in developing techniques for working with such kind of data (where the format is well known in advance) and also deriving value out of it. However, nowadays, we are foreseeing issues when the size of such data grows to a huge extent, typical sizes are in the range of multiple zettabytes. [Read about Quantum Computing]

2. Unstructured

Any data with an unknown form structure that is classified as unstructured data. In addition to the size being huge, unstructured data poses multiple challenges in terms of its processing. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos, etc. Nowadays, organizations have wealth of data available to them but unfortunately, they don’t know how to derive value from it since this data is in its raw form or unstructured format.

3. Semi-structured

Semi-structured data can contain both forms of data. An example of semi-structured data is data represented in an XML file.

Why Is Big Data Important?

The importance of big data doesn’t simply revolve around how much data you have. The value lies in how you use it. By taking data from any source and analyzing it, you can find answers that streamline resource management, improve operational efficiencies, optimize product development, drive new revenue and growth opportunities and enable smart decision making. When you combine big data with high-performance analytics, you can accomplish business-related tasks such as:

  • Determining root causes of failures, issues, and defects in near-real time.
  • Spotting anomalies faster and more accurately than the human eye.
  • Improving patient outcomes by rapidly converting medical image data into insights.
  • Recalculating entire risk portfolios in minutes.
  • Sharpening deep learning models’ ability to accurately classify and react to changing variables.
  • Detecting fraudulent behavior before it affects your organization.

Advantages and Disadvantages of Big Data

The increase in the amount of data available presents both opportunities and problems. In general, having more data on customers (and potential customers) should allow companies to better tailor products and marketing efforts in order to create the highest level of satisfaction and repeat business. Companies that collect a large amount of data are provided with the opportunity to conduct deeper and richer analyses for the benefit of all stakeholders.

With the amount of personal data available on individuals today, it is crucial that companies take steps to protect this data; a topic that has become a hot debate in today’s online world, particularly with the many data breaches companies have experienced in the last few years.

While better analysis is a positive thing, big data can also create an overload of information, reducing its usefulness. Companies must handle larger volumes of data and determine which data represents signals compared to noise. Deciding what makes the data relevant becomes a key factor.
Furthermore, the nature and format of the data can require special handling before it is acted upon. Structured data, consisting of numeric values, can be easily stored and sorted. Unstructured data, such as emails, videos, and text documents, may require more sophisticated techniques to be applied before it becomes useful.

The Future [and Present] of Big Data

For many years, business analysts and executives have had to turn to in-house data scientists when they needed to extract and analyze data. Things are very different in 2022, with services and tools that enable non-technical audiences to engage with data democratizing and decentralizing data.

We are seeing more emphasis on analytics engineering, with tools that focus on modeling data in a way that empowers end users to answer their own questions. Plus, there’s lots of talk about a more visual approach – modern business intelligence tools like Tableau, Mode, and Looker all talk about visual exploration, dashboards, and best practices on their websites. The movement to democratize data is well and truly underway. While many large companies are already edging closer to, if not already fully embracing, all of these trends, giving them an edge over their competitors, the future of big data analytics is no longer locked behind a wall of price barriers.

Data engineers and scientists are developing innovative ways to uncover insights hidden beneath the heap of data without requiring the budget of a Fortune 500. We’re going to see a lot more small and mid-size companies incorporating big data analytics into their business strategies. The future is bright for those who take action to understand and embrace it.

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