Considering the surge in data usage in all sphere of life and business, indications are rife that Big data will continue to be the fastest-growing segment of the information management software market.
Tony Baer, Principal Analyst for Information Management said “Big data has emerged from its infancy to transition from buzzword to urgency for enterprises across all major sectors,” adding that “The growing pains are being abetted by machine learning, which will lower barriers to adoption of big data-enabled analytics and solutions, and the growing dominance of the cloud, which will ease deployment hurdles.”
This is as new discoveries by leading global data and market research, and advisory firm Ovum estimate that the big data market will grow from $1.7bn in 2016 to $9.4bn by 2020.
The growth is expected to comprise 10% of the overall market for information management tooling.
Ovum’s 2017 Trends to Watch: Big Data report highlights that while the breakout use case for big data in 2017 will be streaming, machine learning will be the factor that disrupts the landscape the most.
However, key 2017 trends include Machine learning will be the biggest disruptor for big data analytics in 2017; Making data science a team sport will become a top priority; IoT use cases will push real-time streaming analytics to the front burner; The cloud will sharpen Hadoop-Spark “co-opetition.”
Others are Security and data preparation will drive data lake governance.
Under the covers, machine learning is already becoming ubiquitous as it is embedded in many services that consumers take for granted.
Increasingly, machine learning is becoming embedded in enterprise software and tooling for integrating and preparing data.
Machine learning is placing a stress on enterprises to make data science a team sport; a big area for growth in 2017 will be solutions that spur collaboration, so the models and hypotheses that data scientists develop do not get bottled up on their desktops.
Open source technology has lowered barriers to entry for both technology providers and customers; scalable commodity infrastructure has made the processing of large torrents of real-time data in motion economically and technically feasible.
Again, the explosion in bandwidth and smart-sensor technology has opened up use cases ranging from location-based marketing to health and safety, intrusion detection, and predictive maintenance, appealing to a broad cross section of industries.
