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News and Perspectives from IT Experts
July 20, 2021

Top 2021 Big Data Predictions

Here is all you need to know about graph analytics, in-memory processing, natural language processing, and other big data trends in 2021.

Big data is surrounded by various concepts and ideas on how to apply it. The demand for big data analytics has increased over the past years, with most of the big shot brands relying on it for effective training models and personalized solutions. It is predicted that by 2025, big data will reach 163 trillion gigabytes. Put in plain terms, that is equal to 2 billion years’ worth of music. With the speed at which big data is generated, it is evident that we are failing to keep up with it. However, big data technologies are getting more and more advanced, and the issue is expected to be mitigated in the next 3 years.

Big data is playing a significant role in many industries these days, with businesses adopting big data analytics to shape a more successful future. With big data, more opportunities are expected to be unveiled, which will have a positive impact on our daily activities. With that said, here are some of the top big data trends to watch out for in 2021.

Graph Analytics

When it comes to analytics, spreadsheets have always played a pivotal role. However, with so many activities happening in the business world and technology developing at a fast pace, companies are failing to find a balance due to data complexity surpassing the capabilities of a common spreadsheet. Therefore, graph analytics are expected to gain a lot of traction in 2021 as more businesses will be able to detect various data connections within a short period. With graph technologies, people, areas, periods, and items can be easily linked, thereby simplifying the process of getting market insights for companies.

In-Memory Processing

The costs of in-memory processing are decreasing with time, resulting in a significant increase in real-time environmental analytics. Real-time analytics demand fast CPUs and fast in-memory processing. Real-time environmental analytics gives companies the ability to instantaneously react to production and infrastructure alerts, activities concerning online sales and unforeseen changes in the financial markets. This has triggered the demand for in-memory processing to go higher as most businesses are looking to increase their productivity and stay ahead of the competition while mitigating risks.

Predictive Analytics

2020 witnessed an increased number of companies venturing into predictive analytics to understand current and historical events and come up with accurate predictions. 2021 is expected to see the same enthusiasm towards predictive analytics, with more companies diving into assessing future economic conditions, investment interests, and risks.  

Natural Language Processing

Voice-based apps have not excelled significantly over the years. Most of the challenges faced by their developers involve problems related to capturing intonations and accents to achieve more accurate recognition of language. In 2021, natural language processing is constantly improving, thus paving the way for developers to come up with much-improved language recognition and interpretation solutions. This is especially important in industries such as logistics and transportation, where employees need to work hands-free. This technology is also effective for executives and managers looking to obtain complex data through voice commands.

Life Cycle Development

IT departments can utilize analytics software to understand the nature of their traditional transactions. IT specialists develop life cycle management policies to conduct effective analysis and design robust disaster recovery systems.

IoT Analytics

Solution providers for the Internet of Things are increasingly furnishing their tools with analytics. This year, IoT analytics is expected to shift towards a more comprehensive approach. Through the year, we expect to see more effective steps being taken towards the amalgamation of IoT analytics channels. Input businesses are also venturing into an IoT grid that is integrated to closely reflect real enterprise functions.

Data Automation

It is estimated that in the US, “dirty data” costs about $3.1 trillion per annum, with scientists dedicating most of their time preparing and cleaning data. With so much time wasted on it, businesses are looking to embrace data automation, which will reduce the involvement of humans in these taxing operations. Data automation will contribute significantly towards making scientists more productive and bringing data to the market faster.

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