If you have not understood the Big Data till now then let us clear you the concepts in regards to Big Data. First, let’s start with the definition: “Datasets which are big and complex and are used by data processing software’s to calculate, process and change the data, either for data storage or data analysis is known as Big Data.” Now an older definition by Mr. Gartner gave back in 2001, “Big Data is data that contains greater variety arriving in increasing volumes and with ever-higher velocity. This is known as the three Vs”

Importance of Big Data

X amount of data can be stored in hard drives, can be stored on servers and can be processed, it is not the time, but the amount of work you need to do. Hence it is big data that reduce time, cost, and helps in new product development, and even in smart decision making. Big Data when combined with high–powered analytic software then you can help a number of business-related tasks such as. Causes of failures and defects, generating coupons in bulk, calculating risks of portfolios, detect frauds in an organization.

The Three Vs of Big Data

The three Vs of Big Data are Volume, Velocity, and Variety. Let us start straight away with the three Vs.

Volume

Mostly the big in the big data stands for volume. Here we always process a large number of volumes which are of low density, i.e. unstructured data. For example, these unstructured data can belong to Twitter data feeds, webpage, sensor-enabled equipment, self-driving cars, etc. All this information can be either in zettabytes or terabytes, hence this huge volume of data needs to be processed and is the main reason for the data to be called Big Data and is then proceed in big data standards.

Velocity

There are many materials which work in real-time and hence we require a speed with which we can store this big amount of data for every minute. The speed of disks is less than that of in-stream data. Hence if the velocity of data is high, and hence no time to process it to be structured then it is said to be big data.

Variety

In earlier days we used to have data bifurcated into different types of data types, but now all the data is scattered and hence cannot fit into a relational database. Hence unstructured and semi-structured data such as text files, audio, a video that requires additional preprocessing is the additional variety of data that fall with Big Data.

Big Data is a new capital where the new tech companies are heading. Data holds a large value for each company’s data. Big Data recently have been used in predictive maintenance, customer experience, fraud and compliance, machine learning, operational efficiency, drive innovation, and other fields.

There are many challenges that come with big data, we will surely be learning more about big data in the coming days.

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Better Algorithms with ML & BD

We have been using long algorithms and tough software which are making it difficult for us to make way for a better and optimized software. As more and more algorithms are going to make way with ML and BD we are going to save space, and optimize the program for better performance and efficiency. The point to learn both the new languages is to find the hidden pattern in data and use it to increase the efficiency of the machines to perform much better and with higher efficiency.

Better Learning

With both the BD and ML we are going to get better devices that can learn on their own. Learning of the devices are one thing, while on the other way it will become easier for the coder to write the codes, import the modules and finish the task of the machine to send it for testing where it will learn on its own and perform in the future.

With Big Data we are going to get better management of data in the future where acquiring the data will be much easy, currently we are in the basic stage of managing the data with the big data concepts, as and when we develop more concrete concepts it will be better for us to manage the data, and solve the algorithms much easily.

Personalization

Currently, with each and every code being written manually, it has been tough for us to write the codes manually for everyone. When these two are implemented in the machines we will have better personalization and even deeper personalization, as machines will be able to write the codes with the help of Machine Learning and Big Data. Even with these implemented just on the UI, we will have better recommendations and opinions from the backend making it better for the companies to sell their products.

We have known that after Big Data, Machine Learning was the next big step in the industry, and now we have both of them integrated with each other to a huge level. The next thing we are going to look forward apart from these are some new positions in the industry such as:

Chief Data Officer

Although we have a CEO in the industry now the Data officer will make it a better workflow, and look forward to a better way to store the data into the No –SQL databases. There will be new norm about the CDO being in the organization dealing with data, and this will boost the company’s performance about data storage and efficiency.

Data Scientists

This you might have seen in the recent times, but soon we are going to have most of the engineers migrate to being Data Scientists. They will be looking after the data and we will have professionals looking just after the data to find more and more logic being the data.

These are some of the predictions for the Big Data and Machine learning industry, if you are in the industry from quite a few whiles do let us know where the industry will head in the coming years in the comment section below.

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