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Dominating Trends in Big Data Analytics

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It is the intrinsic nature of technology to keep evolving and becoming better with time. In today’s age, every business relies on small or big data and valuable insights derived from it. Big data analytics is highly resourceful when it comes to understanding the target audience and their preferences and feedback for the products and services provided by businesses and service providers. Using this, brands and businesses can easily anticipate customer needs and help organizations achieve their goals while outshining in market competition and corroborate their mark with data-driven strategies. Currently, this industry is worth US$189 Billion, which is an expansion of US$20 Billion more than 2018, and is forecast to reach US$247 Billion by 2022.

Actionable Data:

2021 will see more emphasis on actionable data. This data signifies the missing connection between business prepositions and big data. While a business can invest in the high-end big data software and tools, data in itself is useless without analysis as it mostly exists in excessively mind-boggling, unstructured, multi-format, and voluminous form. So, businesses will focus on data analytics that shall help extract actionable data insights. This implies the insights mined can help make business decisions, improve organizational activities, and plan all the more big data use cases.

Accessible Data:

Big data is set to become much more accessible, and therefore much more useful. Today, many enterprises struggle with unifying all of the sources of data. While building data lakes and other flexible storage environments was a major priority in 2018, 2021 will see much of this critical data be housed in systems that are much more accessible by the tools that will use them (visualization, analysis, predictive modeling). This can open up limitless possibilities for every aspect of business operations to be purely data-driven, says Sam Underwood, VP of Business Strategy at Futurety, a data analytics and marketing agency.

Big Data in Climate Change:

Climate Change may not be a new topic for scientists, but leveraging big data to fight against it can be mainstream in 2021. In fact, it is believed that by leveraging big data by the researchers can help us in knowing about the current stage of carbon dioxide emissions and the remedies to the situation. Not only that, even data from meteorological research, earth sciences, ocean research, and even nuclear research facilities are stipulated to help us understand climate change and other primary environmental conditions related to the planet.

Database as a Service:

While, Data as a service uses cloud technology to give users and applications with on-demand access to information without relying on the location of the users or applications, companies may take a step further. Introducing Database as Service- merging big data analytics solutions to meet the fast-growing client needs using customer information. By integrating big data analytics solutions into their platforms, DBaaS providers will host and manage data and help enterprise clients better harness it.

Continuous Intelligence:

It is a system that integrates real-time analytics with business operations and recommends actions based on both historical and real-time data. It processes historical and current data to provide decision-making automation or decision-making support. Gartner predicts over 50% of the new business systems will be using continuous intelligence by 2022. Industries could use continuous intelligence to monitor and optimize scheduling decisions and also provide more effective customer support.

Cleaner Data:

One of the most significant issues right now for big data is the clutter and incorrect data. Due to the poor quality of data, companies had to face the problem of slower data retrieval processes, resulting in a massive loss of money. While ‘scrubbing’ through processed data is gaining relevance globally, it also costs huge time for data scientists. Hence, the coming years may witness automation of cleansing of data through the use of AI and machine learning.

Natural Language Processing:

Though NLP was initially popularized as a subset of Artificial Intelligence, it quickly evolved into expanding regular activities and business processes. From studying data to finding patterns and more. 2021 will see the Natural Language Processing for instantaneous information retrieval from big data repositories.  Not only NLP will help access to quality information; it can also prompt the system to give them the business-related insights that will be needed to move forward. NLP further gives businesses access to sentiment analysis. It will allow them to know how their customers feel about their brands at a much deeper level.

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