We’ve long established — here on Industrial IoT News, and over on our IoT Evolution World news hub — that “connectivity is the lifeblood of IoT” and that still rings resoundingly true today.
In that vein, we could also go as far to say that data has become the lifeblood of digital transformation; broadly so, in fact.
See, businesses looking to transform their digital operations — thereby empowering their teams to succeed in innovative ways — need reliable, comprehensive and unbiased data to truly carpe diem, so to speak. In 2025, so much of this data goes part and parcel with insightful analytics capabilities and the usage of AI models that help drive business-building (and industry-spanning) improvements. Without the right data, systems remain limited (if not flawed) and human output is hindered. Without data optimization-focused processes in place, projects don’t reach the level of modernization many business leaders have come to desire.
With this in mind, let’s refocus attention on AI and machine learning, in reference to smart data utilization.
In IIoT (and the overarch of IoT, largely), AI models can pinpoint operational anomalies and quality issues far quicker than humans alone can. (We work hard. AI works fast. Two things can be simultaneously true.) This speed is crucial for maintaining safety measures, limiting undue downtime, staying competitive, etc.
However, the data fed into these systems (i.e. to train and refine) isn’t always quote-unquote “neutral.” Those training and refining them may unintentionally inject their own biases into the process, shaping the AI’s “understanding” in ways that may not ultimately prove productive. Naturally, this shouldn’t be the path; skewed results and limited potential there await, rather than meaningful impacts made reality by powerful tools.
A critical challenge, then, gravitates around how we empower AI and machine learning to, as one source put it, “transcend human biases” and stay the right course. The answer to said challenge involves genuinely relevant data — information that drives improvements in production and quality, lest teams risk avoidable degradation of their efforts.
So, what do you think? Are we on the money here? Or does this sound perhaps vague or not quite as fleshed out as readers may want?
Well whichever way you’re leaning, there’s a great opportunity to take advantage of. Hear me out:
At the upcoming Industrial IoT Conference (along with IoT Evolution Expo, both of which are part of the overall #TECHSUPERSHOW experience), there will be a conference session that we believe will prove both interesting and insightful to the show’s attendees. It is titled “Analytics and AI: Machine Learning Thinks for Itself,” and it will be led by Carl Ford (read his latest IIoT article here) and Radix IoT’s Michael Skurla.
We encourage you to join Ford and Skurla on Tuesday, February 11 (a.k.a. one week from today) from 1:30-1:55 PM. That’s the when of it. The where — the location of the whole #TECHSUPERSHOW — will be the Broward County Convention Center in Fort Lauderdale, Florida. Before and after their session, folks will have the opportunity to register for other must-attend sessions, explore the show’s recently expanded (and robust) exhibit hall, and take part in unique networking events.
Consider registering here. We hope to see you next week!
Edited by
Greg Tavarez
