IoT, Cloud, API predictions for 2016
The year 2016 will be exciting in terms of applied technologies. We see a lot of technologies maturing and moving from lab exercises to real-world business technologies that solve real-life customer problems – especially in the areas of digital transformation, API, cloud, analytics, and the Internet of Things (IoT).
In particular, we see the following areas evolving faster than others:
Year of the Edge (Decentralization of Cloud)
Cloud has become the ubiquitous digital platform for many enterprises in their quest to provide a single unified digital platform. Integrating the core IT with the shadow IT has been the main focus for the last few years, but in 2016 we anticipate the next step in this process. We started seeing companies moving from the central cloud platforms toward the edge, or toward decentralizing the cloud. This is partly because, with the proliferation of IoTs, operations technologies (OT) and decision intelligence need to be closer to the field than to the central platform.
Cloud has become the massive centralized infrastructure that is the control point for compute power, storage, process, integration, and decision making for many corporations. But as we move toward IoT proliferation, we need not only to account for billions of devices sitting at the edge, but also to provide quicker processing and decision making capabilities that will enable the operations technologies. Areas of low or no Internet connectivity need to be self-sufficient to enable faster decision making based on localized and/or regionalized data intelligence.
An IDC study estimates that, by 2020, we will have 40+ zettabytes of data. IDC also predicts that by 2020, about 10 percent of the world’s data will be produced by edge devices. Unprecedented and massive data collection, storage, and intelligence needs will drive a major demand for speed at the edge. Services need to be connected to clients, whether human or machine, with very low latency, yet must retain the ability to provide a holistic intelligence. In 2016, the expansion of the cloud – moving a part of cloud capabilities to the edge – will happen.
Because of the invention of micro services, containers, and APIs, it is easy to run these smaller, self-contained, purpose-driven services that specifically target only certain functions that are needed at the edge. The ability to use containers for mobility and the massive adoption of Linux will enable much thicker, monolithic services previously running centralized to be “re-shaped” or “re-imaged” into a collection of smaller, purpose-driven micro services. Each of these can be deployed and run on the edge as needed and on-demand. Spark is an excellent example of this because it is focused on real-time streaming analytics, which is a natural “edge service.”
M2M Communications Will Move to the Main Stage
The proliferation of billions of smart devices around the edge will drive direct machine-to-machine (M2M) communications instead of the centralized communication model. The majority of the IoT interactions are still about humans (such as the concept of the quantified self) and a human element also is involved in the decision making somewhere, even if it is not about quantified self.
We predict that the authoritative decision making source will begin moving slowly toward machines. This will be enabled by the M2M interactions. The emergence of cognitive intelligence themes (such as IBM Watson) and machine-learning concepts (such as BigML) will drive this adoption. Currently, trust and security are major factors preventing this from happening on a large scale. By enabling a confidence-score-based authoritative source, we can eliminate the human involvement and the ambiguity in decision making. This will enable autonomous M2M communication, interaction, decision making, intelligence, and data sharing, which will lead to replication of intelligence for quicker localized decisions. In addition, when there is a dispute, the central authoritative source, with cognitive powers, can step in to resolve the issues and make the process smoother – without the need for human intervention.
This centralized cognitive intelligence also can manage the devices, secure them, and maintain their trust. It can help eliminate rogue devices from the mix, give a lower rating to untrusted devices, eliminate the data sent by breached devices, and give a lower score to the devices in the wild versus a higher score to the devices maintained by trusted parties.
Cloud, AI, ML, AIOps, Edge “Strategery”
This article was originally published in Linkedin on January 3, 2016 – https://www.linkedin.com/pulse/iot-cloud-api-predictions-2016-andy-thurai