Over the years, big data trends have changed. Without a doubt, 2018 was a huge leap for big data. New tools and technologies have emerged, companies have merged, and startups have taken off. The advancement in this technology is allowing the emergence of new trends. Organizations must implement the right trends to stay ahead of their competitors.
With that in mind, we’ve separated some big data trends and predictions which are expected to be seen in 2019.
2018 can basically be called the first year of artificial intelligence. Most companies laid out the manpower, tools and infrastructure of machine learning and deep learning technology, and some industry solutions gradually came forth.
From the perspective of this year, in big data platform and tool market, more and more AI solution tools are being built which gradually evolved from AI modeling and AI algorithm framework tools to data development, process scheduling, and A/B experiments. In 2019, AI platforms will be used more extensively for data analysis. They will allow you to work more efficiently than traditional frameworks, and offer faster communication with data scientists and other data trades.
With smart devices, like Microsoft Cortana and Google Assistant, several companies are already developing products that can be connected to the network and be operated over the internet. Currently, many companies are trying to use IoT to combine streaming analytics and machine Learning. The idea is to train machine learning algorithms from IoT data in real time rather than exploiting stored data.
The observation is that as organizations begin to provide better IoT applications, new ways of collecting, managing and analyzing information will be created. The primary goal is to increase machine learning’s responsiveness and flexibility in a wide variety of situations, including communication with humans.
The rise of predictive analysis is also among the big trends of big data in 2019. Existing solutions are not only capable of analyzing but also for processing the data and understanding the reasons why certain events occur.
However, with predictive analytics through big data, your business will be able to predict what might happen in the future, this possibility will make all the difference in understanding consumer behavior.
Last year, hybrid clouds gained great popularity, as this tool allows companies to safely store data. In 2019, the use of this technology will increase significantly among companies. The hybrid cloud allows you to combine a company’s private cloud with the rental of a public cloud to enjoy the benefits of both the models.
Thus, applications and data can be easily transferred from the on-premise servers to the IaaS of public clouds as needed, which allows greater flexibility.
Although more and more enterprises are trying to adopt big data trends and AI in business scenarios, the industry as a whole still lacks in data management.
Data management in 2019 will remain a difficult and challenging issue for the enterprise data department. Even large, leading Internet companies and technology-based companies are constantly exploring new approaches to data management.
Quantum Computing is a new paradigm that introduces new programming techniques. It is a new way of approaching and solving problems.
Quantum computing improves data encryption, predictions and gives the resolution to complex problems. Technological giants such as Microsoft, IBM, Intel and Google are competing with each other rigorously in an attempt to build the first quantum computer.
Dark data is the information found in the data repositories, not explored or analyzed. It is a type of unstructured and untapped data. As analysis and data become everyday aspects for organizations, there is a greater need to understand that any data that has not been explored is a missed opportunity and can generate a potential risk.
In 2019, new solutions will come to give visibility to these data in non-accessible formats.
In 2018 machine learning was at the heart of almost every advancement in technology. Many companies have adopted this new technology for many use cases. However, in 2019, machine learning will be exploited in new ways.
Indeed, companies will no longer be in experimentation and concept, but in application and production. They will use the ML for automation of pattern detection, prediction and decision making. This will help them become more efficient, stand out from the competition and stimulate their growth. Infrastructure and tools will evolve to facilitate the development and deployment of machine learning applications.
2019 will witness more software tools and free data that will be available in the cloud. In 2019, both start-ups and small businesses will benefit the most from this data trend. Analytical languages and programming environments like R will support this trend in 2019.
2018 brought more headache due to the implementation of the European Data Protection General Regulation (GDPR), which came into force in May. This forced many companies to modernize their consent procedures and their data processing processes, as well as their procedures for notifying and receiving consent. 2019 may be the year in which the rest of the countries decide to imitate the European Union with a similar law.
In 2017, Gartner analyst Mark Beyer predicted the end of Big Data. According to him, this technology was doomed to join other technologies which disappeared as vulgar fashion effects. Similarly, another Gartner analyst by the name of Svetlana Sicular believed that big data had become the new norm and that we can now speak of it in very simple terms.
However, the big data is still a heavy burden for infrastructure, and huge data sets are always retrieved, stored, sorted and analyzed for software development. In 2019, big data will remain challenging and indeed, the volume of big data will continue to increase.