Industrial operations are always evolving and innovating to reach new heights. Certainly, we see the industrial Internet of Things (IIOT) contributing to a lot of the modern efficiencies that have only been made possible by such a degree of digitization. But one aspect of relying on the Industrial Internet of Things remains a work in progress, and that is assessing the data capture and storage needs of an IIOT facility. Of course, there isn’t a shortage of available solutions either. Companies need to consider whether edge computing and micro data centers will be the best solution or to use larger cloud data centers. To decide how to manage the massive amounts of data generated by the industrial Internet of Things, facility managers must turn to the data in question when making this assessment.
Volume of Generation
Firstly, one should consider the volume of data being generated in an industrial Internet of Things environment. As mentioned above, there are massive amounts of data being continuously created, captured, analyzed, and stored. Even if you are monitoring industrial performance factors like temperature or time, the data points generated over time do add up considerably. Although, the simplicity of these measurements yields quite minuscule amounts of data at first assessment. Of course, the volume of data also increases with the scale of the operations under analysis and how frequently data is captured. Not to mention, some industrial facilities employ more sophisticated methods of gathering data, like using high-resolution footage. In this scenario, much more data capturing and processing is required to analyze performance from camera footage, which creates several gigabytes worth of data per second.
Variety of Data
There are multiple applications for the Industrial Internet of Things technology. Sensors, meters, and other data collection devices gather data readings and samples of all kinds. One type may be measuring and reporting numerical data on performance. Another may only be capturing instances when there are possible conditions for performance deviation, like for predictive maintenance purposes. The variety of data a facility captures will vary depending on system sophistication and corporate goals.
Accuracy and Reliability
Indeed, we want the data we go through all the trouble of capturing to reflect accurate readings that are reliable for insightful analysis reports. As it stands, all large pools of collected data are bound to contain outliers. The solution is to understand that there will be a degree of inaccuracy and isolate these deviations. A separate analysis of the data set may be needed to produce a more accurate representation of the industrial operations. This should be done before the data set gets moved along the chain for additional analysis and processing.
Value and Retention
Data is valuable because of all the insights facility managers can derive from the reports. But after the raw data has been processed, is it still necessary to keep it around? If so, when would it be acceptable to purge the data from our databases and systems? Individual corporations will have protocols for how they handle this. Further, different industries may have different compliance codes they must follow regarding data collection and documentation. These issues inform companies’ decisions on the type of storage solution they must use in the meantime.
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