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Big Data Analytics Trends to Watch Out for in 2016

According to the recent analysis, the big data market will reach almost $50 billion by 2019. However, machine data analytics is just in its inception stage, which makes it all the more exciting! As 2016 sets in motion, we may want to look back at the year 2015, when machine data analytics emerged as a natural market radical, arising out of mega trends like the Big Data, DevOps, Cloud and the IoT.

As 2016 sets in motion, we may want to look back at the year 2015, when machine data analytics emerged as a natural market radical, arising out of mega trends like the Big Data, DevOps, Cloud and the IoT. The satire here is that these mind-blowing trends served a dual role amidst the market separation. Wondering how? In the first place, by being the creator of the problem – the volume, the velocity and the variety of data. On the other hand, they also served as the solution source with a cloud-native, service platform. As the problem solution, this platform provides scalability, security and rich data analysis for transforming real-time data streams into effective and functional customer insights for business development and success.

According to the recent analysis, the big data market will reach almost $50 billion by 2019. However, machine data analytics is just in its inception stage, which makes it all the more exciting! As digital transformation evolves, so does the need for speed, agility and full-stack visibility increases but all in real time. This is the real business demand supporting the growth of machine data analytics. In this perspective, let us explore and learn about some major trends in 2016 that will have a considerable impact on machine data analytics.

  • Evolution of DevOps: As organizations look at new technologies to enhance their business agility, speed and competitive edge, they also face new challenges that can't be merely met with traditional, out-of-date and on-premise monitoring tools.
  • Security Operations to Invest More in System Intelligence: Companies have by now, well understood the value of big data to gain actionable acumen for their business decision-making. Now with latest technology advancements such as machine learning, data analytics provide business insights at the atomic level. This takes place in the systems infrastructure and is not yet very popular with most of the companies. Additionally, security teams and CISOs will join to partner with the DevOps team to help secure new app architectures that are embed with security potential, benefitting from integrated machine analytics.
  • Business Intelligence Shifts: With the technology infrastructure changeover to cloud-based platforms for enhancing business speed and agility, business intelligence value will also observe a shift from rear-view to a more continuous one. Thus, 2016 will perceive the BI shifts from on-premise solutions to the ones delivering continuous intelligence in real-time. For companies wagering their business software app platforms, continuous intelligence is a must have.
  • Log Management – A Huge Opportunity for IT Operations and Customer Support Teams: Using analytics to check, manage and gather log (user/ application/ infrastructure) insights will be the only way almost perfect way to address the increasing complexity of cloud and hybrid infrastructures. As such, we await this to increase the organizational awareness of the log management value to support app development, security and IT operational success.

At the outset of 2016, it is apt to learn about the above mentioned Big Data Analytics trends. This will make it all the more interesting to look back 12 months from now to appreciate what unfolds. No matter what the result isScience Articles, one thing is certain and that is – we are in an entirely new world with the data in the driver's seat.

Article Tags: Data Analytics Trends, Machine Data Analytics, Data Analytics, Analytics Trends, Machine Data

Source: Free Articles from ArticlesFactory.com

ABOUT THE AUTHOR


Hi i am Man Singh, worked as Data Analysist for Intelegain Technologies. He has work in diverse domains including healthcare or social sciences etc. Also he has experience on various important tools like SAS & SPSS.



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