Systems based on Internet of Things (IoT) technology necessarily involve large combinations of business assets (i.e., sensors and other intelligent devices), networks, databases, and software applications. But the most important aspect of business-oriented IoT systems is the data generated by large populations of physical assets—so called “things.” It is this data that can help drive better business outcomes. And it is the harnessing, processing, and acting upon this data that is the central role of DataV from Bsquare.
While the range of business benefits that can be realized using IoT is large and varied, the most important benefits are those that drive down operational costs through more efficient and effective use of critical business assets or drive up revenue through improved customer interaction and even entirely new services. Either alone or together, these benefits directly contribute to improved bottom-line financial outcomes.
DataV is a software platform that businesses can employ to connect remote devices, monitor data streams, automate corrective processes, predict adverse conditions before they occur, and, ultimately, optimize the performance of critical business assets and processes. In short, DataV extracts superior business outcomes from the data generated by diverse business assets.
The principal function of IoT technology is to collect data from a broad variety of physical assets over an extended period of time and deliver that data to cloud-based (public or private) databases so that rules and analytics can be applied to the data. The end goal is always and everywhere to improve business outcomes, but a variety of complimentary technologies are required in order to implement IoT systems. These include:
The most foundational aspect of IoT is to connect, through whatever technology is most appropriate, physical assets to networks, cloud-based databases and applications (the cloud may be public or private, on-prem or off-prem). A key function of the connect process is to intelligently fil
ter, compress, or combine data sets in order to reduce network costs. In many IoT implementations, as much as half the cost of the overall system is comprised of network transport costs. By taking steps to reduce the volume of upstream data, costs can be substantially reduced.
The data stream generated by physical assets is monitored in real-time in order to detect anomalous conditions. There are two important aspects to the monitor function which are often overlooked in IoT implementations. First, “monitor” does not mean to display data on a dashboard for human consumption. Rather, it means software intelligence examining real-time data feeds and applying heuristically derived (and possibly changing) rule sets. Second, aspects of the monitoring function ideally take place on the physical asset itself. This is necessary to provide faster response to critical conditions and also to facilitate operation in off-line conditions.
A broad array of actions may be required in response to anomalous conditions detected by the monitor function. These include commands to the physical asset itself (e.g., reduce engine RPM to prevent overheating), notifications to operations and support personnel, and even commands to other enterprise applications (e.g., inventory, support and trouble-ticketing systems). Here also, it is important that a portion of this functionality reside on the physical asset in order to allow actions to be taken even when the asset is disconnected from the network.
Ultimately, one of the most valuable functions of any IoT system is to predict failures before they occur and take ameliorative action so that downtime is minimized or even eliminated. This is primarily a data analytics function—looking across very large data sets and extended time periods in order to determine conditions that frequently precede failures. By this means, businesses can deter
mine that an asset failure is about to occur, what service action and parts are required to prevent the failure, and schedule remedial actions off hours so that downtime is eliminated and repairs are less costly.
The final step in driving better business outcomes from the data generated by physical assets is to modify operational parameters of those assets in order to optimize efficiency. Similar to predictive failure, this is done by examining large data sets across all connected assets, determining the operational parameters of the best performing equipment, and applying those parameters to other assets. In addition to further improving asset uptime, this process can reduce operational costs and increase asset output, productivity or yield.
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