Information technology systems of the future are increasingly focused on where data is generated and processed, how it’s delivered and collected, and how quickly this data can move. Finding the most efficient path is key.
Two of the most significant trends are the internet of things (IoT) and artificial intelligence (AI), which fit together like hand in glove. In a very simple form, IoT is about a multitude of devices exchanging data from a multitude of data points, which are being collected in a plethora of ways and on a plethora of platforms. That data must be quickly analyzed and in most cases, sent to the next level for further processing.
Meanwhile, AI is about programmatically manipulating this big data to make real-time and time-sensitive decisions. The only way to build for this technological union is with a hybrid multicloud platform. The elements of hybrid IT infrastructure providing the most efficient path for AI and IoT form the foundation of technologies that will spark business advantages, innovation, and the “cloud of clouds” of the future.
IoT and the edge of computing
There are devices all around us that are collecting, distributing, and processing data at what is considered the edge of the modern enterprise and the consumer space. Even further, all of this data must be quickly analyzed, collected, and transferred in space that is beyond the realm of immediate control.
This level of effort requires incredible distributed collection and storage requirements that are closest to the source. This means that the IoT edge and all the computing events that occur in these systems are a focal point for automation and other emerging trends. These elements are the main catalyst for further innovations in computing architecture of the future, due to the ongoing growth of the edge through the proliferation of increasingly intelligent and interactive devices.
The edge of IoT must exhibit instantaneous transactions, without central controls, through distributed connections that can validate, create, and tear down connections. At the very least, the basic principles set limits on how far data can be moved before latency begins to create operational issues. How far is the edge, the actually feasible edge?
Working together, the logic behind it all is AI. Data life cycles, flow, data classifications, reporting, and countless aspects of IoT are dictated by the intelligence of AI.
AI is not some self-aware robot, as Hollywood movies would like us to believe, but it can seem like it’s straight out of science fiction. At this point, AI technology is far beyond the initial phase of hype and to find it you must recognize that these technologies are designed to learn, adapt, recognize patterns, and mimic human intelligence at scale. All you have to do is look at the self-driving vehicles out there—from autonomous cars to auto-pilot on planes—with the amazing intelligent split-second decisions they are able to make.
AI and IoT are symbiotic, and it is critical to understand the relationship between them. AI calls for an immense order of computing power to operate, and in many cases, this need can only be served via bare-metal compute power. Speed and performance are key, as life and death decisions can hang in the balance. Further, decisions made by AI engines need to be fed back quickly and accurately to the IoT devices. Examples of this include:
- Self-driving autopilot systems that detect life-saving conditions such as floods, rerouting traffic, and alerting to avoid accidents
- Medical devices that can automatically defibrillate and send a 911 signal to the nearest hospital
- Automated agricultural combines that can avoid a loose animal or a herd and alert a farmer
- Credit card fraud detection
- On-demand recommendations that come from video services
- Apple’s Siri technology and Amazon’s Echo ecosystem making super-rapid decisions that are manifested at the endpoint
The list goes on and on. You can see in those examples that AI requirements call not only for speed, they call for lots of data, and AI systems will programmatically manipulate oceans of data to make real-time decisions. AI endeavors to deliver programmatic reasoning, self-correction and ultimately learning. In the enterprise, the potential advantages and benefits are unlimited.
Among these capabilities, AI can:
- Help reduce human errors across the organization
- Manage large amounts of data
- Improve the work processes of staff
- Support the digital transformation of a business
- Significantly help to deliver a seamless customer experience
AI technologies are increasingly being introduced through third-party software and capabilities within existing software tools. AI and IoT designs are becoming the blueprint for the enterprise.
Hybrid multicloud disruption
The merger of IoT and AI is simply not possible without an enabling platform and architecture. That’s where the hybrid multicloud steps in. All companies, even those in the same line of business, exhibit unique technology DNA that has been built for and suited to their individual business needs and growth. Hybrid multicloud is a disruptive technology development and a business opportunity. To understand this disruption requires an understanding of the relationship between hybrid, IoT, and AI.
Among the most critical of advantages that hybrid cloud platforms provide for IoT-AI environments are:
- Various forms of storage Custom-tailored for AI and IoT constructs, hybrid clouds can feature various tiers of storage such as real-time, archival, redundant, distributed, etc. No single cloud can do this. Stored data can be accessed quickly and programmatically by the AI engine, and enriched over time through machine learning. For example, one could use AWS S3 storage for archival and off-premise SAN storage for high-performance needs.
- Rapid processing of information and rapid enrichment of data through correlation of various sources for AI: Data processing runs fastest on bare metal, where there is a minimal amount of barriers, and hops between this core and the raw processing power of servers. A bare-metal server farm remains the most optimal construct for AI processing.
- Customized security to the application: Securing the application is a critical enterprise mission, especially in centralized scenarios. Recent security breaches in the news were tied back to the poor usage of AWS systems, allowing for the exposure of private information. Many of the procedural gaps at the root of these and other incidents can be tracked to knowledge gaps, training and technology. Core hybrid data processing allows for enterprise controls, reporting, and auditing constructs that are not simply used in public cloud environments.
Hybrid: cloud unlimited
We live in an age where somehow, somewhere, the cloud seemingly processes every interaction, transaction, and communication. Nearly every application in the world uses the cloud as its integration fabric. The information systems of tomorrow will become more focused on real-time experiences across an increasingly widening range of devices. Under the old rules, such as Moore’s Law, broadband growth, and other linear trends that defined the computer services industry, innovation could only deliver along the boundaries of time. That’s not the case anymore, thanks to hybrid cloud technologies.
IoT, AI, and hybrid cloud are three sides to the same triangle, three legs to the same stool—the holy trinity of IT. Together, these forces have elevated data as the core of modern-day application innovations. The future for this world of applications is unlimited. Hybrid cloud is not just a platform. It is built of strategy, as a leading technology solution, as an architectural marvel, and most importantly, as a promise to build into the future.
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