Top 20 Latest Big Data Trends for 2021

Today’s technological world has opened doors for Big data to improve businesses across industries and boost economies. Big data helps organizations and businesses handle critical responsibilities and make the world a better place to do their jobs efficiently.

Big data is evolving at an incredibly fast rate; it has allowed even small businesses to get benefit from it.

Now let us explore the Top 20 Big Data trends in the technological world.

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1. Data Analysis Automation

Automation has improved the business and efficiency of many enterprises. It is not surprising that soon, around 40% of data-based tasks are automated.

Automation brings a higher pace of productivity and is profoundly favored in the digital world. Thus it is an exceptionally supported element in organizations and large enterprises as well. Automation will also help in effective observations and guide the company to proceed further with the right analytics.

2. Continuous Intelligence

“In the coming few years, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.”

Organizations generally have long sought real-time intelligence, and systems are available to do this for a limited set of tasks. Now it can be said that it is finally practical to implement these systems. Continuous intelligence supports the cloud, advances in streaming software, and growth data from sensors in the Internet of Things (IoT).

3. Internet of Things

“ In the coming few years, we can hope to see more than 20 billion active IoT (Internet of Things) devices ”

We will see a lot more analytics solutions for IoT gadgets to give valuable information and transparency. The Internet of Things (IoT) will be the new trending technology, which will generate more than $300 billion annually in the coming few years.

According to the latest industry trends and study, the global IoT market will grow at a CAGR of 28.5%.

4. CDOs in Demand

Nowadays, the Chief Data Officer (CDO) is in demand, but it is still a new concept for many organizations.

Organizations have slowly started to realize the need for COD. They started hiring a CDO for enterprise-wide data cleaning, analysis, visualization, and studying intelligent insights. The CDO profile has evolved a lot, and human resource personals are looking for professionals who can fill this trendy job role.

5. Edge Computing

Edge Computing is in the technological space steaming network performance for quite a while now. Using Edge Computing data can be handled and stored away from the silo setup closer to end-users. In this, the processing takes place either in the device itself or in the fog layer or in the edge data center.

6. Quantum Computing

Quantum Computing enables seamless data encryption, solving medical problems, weather prediction, real conversations, and better financial modeling. Proper financial modeling helps organizations to develop quantum computing components, algorithms, applications, and software tools on qubit cloud services.

“ Big tech firms like IBM, Google, Intel, compete against each other to work rigorously in a bid to build the first quantum computer.”

7. Predictive Analytics

Predictive Analytics offers customized insights that help organizations to generate new customer responses or purchases and promote cross-sell opportunities. It also helps technology to integrate into diverse domains. The domains are finance, automotive, retailing, aerospace, healthcare, manufacturing, and pharmaceuticals.

8. Dark Data

Dark data in the technology is the kind of digital information that is currently not in use for business analysis. In many cases organisations are not even aware that such data is being collected, which can be used for the purpose of deriving insights or for decision making.

Nowadays, data and analytics are becoming daily aspects of organizations. So, there is a need to understand that any data left unexplored is an opportunity lost, and we need to explore the options for better implementation of Dark Data.

9. Open Source

In the future, we will witness more free data and software tools becoming available on the cloud. Small organizations and start-ups are getting the most benefit from this data trend.

Open-source analytical languages like R, a GNU project associated with a statistical company and graphics has seen a considerable adoption credit to the open-source wave.

10. Deep Learning Gets Deeper

Organizations are planning to expand deep learning beyond their initial use cases like computer vision and natural language processing (NLP). According to a study by large financial institutions, it is found that neural network algorithms are better at spotting fraud than “traditional” machine learning approaches.

There is an increase in demand for processors like GPUs, which are used for training deep learning models. Thus there is a clear demand for faster training. However, instead of being demanding technology, deep learning is still not used by many organizations on generalized platforms.

11. Cloud in Demand

In the coming few years, many small businesses and start-ups will gravitate to the major public cloud providers. And cloud providers are investing significant sums in building ready-to-run big data platforms, analytical databases, machine learning algorithms, and real-time analytics.

