The amount of data collected in industry and commercial business is increasing at an unprecedented rate. As connected systems become more prevalent, there is a real opportunity to leverage off of the machine generated data (MGD), to improve the effectiveness of IoT systems, devices, and sensors. With that opportunity, also comes a significant challenge. How can operators make sense of the vast amounts of data, and how can they redesign their systems to integrate more closely and with more efficiency?
New Approaches to Data Integration
Perhaps the biggest difference in a world of MGD, is that systems are no longer isolated. If we look back to a time when IoT was not fully realized, we would find systems where devices and sensors operated independently, within closed and often proprietary systems. Big data would usually be logged within its own system, with reporting only being specific to a small group of machines or sensors. Not only did this mean that efficiency was reduced, but there was also the potential for lost or misinterpreted data that would lead to failures in the operating chain. Within industries like manufacturing, this type of operation was particularly inefficient.
Because IoT allows for hundreds and even thousands of devices to communicate effortlessly, the MGD can more easily be used for automated functions, or to improve the decision making process within any business. In the past, data collection, aggregation, and interpretation, would be time consuming and require complex computer systems or human interaction. With MGD, the time required to collect and make use of data is drastically reduced. The problem is, it’s easier in theory than it is in practice.
Operational Technology Must Adapt to Machine Generated Data
To make the best use of MGD, operational technology must be redesigned from the ground up. Traditional IT systems cannot feasibly cope with expansive MGD because isolated systems within a wider IoT network will not work quickly or efficiently enough to process the data into meaningful instructions and insights.
For some time now, cloud computing has been described as the solution that would bring IoT systems closer together. From an operational standpoint, the purported benefits of the cloud are enticing. Offsite data storage, easy integration with individual devices, and cost effectiveness, are just some of the key benefits that make the cloud so attractive. However, for businesses with complex device implementations, the cloud is inherently inefficient. Data must be sent and returned to and from external data centers, and local devices are left vulnerable to communications and systems failures. Even with the high level of redundancy that cloud systems provide, the situation is still not ideal for MGD in closed environments. In applications such as industry and transportation, MGD needs to be analyzed and exploited in real time, or at least with minimal latency.
To overcome this challenge, many operators are moving towards Fog Computing systems. While still leveraging off of some cloud technologies, fog computing brings services closer to the network. Some of the processing could be performed on devices or local network servers, which allows for increased security and speed of data utilization. When some devices can produce gigabytes or even terabytes of data within hours or even minutes, Fog Computing makes perfect sense.
There’s a Special Relationship Between Sensors, Devices, and the Data They Generate
The Internet of Things is quickly becoming the Internet of Everything. The potential for sensors to integrate with devices for data collection and automation is near limitless. As more data is collected, the need to process and use this data efficiently is growing. While latency and processing ability is not essential for low level devices such as consumer convenience devices and basic commercial devices, the stakes in industry and transportation are much higher. Operators who integrate sensors must also rethink their data collection and processing approach, which will reward them with faster analytics, more efficient use of bandwidth and storage, and better support for critical machines and applications.
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