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Understanding Big Data Analytics In Less Than 10 Minutes!

Analytics Have you ever wondered what exactly the difference between data and big data is? Both are collections of information, right? While that is true, a unique identifier for big data is its large volume. Big data is often used by businesses for obtaining customer insight. Such datasets are so extensive that traditional software like MS Excel is not agile enough to handle them anymore.


Not long ago, businesses were using traditional database methods to store quantitative information on spreadsheets like Excel; a program using grids, tables, and columns to organize the storage of data. Little did we know that the gradual inflow of data would accelerate at such a high speed that we would find ourselves in an explosion of information. It was around that time that we realized that the traditional data management systems were no longer capable of processing such massive volumes of information.

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The 3Vs That Define Big Data

Volume, Velocity, and Variety are the 3Vs that best define what big data stands for.


Whether or not a dataset is considered to be big data depends entirely on the volume of the information. Hence, this is the most important V defining big data. It is a simple rule: any information that is massive in volume is big data. For example, according to Fortunly, there are around 1.5 billion WhatsApp users in 180 countries! Just imagine the huge volume of data being generated on a daily basis. As traditional data management can no longer take the burden of this colossal amount of information, we rely on new methods (like Hadoop) to store and analyse the information.


In his recent letter to Amazon shareholders, Amazon CEO Jeff Bezos emphasized how “speed matters in business.” Velocity, which refers to the high speed at which data is received, helps businesses make the most timely decisions. Tesco, Sainsbury, and Marks & Spencer are some examples of retailers using big data analytics in retail and leisure industries.

If you were the owner of a retail business, your biggest concern would be to know which items go out of stock within minutes. For retail businesses, predictive analytics is practised on a large scale, which means they predict future consumer demand with present data available. Retailers can observe customer behaviour to predict what product should be stocked in future to meet the demand.


Big data always features different varieties of data. Social media alone has a diversified range of data, such as text messages, videos, audio files, pictures, and much more. When working with big data, it is important that you organise and structure the variety of information for later analysis.

Variety of data refers to two basic categories:

  • Structured Data: This can be easily defined and searched for by machine language. It is made up of quantitative information, e.g. name, address, telephone number, and billing information.
  • Unstructured Data:  It is unorganized in nature and is mostly text-based, e.g. emails, voicemail, ECG recordings, and business recordings.

Now that we know what big data is, it’s important to understand how big data and analytics integrate to extract value from the data. Before big data came into existence, businesses relied on basic data analytics, a traditional system to examine data for insights. With the cutting edge technology we have today, big data can be examined through big data analytics, a much faster way to draw out customer insights and behaviour patterns.

What Is Big Data Analytics?

A lot of people wonder what big data analytics is, and how it can be used to create value for their business. A simple definition of big data analytics is:

The process of examining big data for adopting appropriate business strategies and better decisions.

For example, if you were running an entertainment channel like YouTube, you would depend on big data and business analytics to answer some of the following questions:

  1. What kind of videos do viewers watch the most?
  2. How long is each video watched for?
  3. What are the viewer’s preferences?
  4. Which celebrity video is watched more?

Once we are able to obtain the answers to these questions, we can combine the powers of big data and analytics, and adopt a business strategy that would help lay out our marketing goals.

To further comprehend big data analytics, let’s take a look at its three major aspects:

  • Sources of big data analytics
  • Top 5 big data analytics tools
  • Innovative ways companies are using big data

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Sources Of Big Data Analytics

Big data can be split into three wide categories on the basis of the three primary sources that it is obtained from.

1. Social Data

Social media has completely turned our lives around. Every click, tweet, and comment that we make is stored and used for the purpose of big data analytics.

Are you still wondering how that happens? Well, social data gives an insight into user behavior patterns through various social media sites. Big data is obtained through the monitoring of how a user engages through different media channels, including Google, Facebook, LinkedIn, Instagram, and many others.

2. Machine-Generated/Internet-Of-Things (IoT) Data

Machine data is composed of all the digital information that we get from software installed in devices such as road cameras, smartwatches, smart meters, and satellites. Let’s take the smartwatch, as an example. Smartwatches track an individual’s health data by monitoring their blood pressure, seizures, and steps walked on a daily basis. This data can then be used by doctors who can offer their patients with better diagnosis owing to the easy access to their consistent medical record.

3. Transactional Data:

For the owner of a retail shop, transactional data is no less than a treasure chest. A large volume of data is available to them, including:

  • Purchases and returns made by the customers through payment slips
  • Information about the services that customers are subscribed to
  • Invoices for recording all the sales transactions and orders placed by the consumer
  • Consumers’ personal information gathered through loyalty cards programs for better customer relations.

As surprising as it may sound, even a small incentive, such as a loyalty card, has a big role to play in the collection of big data. Customers are given discounts in exchange for their personal information, helping businesses to determine the consumer preferences for future planning.

While all sources of big data are important, the most widely used method in the modern world is social media. Hence, it would be unfair to go ahead without shedding some light on this relationship.