Poor software choices can underutilize IIoT monitoring hardware and render technology investments worthless. When implementing IIoT monitoring solutions, it’s essential to choose a software that maximizes hardware innovations and provides a user experience (UX) where someone can easily and confidently access the associated features. However, different types of data states affect what these features should be and how best to engage the user’s needs.
Harnessing Data States: What Are the Three States of Data?
IIoT monitoring solutions deal with many types of data, but there are three primary data states that affect software solutions: data in motion, data in use or data at rest.
Data in motion continuously describes a situation in real time. This data state primarily comes from fluctuating or non-constant sources. Edge devices monitoring data produce an output that is often small but constant. Frequently, data in motion variables describe high-risk processes or low-threshold systems. For example, pressure gauges in waterlines being monitored require continuous readings to ensure the pressure does not exceed or drop below an accepted range.
Data in use is interpretation and processing. This information has been collected and does not require an immediate response. Rather, data in use belongs to deliberation and analysis, answering the questions of diminishing risk or improving performance. Data in use would be a machine condition monitoring report that outlines patterns of downtime in an essential asset and predicts maintenance schedules.
Data at rest is information at rest. This information may be stored on network attached networks, external hard drives, USB sticks or even RAM. Data in rest may be converted to data in use, but it largely concerns archives or audit reports.
IIoT Monitoring Software UX for Data in Motion
Industrial software designed to support data in motion monitoring requires many features, but foremost is an effective, user-friendly notification system. Notifications may be automatic and delivered by the system. An example of this warning would be a system text message indicating high pressure on a valve sensor. Notifications may also be manual and dictated by users. In this case, consider an in-program messaging platform highlighting devices that appear to be off kilter.
An IIoT notification system must be accessible and intuitive with seamless display. The software needs to make configuring the notifications quick and easy. Menus that open over other forms, input fields that populate oddly or pages with awkward breaks are all issues that could make configuration or reading difficult.
As well, these notifications need to be sent with confidence that they will arrive at the specified when and to the identified whom. Thus, having programs that can operate over cellular or Wi-Fi and deliver in-platform, via e-mail or to mobile devices is essential when working with industrial monitoring. However, notifications must also be sent with accountability, requiring verification and authorization windows. It’s annoying, but sometimes it’s good to be asked, “Did you mean to send a warning for ‘catastrophic meltdown’?”
An example of a remote monitoring program that did not have a good notification system UX is the Hawaii Nuclear Warning System. The software program was outdated and required no verification. It was also not intuitive, with incorrect color coding, and had buttons with drastically different outcomes too close together. As a result, a state-wide panic occurred when someone sent a catastrophic warning instead of a simple test text message across all available media channels.
IIoT Monitoring Software UX for Data at Rest and in Use
The UX requirements for software handling “inactive” data states are much different compared to active data software. Because the monitoring solutions provided by these systems are deliberative and analysis heavy, these programs need to have plenty of features. However, an excess of features can be just as frustrating to users as not enough features.
Because reporting is a major component of predictive maintenance or operations optimization, features that allow users to be creative with data are a plus. These may be things like multi-field analysis, macro and micro time overviews, comparison charts, exporting tools, modeling or other reporting components. As AI-enhanced software becomes prevalent, choosing a software that has some native interpretation features or language generation abilities could be beneficial when working with hourly or daily reporting.
Another necessary feature in this data at rest software is cloud accessibility. Although much of the processing and storage may happen on local, rugged embedded computers or servers, virtualization is a common alternative. Virtualization allows use of remote servers or computers capable of higher speeds or can handle massive data sets. As well, cloud accessibility serves data-at-rest for archiving or historical analysis without requiring hardware-heavy computing stations. Cloud computing, when done on a client-tailored program, can also add layers of security. Through authorized access, minimal end points and encryption, cloud accessibility can make sure processing happens in a safe place.