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Top Trends in Big Data Analytics That will Dominate in 2021

Blog: Think Data Analytics Blog

Summary: The enormous data collected by websites, mobile apps and other IoT devices together makes the ‘big data’. Let us understand how it is being currently used and is set to evolve for further enhancements in the year to come.

Big Data is one of the most futuristic technologies of our times that is enabling collection, storage and utilizations of user data to understand their behavior and develop predictive models that will effectively take autonomous decisions as per their outcomes.

And, herein we are not talking about a gigabyte or two of data; rather we are targeting data that is accumulated at the rate of 1.2 megabytes per second from every online subscriber (data that cannot be contained with traditional means of data storage).

Defining Big Data and its Usage

As the word ‘Big Data’ itself implies, it relates to eons of data that might be structured or unstructured and that can be used by businesses by varied means to understand their customers, their expectations and requirements as well as the developing market trends.

This data is also being utilized to develop predictive modeling strategies to help smart devices become smarter and customized as per user patterns.

In simple terms, under the purview of AI, Big data is utilized to develop the ‘intelligence’ of these smart gadgets.

Big Data now forms the centerpiece of various business strategies to compete, innovate and capture value as well as to tackle industry-wide competition.

The most important aspect of these functionalities is the fact how businesses manage its required Velocity (the speed with which this data is collected through billions of connected devices worldover), Volume (the way this enormous amount of data gets stored including solutions like clouds and Hadoop) and Variety (as the data might be unstructured or unstructured and collected in various formats like audio, images, videos, etc.)

However, the technology as well as the scope it entails can help businesses mitigate their work risks and help in enhancing their overall customer relationship management, overall work efficiency and evolve into a better customer-centric brand all together.

Visit here: Top Big Data Companies in USA

Trends that are set to be the norm of Big Data Developments in the coming times

Let us now try and analyze how big data will be transforming its ways and structures in order to be more viable and effective for businesses the world over:

1.  Inclusion of all aspects of Artificial Intelligence

As stated earlier, Big Data is a concept that comes under the purview of the much broader AI spectrum. But, one of the facts about AI is that it actually works simultaneously with Big data to grow smart systems.

Data herein, makes the concept of Big data more effective with better predictions, enhancing AI and AI in return helps data to transform into ways that it can be more effective (actionable data).

They together form a vicious circle of interdependence. Since, Smarter machines are the need of the hour for all businesses, we ought to see a lot of work and applicability of both these technologies in the coming times.

2.  More utilization of Data as a Service

Have you witnessed the embedded data of covid-19 death and patient count in the post-pandemic era on the various websites?

This is an example wherein, businesses tend to develop and offer data as a service to other businesses to embed and utilize within their work fabric.

Though some users may term it as being detrimental to user privacy and security concerns; the technology development has been helping businesses to move data easily from one platform to the other in a no-nuisance kind of presentation, without any vendor lock-ins or data accessibility, administration and collaboration issues. DaaS is thus touted to see its own share of glory in the times to come.

3.  Things tend to get faster with Quantum Computing

Technologies are evolving by the day and most of them require data as food. But, it is mainly the speed at which they can ingest and digest this ‘food’ that separates them in functionalities and efficiencies.

Do you know that Google has already developed a Quantum computing (wherein, decisions of yes or no are not taken by binary digits 1 and 0, rather with much faster qubits or quantum bits) based processor named Sycamore, that it claims to have solved a problem in 200 seconds, that another state-of-the-art supercomputer would take more than 10,000 years to resolve.

Machine Learning algorithms as of now, have been limited by slow computational speeds and prowess of classical computers.

Quantum computing is a novice development trend that herein tends to administer large data sets at much faster speeds to analyze data at a faster and efficient pace to identify patterns and anomalies in real time, making it much more effective for businesses worldwide.

Quantum computing can easily integrate data by running comparisons to quickly analyze and understand the relationship between two or more predictive models or the effectiveness of algorithms.

4.  Edge Computing for better problem solving

There are more than 30 billion connected devices out there with the numbers soon rallying to touch the 50 billion mark.

These IoT devices are the new normal of the world, and businesses are thus, on a lookout for ways to better utilize the enormous data that they tend to generate all the time.

Edge Computing is a new development framework in this regard, wherein processors are located closer to source or destination for data, rather than directly going to the clouds. As businesses become more possessive of the data they generate and the value it holds, the trend is sure to see much wider use and scope in the future.

5.  Hybrid Clouds

With the rise of cyber attacks and privacy and security issues of data within clouds, businesses are opting for usage of hybrid clouds.

This one-infrastructure cloud model enables utilization of one or more public clouds to work  in synchronisation with one or more private clouds, leading to a more comprehensive environment which in addition, also tends to reduce the average updation and app maintenance cost.

For this cloud topology development, an organization must have a private cloud to gain adaptability with the aspired public cloud.

Things in the pipeline

Big Data is still a technology in its  evolution stage. Though year 2020 has been witness to its widespread use, its applicability and scope are further set to improve with passage of time and  more technology developments. All above stated Big Data trends are sure to be an important part of them.

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Frequently  Asked Questions (FAQ’s)

Q1. What is Big  Data?

Ans. As the name suggests, ‘Big Data’ is a collection of complex data (available in unstructured, semi-structured or structured forms of data sets) which have the potential to provide insights to gadgets, that help them make actionable decisions.

Q2. What are the 4 V’s of Big Data?

Ans. The four Vs of Big Data are:

  1. Volume : It describes the amount of data
  2. Variety : It describes about the types of data involved
  3. Velocity :  It pertains to the ever increasing speed at which the data is growing and the speed at which it can be processed.
  4. Veracity :  It describes the degree of accuracy of data available

Q3. Why is big data important to businesses?

Ans. As on date, ‘data’ is the most powerful tool at a business’s disposal. Big Data transforms raw collected data into meaningful and actionable insights that can help shape business strategies or make smart decisions, both autonomously as well as manually.

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Author bio

Andrea Laura is a very creative writer and active contributor who loves to share informative news or updates on various topics and brings great information to her readers. Being writing as her hobby, Andrea has come out with many interesting topics and information that attracts readers to unravel her write-up. Her content is featured on many mainstream sites & blogs.

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The post Top Trends in Big Data Analytics That will Dominate in 2021 appeared first on ThinkDataAnalytics.

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