The Internet of Things (IoT) got its own name for a reason. While the wildfire-like spread of this vast network of connected sensors and devices shares many elements with the birth of the plain old internet, the distinctions are fundamental. Primarily, it is a difference in the nature of the connected nodes. IoT devices may be “smart,” but they aren’t PCs — they have little compute power of their own, they aren’t operated directly by humans, and they are wholly dependent on networked connections to function. Moreover, IoT devices don’t work via batch processes; they send alerts and data when real-time events trigger them to communicate. These dynamic networks require fluid, extensible, software-defined infrastructure.
As we add billions and billions more data-generating nodes to the internet over the next few years, the inherent challenges will grow apace — especially security, visibility, data storage, and data traffic management. Market projections vary; Cisco forecasts 50 billion devices by 2020 while IHS Markit predicts 125 billion devices by 2030. In any case, the scale is massive and unprecedented. Harnessing the opportunities and sorting out the complications will require robust automation and intelligence capabilities. The evolution of the intelligence stack will determine the security and sustainability of IoT applications. Ultimately, intelligent algorithms will dynamically process everything from sensor data streams to cybersecurity alerts to predictive business insights.
In the battle to keep up with the explosion of IoT devices and data, a strong foundation is paramount. The reliability and responsiveness of the network is fundamental. If data can’t transmit at the highest speeds and reliability, the basic functions of many IoT products — medical devices, smoke alarms, security cameras, connected cars — will be unacceptably compromised, and further innovation inhibited.
AI and Automation
Of course, the essential role of automation, artificial intelligence (AI), and machine learning (ML) doesn’t end with device operability (i.e., ensuring data can flow back and forth in real time). The next layer is analytics (mining data in real-time for trends, patterns, and insights), followed by decision-making and extended use cases (intelligence to act on the collected and analyzed data or to share and sell it for use by additional systems). For example, in the event of an after-hours office fire, not only is it imperative that the smoke alarm signal reach the fire department, the system needs to be smart enough to check for alarm activity in nearby offices or buildings, and send the correlated data to the fire department so they can send enough equipment and personnel. And of course all of this has to happen without fail and without delay.
Digital Transformation Overdrive
It’s clear that when setting up the logic for any step in your IoT network, intelligent controls will have to be dynamic, programmable, and accessible. While substantial, proven IoT use cases may be years from fruition in your organization, the drive toward digital transformation is already in full swing. To withstand disruption, enterprises should start building their intelligence stack now, starting with intelligent global network traffic management.
Your competitors are diving into IoT, AI, and ML adoption. A 2017 Cowen study highlighted that 81 percent of the IT leaders surveyed were actively investing in AI — 43 percent are evaluating and executing proofs of concept, and 38 percent have deployed and are planning further investments. A McKinsey Global Institute report summary outlines how AI is creating value for early adopters throughout their value chain. Auto manufacturers, for example, use IoT and AI to optimize their shop floor operations, advance their marketing intelligence, add sensors to cars that help with maintenance and repair, and develop the next generation of connected cars. As these technologies are adopted throughout their supply chain, they will drive step changes in efficiency, visibility, and quality control.
Oracle’s Amit Zavery shared some eye-opening predictions about the near term penetration of intelligent automation in cloud computing operations that emphasize just how rapidly this transformation is being realized. And he’s talking about 2020 — a mere two years from now — not a theoretical future.
If you aren’t investing in your intelligence stack, you’ll lose out on multiple fronts: productivity, cost reductions, product and service insights, and business opportunities with enterprise clients that require their partners and vendors to be IoT-enabled.
Circling back to where we started, consider again what underpins each of these efforts — wherever IoT/AI/ML technologies are being applied, the networked flow of data is an essential component. As the number of devices and petabytes of data continues to surge, providing computing power, data storage, and connectivity at the edge (closer to the devices) is going to be increasingly vital — and increasingly complex. The already low tolerance for data latency and outages will quickly become zero tolerance, especially for critical applications with public safety and cybersecurity implications (transportation, medicine, smart cities, commercial and residential security systems, etc.).
Certainly none of the traditional, static ways of handling network traffic will suffice — every bit of infrastructure must be optimized to handle the IoT burden, starting with DNS. Data traffic routing will have to be intelligent, driven by algorithms that ingest server data, real user measurements, and third party metrics in real time in order to pick the best path — with “best” being defined by parameters such as speed, reliability, type of data, and efficient resource use.
As always, the pace of change is relentless. Jump into the fray now by getting a handle on automated, algorithm-driven network and data management, and build your intelligence stack — apps, platforms, analytics, and infrastructure— from a smart start.
Andrew Marshall, Principal Product Manager
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