Collecting your IIoT data is only a start. How are you getting value from it all? A cognitive solution can help you detect and predict issues without false alarms.
Sure, you’ve implemented IIoT on your shop floor. Sure, it’s raking in the data. And sure, you now feel secure, knowing that all your machines are turning in their daily, hourly, secondly timesheets. But let’s take a minute right here to evaluate exactly how secure you should be feeling. Let’s do that with a couple of questions:
• How valuable is the data you’re generating?
• Is the data analyzed? What’s it analyzed for?
• Is any further action taken post data generation/ analysis?
The truth is, data is only useful if it generates value. But how exactly can data generate value? This is where most manufacturers draw a blank—and it’s also where it becomes painfully apparent that IIoT implementation today is at best, partial. Like a knee-jerk reaction, or something that wasn’t thought through. So what’s the way forward? It lies in identifying the signals hidden in the data.
It isn’t as weird as it sounds. Think of all the IIoT data you’re generating as a route map you’re trying to draw, but you have no idea where you’re going. In the case of IIoT, it’s worse—you can’t stop the drawing, it happens automatically. Where then is the direction it needs for you to be able to make sense of it?
That’s why it makes sense to work backward with it—rather than trying to figure out what the data is trying to say (and you can really have no clue about that), the trick lies in looking for indicators of chosen business outcomes. For instance, if you want to enhance productivity and machine uptime and minimize maintenance costs, you look for all data corresponding to these outcomes, and then identify the flags (if any). In other words, pattern recognition can take place only once you’ve decided on your outcomes, and this is also what minimizes the appearance of a false alarm.
Cognitive anomaly detection and prediction makes use of unsupervised learning and pattern recognition to facilitate outlier detection—which identifies the relevant signals in your machine data. The use of unsupervised learning ensures that you identify not just the “known unknowns,” but also the “unknown unknowns.” What does all this mean?
Cognitive anomaly detection and prediction thus helps filter out machine noise for signals relevant to your business objectives, and also identify future anomalies—facilitating the implementation of proactive, prescriptive measures.
So how exactly does cognitive anomaly detection and prediction work for you? Download this infographic to find out.
See the Infographic
Anita Raj is a Product Marketing and Growth Hacking Strategist on the Progress DataRPM team, with over 10 years of experience working in the field of big data, cloud and machine learning. She brings a deep expertise in running growth marketing leadership experience at multi-billion-dollar enterprise companies and high growth start-ups.
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