We previously addressed how corporate politics can derail an IoT project in a very quick time. But another impactful element is the human factor when it comes to IoT adoption. A minefield where projects often step on the wrong trigger, leading good people into bad outcomes. The crux of the problem isn’t deploying devices or collecting data; it’s empowering teams to interpret and act on that information.
The volume and variety of information coming from IoT networks is overwhelming. Users require foundational training not just on how to use IoT platforms. but on basic data literacy and trend analysis skills. Teams need training on how to use IoT platforms. It’s one thing to know how to generate reports—it’s another to understand why those numbers matter, how to extract knowledge. And then, what to do with those nuggets of wisdom.
It’s not enough for one person to know what happened yesterday— you need to spot long-term trends to predict anomalies. Data is streaming, so static analysis doesn’t cut it. Unparsed data dumps don’t help. Being able to visualize data through dashboards or graphs helps to make sense of patterns. It is not about becoming data scientists. You do not need a degree in mechanics to drive a car. Your driving instructor tells you in simple terms how an engine works, where the oil goes, how to turn the steering wheel and which pedal to press.
Data should not be siloed. Maintenance, logistics, management, production teams — everyone needs to know some basics to make cohesive decisions. Cross-functional training is a key element to a deployment. There is a natural reluctance to embrace new things. Knowledge empowers and concurrently dispels fear of change. In hierarchical organizations there can be an aversion to the wider distribution of real-time information outside the management cadre. Inertia comes bottom up or top down, usually it is both at the same time.
Later in the process teams should be encouraged to critique the data collection itself, developing a virtuous cycle or feedback loop, helping to “clean the data.” This was the big leap forward for Japanese companies in the 50’s and 60’s and it is a technique we could borrow from in our processes. This critical thinking, problem solving, attention to detail and domain knowledge merging brings key benefits over time.
🟧 Is this data relevant?
🟧 Is it too much or too little for our use case?
🟧 Does it do what we want it to do in this context?
This feedback creates a culture where data quality is refined and improved for your use case.
