With cognitive anomaly detection and prediction based on unsupervised learning, you can have your cake and eat it too: Analyze more data in less time with higher accuracy.
Often, organizations are faced with contradictory challenges—increase productivity, but eliminate machine failure… enhance quality, but reduce time to market… predict all potential issues, but quickly enough to take corrective action. A closer look at these challenges usually reveals that business and technology are in opposite corners of the wrestling ring. And however practical both perspectives may be, addressing them simultaneously is incredibly challenging—if not impossible.
The thing is, many of us are programmed to assume that we can’t solve more than one—or one type—of problem at a time. So the gut reaction, when faced with a dilemma, is to choose one or the other side, automatically relegating the other side as less important in the process.
But what if both sides are equally important? For instance, there’s really no choosing between business and technology—neither is more important than the other. Both are equally essential to run a business. How then does one choose one over the other?
The truth is, until the advent of the Industrial Internet of Things and machine learning, it was necessary to choose between business and technology. Though anomaly detection existed before, it was manual—which meant that it was time-consuming and prone to error due to its subjective nature. With the advent of cognitive computing, however, things have changed. Data scale, time constraints, accuracy of results… all these have now become issues of the past.
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? That:
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.
What does all this boil down to? That you can now have your cake and eat it too. You can now:
Learn more about exactly how cognitive anomaly detection and prediction solves diverse challenges—download this infographic to see it in action.
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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