Top three trends in big data that we would witness in 2022

The exponential growth of big data has been one of the greatest highlights of the 21st century. Data has grown at a rate like never before. Different types of social networking platforms, IOT devices, and sensors have led to the amplification of data waves that are difficult to handle. In order to handle these data waves, Big Data Analytics training of professionals is extremely important. This is because big data analytics involves new skills, processes, and techniques for data management.'

That said, the unstoppable growth in data volumes can be envisaged from a report by Statista that mentions the growth of data to reach 175 zettabytes in the next five years. Let us briefly analyze the various trends in big data that we would witness in the coming years.

Data migration to cloud environs

For those who consider that the migration of data operations to the cloud environment is unnecessary, it is important for them to consider the following figures. The number of unique mobile users has already crossed the five billion mark which is an increase of more than 60% in the last three years. The number of internet users has crossed the four billion mark which is an increase of more than 50% in the last three years. When we look at the number of active social media users, we find that this number has already crossed 3 billion which is an increase of more than 40% in the last three years. In addition to this, there is a proliferation of thousands of devices that are connected to each other in the IoT environment and are responsible for generating voluminous amounts of data.

Migration of data operations to the cloud environs would mean faster processing, less infrastructure as well as decreased latency in the computational processes. There are three important options that are available for utilizing the cloud environs. These include public cloud, private cloud, and hybrid cloud. The services include infrastructure as a service, platform as a service, software as a service, and even data as a service.

The influence of machine learning

It is important to understand the critical role that machine learning has to play in Big Data Analytics. The power of machine learning comes to the aid of big data in platforms like smart robotics, computer vision platforms, recommendation engines, virtual assistants, speech recognition, and natural language processing. Needless to mention, these systems generate a large amount of information and also rely on voluminous data for the purpose of training. With the help of machine learning techniques, analytical processes can be automated and different systems can be trained to carry out analysis on their own. They can be trained to detect various patterns in data. A simple application of this is the deep learning methodology that helps in the detection of new diseases or symptoms of a disease in a large population.

The role of data professionals

Given the voluminous increase in data, the demand for data professionals would increase in the times to come. Some of the most important professions related to big data analytics that would be charged with new roles and responsibilities include big data architects, data scientists, data analysts, AI engineers, AI developers, cyber security analysts, and business analysts. All these professionals would be expected to handle different types of data platforms and tools. They would need to be proficient in both programming language as well as machine learning algorithms. Some of the important roles that the above-mentioned professionals would carry out include data manipulation, data processing, data analytics, and data insights. In addition to this, they would also help in the fabrication of data pipelines and managing different types of ETL processes.

Reacting to the future demand of data scientists, companies are now investing in skill development labs within their organizations. These labs help in the skill-oriented training of professionals so that they can take the role of data scientists in the future. These labs also reskill other professionals like software developers so that they gain proficiency in artificial intelligence and machine learning systems.

The way ahead

We can't deny the fact that we would witness an exponential increase in the volumes of data in the coming time. However, what we also need to invest in apart from big data analytics is data security and privacy. The bridging of the security skill gap is extremely important for protecting our systems from cyber-attacks. The drafting of security standards in line with Global data protection rules can help organizations adopt and invest in the best intrusion detection systems. Thus, cybersecurity investment will help in protecting the sensitive and critical data of citizens in the times to come.

The use of neural networks has reinforced the scopes of machine learning. It has become almost common knowledge among people who have some interest in these matters that neural networks are designed to emulate the biological neurons.