9 Key Big Data Trends That Will Dominate in 2021
The 9 top big data trends that will dominate in 2021 predict an increasing complexity in the use of technology, the analytical data and the internet of things (IoT).
The evolution of massive data analysis in recent years has been very fast, so regardless of the economic context you see around you, you can be sure that 2021 brings us important innovations. There will be more intelligent metadata catalogs, real-time analytics with lower latency, higher bandwidth consumption, and simulations and algorithms that will identify data patterns and anomalies .
All this will continue to result in business development and, therefore, will improve growth possibilities. If you’re planning to specialize in this area, take a look at the following trends in the data industry.
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The cloud technology continues to grow and improve. However, the speed of changes does not yet allow sufficient agility and functionality, therefore, a growing trend is the use of hybrid and multi-cloud implementations .
This occurs because for most of the business fabric it is not easy at all to move its entire structure to the cloud and synchronize its platforms and data sources from a local solution. In addition, many companies have left their fingerprint in various clouds .
This process of adaptation and migration can involve an investment of time that, in some cases, is around weeks or months. Therefore, many companies resort to combining cloud and on-premises solutions in a hybrid model .
In this way, they use the cloud to store and work dynamically and use local platforms for stable workloads.
In addition to analyzing data that benefits business development, augmented analytics combines the study of information with machine learning algorithms and natural language processing (NLP) to make your work much more productive.
In this way, it is possible to manage and understand the data and interact with them, as well as detect anomalous trends. In addition, machine learning algorithms, fundamental in the evolution of big data , facilitate predictions and preventive strategies that your company may need.
This technology combines different branches of artificial intelligence such as machine learning in order to optimize the interpretation of data and the possibility of developing and sharing it more quickly.
Related Post: Top 10 skills to become a data scientist
It is based on one objective: to optimize the use of bandwidth and response times in data transfer . This is achieved because the data processing is carried out in a physical space as close as possible to its destination.
The result is faster data flow and lower network traffic density. In other words, less accumulation of time lags is achieved .
It allows you to analyze data in real time . By analyzing large amounts of information we can detect patterns stored in internal memory (in RAM ).
The analysis of data in the internal memory , combined with machine learning techniques , offers fast and accurate information that can be used in large systems.
Although it requires specific hardware with a certain certification and configuration , this technology offers you many advantages, such as table storage and data replication.
Artificial intelligence, the internet of things and machine learning allow, with the help of natural language processing (NLP), that men and machines interact.
This trend will give surprising results in the future thanks to programs that will understand written and spoken human language and will know how to give it various response options.
Although it cannot be considered as a technology, the chief data officer (responsible for data management and analysis) is an alternative to the technology director that is being imposed in many companies. It is an evolving professional profile with growing specialization.
An increasing number of companies handle large amounts of data and many of them want a professional who knows how to handle it in compliance with regulations and getting the most out of their analysis.
Data, if it doesn’t tell stories, is useless. They have to tell, if a piece of information does not tell you anything, you have to find the story behind it, what can it be for.
The Machine Learning or machine learning is another trend already consolidated but continue to grow, and will do its usefulness for all types of companies and sectors.
When we talk about Machine Learning we refer to the ability of algorithms to learn and improve their actions autonomously , that is, the more we execute the algorithm, the better it will fulfill its objectives.
The Big Data ecosystem offers a great capacity for machine learning that will include extensive computation, artificial intelligence and graphical algorithms. Another benefit of this ecosystem is that they will unify analytical technologies, therefore there will be better compatibility of data types and sources and they can be read by any programming language.
One of the priorities for companies is data and resource management by integrating multiple base technologies. Big Data allows us to be able to anticipate the needs and demands of consumers. Data collection opens a wide range of possibilities in customizing the product or service for the customer.
The need for companies to perform data analysis will promote technological innovation (IT) within it and at the same time its integration into the Big Data ecosystem. These advances will be mandatory for all companies that do not want to be left behind in the market. Therefore, the Business Analytics model will begin to be a reality from large multinationals to small startups.
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