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Top 5 Big Data Trends for 2017

Big data quickly became one of the primary buzzwords in the technology sector over the past two years. Although many companies are looking to embrace big data and discover new customer patterns in the process, it remains to be seen how enterprises will go about things. Several potential big data trends have been identified for 2017 already, although one never knows if these predictions will become reality over the next few months.

#5 Predictive Analysis

As most people are well aware of, big data is mostly about finding new patterns and using that information to serve customers better. Predictive analysis will make a big impact in this regard, as this “method” allows enterprises and experts to accurately predict future behaviors and events. In the financial sector, for example, detecting fraud before it can even materialize is an example of how predictive analysis would work.

#4 Cloud-based Big Data

It is not entirely surprising to picture a world where the cloud will help enterprises put big data to good use. To be more precise, a lot of data analytics tool will be moved to the cloud, as they require a lot of computational resources. Moreover, the cloud-based approach should eventually accelerate the adoption of new capabilities to turn raw data into useful information.

There is a second purpose to moving analytical big data tools to the cloud, though. Cutting costs in maintenance and operations by moving to a cloud-based service is quite appealing to a lot of enterprises all over the world. It remains to be seen which companies will emerge as cloud-based big data analytics provider, though, but it is expected a lot of proprietary solutions will be created in the coming years.

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#3 Deep Learning

Popular technologies will come together to create a more robust big data ecosystem in 2017. Experts predict deep learning will make a major impact. Considering how deep learning is used to solve business problems through complex algorithms, feeding big data into these solutions can yield quite powerful results. Deep learning primarily learns and evolves from processing large amounts of unsupervised data, which makes the “big data” pile an excellent fit.

#2 Apache Spark

There has been a lot of talk about a “new” project called Apache Spark. This project provides Spark Streaming to allow for near real-time processing of big data. A lot of enterprises have grown keen of using Apache Spark in recent months. In fact, some experts claim Spark has become the largest big data open source project, and expect this solution will continue to become even better as time progresses.

#1 Improving Cyber Security

It is evident the cyber security aspect of nearly every company needs to be improved sooner rather than later. Contrary to popular belief, big data can be used to accurately predict cyber attacks, vulnerabilities, and even thwart ransomware attacks. Analyzing big data will help security engineers gain a better understanding of which vulnerabilities criminals may want to exploit in the future.

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JP Buntinx

JP Buntinx is a FinTech and Bitcoin enthusiast living in Belgium. His passion for finance and technology made him one of the world's leading freelance Bitcoin writers, and he aims to achieve the same level of respect in the FinTech sector.

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