More prominent companies also find it hard to resist cloud technology and will surely start adopting it frequently.

12. Graph Analytics

“ According to Gartner, the application of graph processing and graph databases will grow at 100% annually over the next few years.”

Graph analytics is defined as a set of analytic techniques that shows how entities such as places, people, and things are related to each other. Application of graph technology includes fraud detection, traffic route optimization, and social network analysis to genome research.

Generally, business users are asking complex questions across structured and unstructured data. And thus, graph analytics helps to accelerate data preparation and enable more complex and adaptive data science.

13. Commercial AI and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) have been the primary source of innovation in algorithms and development environments. AI and ML have their specific use in project and model management, transparency, and integration.

Also, the deployment of models in production will be accelerated by the increased use of commercial AI and ML. And this will also derive business value from these investments.

14. Data Fabric

A data fabric is a custom-made design that provides reusable data services, semantics tiers, pipelines, and APIs. All these are combined in a setup via a combination of data integration approaches in an organized manner.

It becomes easy to derive value from analytics investments depending on having an agile and trusted data fabric. It also enables frictionless access and sharing of data in a distributed data environment.

15. Blockchain in data and analytics

Firstly, blockchain provides the lineage of assets and transactions. Secondly, for complex networks of participants, it provides transparency. And blockchain is not a stand-alone data store. Realistically, the technology is not yet matured to real-world, production-level scalability for use cases beyond cryptocurrency.

16. Persistent Memory Servers

With the rapid growth in data volume, there is a need to increase memory size also. And it is not enough to use only database management systems (DBMS), which make use of in-memory database structures. There is a need for server workloads that have not only fast processor performance but also have massive memory and faster storage.

Thus, Persistent memory technology will help businesses in the extraction of more profitable and actionable insights from data. Nowadays, many DBMS vendors are experimenting with it. But it may take a few years to modify their software to take full advantage of persistent memory.

17. Explainable AI

Explainable AI can be defined as the set of capabilities that are used to describe and analyze a model. It highlights the model’s strengths and weaknesses, predicts its likely behavior, and identifies any potential biases. It also increases the transparency and trustworthiness of AI solutions and outcomes, which reduces regulatory and reputational risk.

Without acceptable explanation, auto-generated insights or “black-box” approaches to AI can cause concerns about regulation, reputation, accountability, and model bias.

18. Augmented Analytics

Augmented analytics uses advanced artificial intelligence and machine learning to automate the process of gaining business insights. The organization’s data can be cleaned and analyzed automatically through an augmented analytics engine. In the last step, it converts the insights into actionable steps with little supervision from a tech person.

In the coming few years, augmented analytics will become the primary purchase of businesses dealing with analytics and business intelligence.

“Augmented analytics makes analytics available to smaller businesses by making it more user-friendly.”

19. In-Memory Computing

In-memory computing allows the storage of data inside the random-access memory (RAM) of specific dedicated servers. It helps in detecting patterns quickly and analyzing massive amounts of data easily, which has the added benefit in helping customers, retailers, and utilities.

The primary factor for the growing interest in In-Memory Computing technology is the dropping prices of memory.

20. Smarter Chatbots

Chatbots developed using Artificial Intelligence are now being deployed by companies to handle customer queries to deliver more personalized interactions with eliminating the need for actual human personnel. Bots generally process large amounts of data to provide the appropriate answers based on the entered keywords by customers in their queries.

Chatbots also performs real-time analysis by collecting and analyzing information about customers from conversations. This process helps marketers to develop a more streamlined strategy to achieve better conversions.

Conclusion

All the technologies that we have discussed have solid foundations in the form of Big data. And in order to get real benefit from these trends, you need to understand how they are used and how they can help you to achieve your business goals.

This is only the beginning as Big data will continue to serve and ready to bring the new changes in business and technology. Now it is up to us how efficiently we are ready to adopt these changes.

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