Data Analytics vs. Big Data vs. Data Science – A detailed comparison

In the digital world, the volume of unstructured data is rising every day. With this elephantine data, various avenues have been developed in the Big Data landscape, including Data Analytics and Data Science. Although people generally use the terms interchangeably, all of them perform varying but significant jobs. Also, there is a huge difference between these concepts.

To know the difference between these three jargon, let us first understand data. The collection of facts, as well as bits of information, is known as data. There are two types of data – structured and unstructured data.

• Structured data is the one that has a well-defined structure and order. Because it is well-defined and consistent, it is easy to store as well as access it.
• Unstructured data is the inconsistent type of data that does not have any format, sequence, and structure. It is error-prone when indexing is performed on it. This is why it is not easy to operate and understand unstructured data.

Data is amongst the biggest assets a company possesses in the current time. Consider bank payments. When you shop online, merchants collect your data related to your transactions, as well as of other shoppers, like –

•  Mode of payment
• Time of transaction
• Date of payment
• Amount
• Frequency

Then, the merchants analyze this data and make data products based on these parameters, which exclude customers’ personal information. These data products are then sold to banks that use this information to target potential customers by offering exciting offers to them based on these data products. As a result, they are able to increase their customer base.

Understanding Big Data

Big Data refers to a significant amount of data and deals with complex and large sets of data that conventional data processing systems cannot handle. Big Data has techniques and tools that extract structured, semi-structured, and unstructured data, store this data systematically, and then produce useful information from it. There are six characteristics of Big Data, which are mentioned below.

Variety – There is a wide range of data collected from various sources, which can be videos, images, audio files, unstructured data, or documents. Big Data tolls help in processing all types of data.

Volume – The volume of data generated each day from various sources is immense. Using Big Data tools, storing huge amounts of data has become easier.

Veracity – This relates to the quality of data collected. Businesses should take care of data quality while gathering it so that information is relevant.

Velocity – The number of users of the internet is growing aggressively in the digital era. Because of this, the velocity of data generation gets enhanced. Velocity here means how fast data generation along with its processing is occurring. Velocity is used to comprehend trends in data as well as meet market demands.

Variability – Market trends keep on changing, and how often they change implies variability. Big Data aids in managing it that benefits businesses to design and offer the latest products.

Value – The aim of Big Data is to collect data that is valuable for the business. This helps them increase their profits and compete in the market.

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