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March 29, 2024

Top Big Data Analytics Trends

Top Big Data Analytics Trends

Big Data has been one of the top growing areas of IT for a decade with the increased digitization and collection of data. Using predictive analytics, businesses leverage the collection by churning and interpreting the data to offer meaningful insights to help target customer needs.

For doing this the right way, the right kind of system that is capable of strategic analysis is the bigdata Analytics Tool. Let’s have a look at the 7 Big Data Analytics that is transforming the way we make choices, and how businesses are shifting their focus from direct sales to Analytics-driven sales.

Fast Growing IoT Networks

“The internet of things, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction”

The Internet of things in simple terms is having most aspects of life connected to the internet and being able to seamlessly monitor and command them to do things. IoT is procuring data from every device like appliances, smartphones, and smart devices. In simple terms, it connects non-internet physical devices such as appliances in our homes, automobiles, and other objects to the internet. To create an IoT system that collects and processes data at the same time, we need Big Data infrastructure that can make lightning-fast analysis and give insights to make decisions.

Did you know that the combined markets of the Internet of Things (IoT) will reach $520B by the year 2021?

Predictive Analytics

Have you ever wondered how stock market predictions, weather predictions are done? Predictive analytics allow us to look at historical data, build appropriate models, and understand patterns and predict future outcomes.

The most significant impact this has had in the recent decade is towards the healthcare industry. The simplest impact is by collecting data from smartphones and analyzing it to predict cardiovascular diseases in the US (where about 2000 people die every day). These will predict the small changes we can make in our lifestyle to mitigate these risks. In the future, we will see more adoptable insights like 10,000 step rule and drinking 8 glasses of water. This will change how we eat or move based on our birth and our lifestyle.

Dark Data

Data is collected in all forms and has to be structured to gain insights. But there remains some unstructured data that cannot be analyzed, and it is called Dark data. According to International Data Corporation (IDC), 90% of the unstructured data are never analyzed.

Most of our scientific discoveries across subjects (immunology, zoology, and environment, etc) have been from later analysis of historical data that couldn’t be analyzed appropriately due to inadequate technology.

For example, climate change predictions we currently see is from the historical data that we thought was fairly for collection purposes.

With an abundance of dark data and AI around the corner, we might just uncover the next technological invention!

Quantum Computing

Quantum computing takes advantage of the strange ability of subatomic particles to exist in more than one state at any time. Quantum computers can solve problems that are impossible or would take a traditional computer an impractical amount of time (a billion years) to solve. However, they are poised to solve different problems than to replace classical computers. Classical computers can best handle simpler tasks while Quantum the most complex.

Remember when IBM’s computer Deep Blue defeated chess champion, Garry Kasparov in 1997? It was able to gain a competitive advantage because it examined 200 million possible moves each second. A quantum machine would be able to calculate 1 trillion moves per second!

Being capable of handling multiple variables at a single time, this property will, if quantum computing will promote lightning speed, reduce electricity consumption by over 100-1000 times and above. Isn’t it the perfect recipe for Big Data?

Open Source

Since the development of new technologies, the faster way is to go with open-source software with an increased community connect. It is faster to use open source tools for analyzing bigdata for actionable insights.

Apache Hadoop is the most well-known system for bigdata analytics, but other components are required before a real analytics system can be put together.

Edge Computing

“Edge computing is the practice of processing data near the edge of your network, where the data is being generated, instead of in a centralized data-processing warehouse.”

Doing this computing closer to the edge of the network lets organizations analyze important data in real-time – a need for organizations across many industries (Majorly in appliances connected with IoT).

It’s for real-time processing that Edge AI makes the most sense. For example, with facial recognition and demographics, a retail store could customize a digital display to show an offer that’s likely to appeal to the person looking at it.

Sending the video stream to the cloud, processing it and then displaying the right offer is too time-consuming. Using Edge Computing, a local computer can decode a person’s demographic information and then display the appropriate offer in a fraction of the time.

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