Technological landscape

IoT, Cloud, Artificial Intelligence and Other Predictions for 2019

In typical industrial IoT scenarios, the analytics functions are distributed across various edge devices and cloud services

2018 has almost passed and as the year draws to a close, it may be good if information technology companies have insights on the technology trends for the coming year. Arun C. Murthy, CPO, Hortonworks, states some of the predictions for the year 2019.

The Grass is Greener on the Other Side (Cloud), and the Other-other Side (other Cloud)

Last year was all about enterprises rapidly moving to the cloud, but 2019 will be the year of multi-cloud and hybrid-cloud. Cloud providers will, more aggressively differentiate among each other in specific areas (Amazon: Operational readiness, Microsoft: Enterprise integrations, Google: AI/ML etc.) to avoid a race to bottom scenarios. Enterprises will adapt with multi-cloud strategies to leverage each of the above differentiators by adopting common security, governance and data/workload management strategies – in several cases powered by open-source standards – to thrive and differentiate. We will also start to see Enterprises move some always-on workloads from the cloud back on-prem for a hybrid model to optimize economics.

Enterprises will get Serious about the Edge

Beyond the firewall is still the Wild West for many organizations. It’s unknown, ungoverned, and not being taken full advantage of. More emphasis is going to be put on securing, managing and governing data at the edge and those that do this well will have a competitive advantage over those who only capture value from local data.

It’s Artificial Intelligence Inside

On scale of 1 to 10 with 10 being AI/ML has matured for the enterprise, in 2019 we’ll be at earlier points in the scale. People-skills and processes needs to advance and getting tangible value needs to become simpler so that users can focus on business outcomes rather than complex technical details.  And as data integrity is the foundation of successful ML/AI applications, organizations will need to implement a higher degree of governance and control over their data inputs when its volume is rising so quickly. Vendors will also adapt by increasingly leveraging AI/ML inside products to help drive value, and quickly.

IoT: Edge Computing and Cloud Harmony

In typical industrial IoT scenarios, the analytics functions are distributed across various edge devices and cloud services. The more important fast reactions and real-time capabilities are, the more important becomes edge computing. The cloud, on the other hand, is the central data collection point for business analytics, machine learning and process control. Organizations will work out the right mix of edge computing and cloud look like as real-time analytics at the edge becomes ever more important to new dynamic and modern business models.

Open Source Business Models will Adapt in the Cloud

Gartner predicts that 95% of IT will use open source in mission critical portfolios in the future, as practical concerns around vendor lock-in, staffing challenges and evolving pricing models are forcing data and analytics leaders to re-evaluate their database management system investment as part of their larger data management strategy. Open source competition will heat in up in this era of cloud as vendors weigh consuming and contributing with their business models.


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