Key Big Data Trends that Will Remain Biggest Challenges in 2021

When the COVID1-19 struck in 2020, a lot of companies were still relying on obsolete data analytics and business intelligence tools thinking it’s OK to work with them. But, as the pandemic lockdown extended from month to month, these organizations realized that it’s futile to keep working with traditional data analytics platforms that leverage only historical data and have a minimal scope of predictive intelligence.  In just over a year, 97 percent of the global business analytics and marketing intelligence firms have switched to Big Data analytics training to pivot existing Artificial Intelligence and Machine Learning techniques.

Why Big Data Analytics Training is Important?

No AI ML tool is useful without the Big Data analytics training to drive their success. These data and analytics training trends are helping thousands of global IT and software development companies meet the radical changes that businesses are looking to adopt to drive their digital transformation goals.

Here are the key trends [ I like calling them “ Mega Trends] in the big data industry that would help you hone your skills and acquire a job that pays well during the pandemic era.

The Rise of “X” Ops

The landscape of IT operations has changed dramatically in the last 5-6 years, especially with the maturity of Cloud computing, Edge computing, Device networking, and virtualization. IT companies that sold Platform as a service and Infrastructure as a service are now leaning toward selling experience and security as a service. AI and Automation have played a huge role in making IT Operations what it is today — all about “X” Ops — which means it spreads beyond the traditional definition of IT and networking.

 Big data analytics training has brought together the various aspects of data management and analytics into Operations management within enterprise resource planning, and these include:

• AI Ops
• ML Ops
• Data Ops
• Information Security Ops (InfoSec Ops)
• DevOps
• Open DevOps and so on.

Why Companies need more “X” Ops analysts and, engineers?

The rate at which companies are acquiring and spending data has hit the roof — and the trend is only looking upward with the global demand for AI ML and automation on the rise. For any company to meet these global demands for IT Ops modernization and cloud migration at agile levels, InfoSec analysts, DataOps engineers, and Big Data scientists would become irreplaceable in the modern context of IT Revolution 4.1.

360 Data Management Experience

Data vendors have switched their gears in the global enterprise market. We already know that data is depreciating at a much higher rate in the 2020s than what it was doing in the first decade of the century. The global explosion in data mining companies has forced IT, analysts, from around the world to leverage Big Data intelligence platforms to scrape through data lakes, looking for actionable insights through websites, user data, mobile apps, page traffic, and social media. The sources of Big Data have grown exponentially against experience management platforms that promise to deliver results that analysts are looking for.

Therefore, the certified Big Data analytics experts are predicting the rise of “Data as a Product” as the next step of Big Data trends.

What DaaP does?

DaaP can be leveraged by any business entity, standardized to their current and future needs. For example, the data that drives engineering projects could be diversified further depending on the resources allocated and the demands of the market. This is particularly true of designing a new smartphone device, or an IoT gadget for school-going kids. Based on a survey of customers using the devices and their behavior around the gadget, analysts can help the Product Management team create the most useful appliance in record time!

Future is all about Augmented Intelligence and Analytics!

We want machines to learn human language and simulate our behavior, even with unsupervised management. Machine Learning algorithms are constantly scrapped and rebuilt to enable machines with human intelligence. The biggest challenge to the whole AI ML game remains fixated on machines’ ability to make decisions when no input is provided by the human masters. While we have gained a certain level of mastery over Voice, Computer Vision, and NLP techniques to control and simulate robotic behavior, we are still a galaxy away from enabling them with “Augmented Intelligence”,  a cognitive science technique that can only be improved when we have high-speed computing processors that can work like neurons in the human brain.