Meeting The Demands Of IoT Data

With new uses and applications of IoT, there also comes new methods of delivering and managing connectivity for devices—as well as fresh ways to monetize the data they carry.

By Vikram K It’s boom times for the Internet of Things. “Things” are being added to the connected global ecosystem at an incredible rate—and not just the overall number of things, but the diversity of use cases is growing rapidly as well. Gartner predicts that the number of endpoints will reach 20.8 billion units by 2020. With new uses and applications of IoT, there also comes new methods of delivering and managing connectivity for devices—as well as fresh ways to monetize the data they carry. While the evolution of devices in the ability to collect data is captivating, the challenge for organizations will be in creating a flexible infrastructure through which they can connect and manage the changing world of things. When Things get complicated Every organization will have unique requirements for managing IoT data based on factors such as how fast, how secure, or how critical the data is. Even within a single organization, different M2M data will need different technologies and flexible architectures to be valuable. And in the vast majority of cases, the data will also need to be shared with other IT instances, like analytics platforms and other applications. The automotive industry provides a great case study for such variances. Today’s newer “connected cars” collect a wide range of operator and performance data. But how are car manufacturers using it? One way is to transform the customer experience, providing constant connect points between the auto maker and the consumer. For example, car makers might use some of this data to warn about impending weather, or alert drivers approaching a border to have their relevant documents ready or risk being fined or turned away. By monitoring how customers are using their cars, the auto maker can better identify more valuable features and functionalities—while the customer gets a better driving experience. The collection and analysis of data on how drivers use their cars can happen over a long period of time, but there are other uses of data in the connected car that require real-time analysis and action, like the risk of collision. This has a very different set of data collection and processing requirements. Auto manufacturers —and any other organization deploying an IoT solution—need a flexible IoT platform to help them meet the requirements of these very different use cases. They also need to address certain challenges, all of which can be classified within these categories:

  1. Connectivity. The most efficient choice for transmitting M2M data will change based on the device and how the data is used. There are new options in connectivity, including cellular networks, LTE, and cutting-edge low-power, long-range networks.
  2. Compute. Already many organizations have learned the hard way that as data volume scales from megabytes to terabytes to petabytes, their ability to ingest, validate, and load that data can’t keep up. Compute resources must scale easily and flexibly—and critical real-time data must be processed at the edge for faster time to action.
  3. Security and governance. If your devices move around, they need business rules to govern their behavior based on changing location or conditions. And of course, sensitive data has its own set of requirements.
  4. Analytics. Where and how you process data is becoming a critical pivot point in IoT architectures. Low-latency requirements often invoke data processing and edge analytics, for example. However, other uses require you to collect data from different locations over longer periods of time, while transmitting to a data center or the cloud for later processing and analysis.
  5. Partner Ecosystem. There is no single company that can deliver the full breadth of an IoT solution. A successful IoT deployment requires many capabilities that can only be fulfilled by teaming with multiple partners for efficiency. Auto makers, for example, may use a third party to deliver service and performance insights to consumers.
  6. Services. Business stakeholders and data analysts need a methodology for interacting with and managing deployed devices—as well as people and processes—so they can monitor for efficiency and performance.
Organizations are beginning to realize that their changing IoT landscape requires versatility. They want a platform that isn’t tied to any industry, vertical, or partner; one that can accommodate the right connectivity technology and the right partners for delivering endpoint services or analytics; one that scales easily and changes when it needs to without IT or service disruptions. The case for adaptive architecture No single company can deliver all of what today’s companies will need in the IoT space. From hardware, to software, to connectivity, to services, each organization will need to create an ecosystem of providers and vendors to make an IoT solution that works for them. What they need is an adaptive and flexible framework to manage it. When building a flexible IoT platform, businesses need to simply and easily lay a foundation for IoT that is primed for any possibility. They should consider a solution that is industry, vertical and client-agnostic, while modular and versatile enough to rapidly accommodate whatever comes. Businesses should also implement a platform that provides access to a wider ecosystem and delivers true integration with agnostic support for smart ecosystems, federation for device and service management, and adaptability to their IT infrastructure and business needs. By ensuring flexibility for IoT in existing IT environments, businesses will be able to monetize their data and build business value rapidly, regardless of where data comes from—or where it needs to go. (Vikram is Director, Servers, Hewlett Packard Enterprise India)


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