What are the new features that await the Big Data market in 2019? What trends will mark the year? Which companies will dominate the industry, and what will be the major technological developments? Let’s take stock.
In 2018, the Big Data market has changed a lot. New tools and technologies have emerged, companies have merged, startups have taken off. As the year draws to a close, it’s time to look to the future. Discover all the trends that will mark the year 2019.
Big Data 2019: RGPD is emulating the big data
Since its entry into force in the European Union on 25 May 2018, the DPMR has fundamentally changed the way companies process data. However, this is only the beginning. Next year, companies will continue to adapt to this new regulation to avoid having to pay a fine of up to 4% of their turnover or 20 million euros.
Moreover, while data protection authorities such as the CNIL have been rather lenient so far, they could become more severe from 2019 onwards. Companies that are not yet in compliance with the GDMP will therefore have to redouble their efforts, both in the European Union and in the United States.
He’s likely that other countries will adopt similar regulations. For example, in Oregon, the Consumer Data Privacy Act was recently introduced by Senator Ron Wyden. California also passed its Data Protection Act in July 2018.
Big Data 2019: Decentralization continues
In 2019, the decentralization of Big Data will continue. Companies will be able to save time and money by processing and analyzing data at the edge of the network rather than at the core.
Among cases of use of this approachThese include fraud detection, pattern recognition, and persistent streams. Similarly, the autonomous vehicle, gas and fuel platforms, and medical devices sectors will continue to exploit this method.
Big Data 2019: Hybrid Cloud Hybrid Increasingly Popular
By 2018, the Hybrid Cloud has become the preferred model for data storage for IT managers in all industries. Indeed, while companies are keen to store their most sensitive data on their own servers, the Public Cloud also has advantages in terms of cost and flexibility.
However, the Hybrid Cloud makes it possible to combine a company’s private Cloud with the rental of a public Cloud to take advantage of the benefits of both models. Applications and data can be easily transferred from on-premise servers to Public Cloud IaaS as needed. In 2019, the Hybrid Cloud should continue to grow in popularity.
Big Data 2019: the maturity of Machine Learning
In 2018, the Learning Machine was at the heart of the news. 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 the experimentation and conceptbut in application and production. They will use ML for the automation of pattern detection, prediction and decision making. This will enable them to become more efficient, differentiate themselves from the competition and stimulate their growth.
Infrastructures and tools will evolve to facilitate the development and deployment of Machine Learning applications. The Cloud-native” platforms will multiply in particular to enable elastic auto-scaling and multi-cloud portability for Machne Learning’s end-to-end workflows.
Big Data 2019: IoT and Streaming Analytics for Machine Learning
Currently, many companies are trying to use the Internet of Things (IoT) to combine Streaming Analytics and Machine Learning. The idea is train Machine Learning algorithms from IoT data in real time rather than exploiting stored data.
The primary objective is increase the reactivity and flexibility of the Machine Learning in a wide variety of situations, including communication with humans. Machine Learning models on the Cloud or in the periphery will also adapt to this change.
In 2019, these efforts can be expected to continue.. Several startups could even make this combination of technologies their main focus.
Big Data 2019: Artificial intelligence meets data analysis
Artificial intelligence makes it possible to process the Big Data more efficiently. For example, the company Absolutdata has managed to increase its sales by 51%. using artificial intelligence and data analysis for his email campaign. According to her, analytical tools help determine who to target, while AI helps to recommend and generate campaigns.
In 2019, the AI platforms will be more widely used for data analysis. They allow you to work more efficiently than traditional frameworks, and offer faster communication with Data Scientists and other data professionals. This reduces costs by avoiding duplication of effort, automating the most basic tasks, and eliminating simple, time-consuming tasks such as copying and processing data.
Big Data 2019: the growth of Data Curator’s business
The role of the Data Curator is to manage metadata, data protection, data governance and data quality. This Data professional manages and maintains databut also needs to identify best practices for working with these data.
The Data Curator is often responsible for data presentations in the form of dashboards, graphs or slideshows. He regularly interacts with researchers and plans educational workshops. He also communicates with other data custodians to coordinate his efforts when necessary.
In 2019, while the volume of data available to businesses will continue to increase, the Data Curator’s job will become indispensable in all sectors. This could be one of the hottest jobs of the year.
Big Data 2019: Hadoop continues its slow demise
In 2019, it is highly likely that the number of new users of the Hadoop platform will decrease and that the growth of Hadoop clusters continues to slow down. This platform is based on technologies designed 20 years ago in the era of “Small Data” is gradually becoming obsolete.
The merger of Cloudera and Hortonworks in October 2018 foreshadowed this coming disappearance. On the contrary, the Cloud-based SQL data platforms will experience massive growth.
Big Data 2019: The death of Big Data?
In 2017, Gartner analyst Mark Beyer predicted the end of Big Data. According to him, this technology was doomed to join the other disappeared technologies as vulgar fashion effects. Similarly, another Gartner analyst by the name of Svetlana Sicular believed that the Big Data had become “the new standard” and that we can now simply speak of “data”… .
However, the fact that Big Data has become ubiquitous and is used on a regular basis does not mean that the volume of megadata has decreased or that its velocity has slowed down. The Big Data is still a heavy burden on infrastructureand the huge datasets are always retrieved, stored, sorted and analyzed for software development, Machine Learning and many other use cases. In 2019, Big Data will still be a challenge even if it is no longer called that. The volume of mega data will continue to grow.