In the present scenario, numerous social media applications are being developed. People tend to use social media applications extensively where they share information, upload videos, images, and are involved in several other activities. This results in the rapid growth of data. This data is no more considered as an overload and is used by companies these days to stay ahead of the competition and achieving growth.

A question that arises here is why is big data so important?

To find answers to the aforementioned question, let us read through the meaning of big data, tools used for big data, the importance of big data, and real-time big data analytics to evaluate and store a vast amount of data.

What is big data?

According to Gartner, big data is high-volume, high-velocity, high-variety information assets, which demand innovative and cost-effective forms of information processing for decision making and enhanced insights.

Big data is a term that describes the large amount of data that is routinely flooded into a company, both structured and unstructured. But it's not the amount of data that matters. What matters is how the company handles data. One can analyze big data to gain insights that lead to better decision-making and strategic business movements.

3 V’s of big data

To quantify what big data is, the IT industry has developed the three "Vs" of big data.

•          Volume: The amount of data is huge. According to Internet Live Stats, 2.3 trillion gigabytes of new data are being created daily.
•         Velocity: Data and processing speed (analyze streaming data to achieve near real-time results)
•         Variety: Different types of data, both structured and unstructured.

The concept of big data and big data automation has been around for years. Today, most organizations understand that by capturing all the data that goes into their business, they can apply big data analytics and make significant profits from it. If a company generates terabytes of data every month, they need to analyze that data to get the results they want, but regular tools can't handle such a large amount of data. With a simple tool, it takes a few days to get accurate results. For this reason, to process this data and obtain accurate results, they use big data tools such as:

•       Apache Storm
•         MongoDB
•         Cloudera
•         OpenRefine
•        Cassandra

Importance of big data

Big Data includes the use of analytical techniques such as machine learning, data mining, natural language processing, and statistics.

•        Big data allows performing multiple operations on a single platform. One can use several big data tools to store, preprocess, analyze, and visualize terabytes of data.
•          Multinational companies, as well as startups, are using these techniques in different ways today.
•          Big data analytics can help businesses process data efficiently and use it to identify new opportunities.
•          Various techniques and algorithms can be used to make predictions from the data. Multiple business strategies can be applied to the future success of the company, resulting in smarter business movements, more efficient operations, and higher profits.
• elow are three main reasons why big data is so important and efficient:
•          Cost reduction. Big data technologies such as cloud-based analytics and Hadoop tools offer significant cost benefits when storing large amounts of data.
•          Faster and better decisions. Combining the speed of in-memory analysis and Hadoop with the ability to analyze new data sources, companies hire big data analysts to analyze information instantly and make decisions based on what they have learned.
•          New products and services. With the ability to measure customer needs and satisfaction through analytics, businesses can deliver what their customers want.

Users of big data

The use of big data has grown tremendously in several industries. Some of them are:

Banking industry

Big data is highly used in the banking sector; big data tools are associated with the system. Bankers can perform multiple operations on transaction data. Tools such as Apache Hive make it very quick and easy for users to query data. Users can fine-tune the query engine to improve query performance

Retail industry

Big data provides retailers opportunities to increase their customer experiences.  They can use big data to anticipate customer demands and empower them in taking customer-centric decisions.

Manufacturing industry

Manufacturers use big data analytics to uncover patterns and recognize the latest information that allows them to boost supply chain efficiency, enhance processes and determine variables that affect production.