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There are valuable insights buried within your data, but they can be virtually impossible to find manually. Enter unsupervised cognitive learning.
Every time a data scientist spends hours immersed in data, wrangling and tweaking a mathematical code or script fragments, the dream of data science and machine learning bringing agility to your organization seems to retreat. Manual data science for industrial processes can be extremely counter-productive, especially when businesses embracing the IIoT are greatly emphasizing superior dexterity in operations. Any opportunity which can speed up delivery and output should be seized by every aspiring industrial manufacturer.
One of the biggest challenges for the industrial digital enterprise is extracting precise outcomes by channeling huge volumes of data into meaningful information, and then performing accurate analyses to streamline business processes. What the industry is looking for is a transformation from ‘sensor to insight to outcome.’ So how does this trickle down in the real industrial context?
The future of the IIoT involves billions of connected ‘things’ which will generate trillions of gigabytes of data, all of which will create a market of trillions of dollars. At the same time, the IIoT has created a massive upswing in data volume due to the increased granularity of the data being produced.
Today, data science and machine learning professionals are faced with a daunting challenge. Finding extremely rare anomalies is itself a hard task, and their rarity makes analysis quite difficult as well. Now consider the data context in which this is all taking place. Detection of these sporadic events in such humongous data sets by conducting manual in depth analysis and visualization of very large data volumes is the equivalent of looking for a needle in a massive haystack.
Further, deeper questions regarding the data itself need to be answered. These include checking if the data is relevant, if it is of sound quality and if the model produced by an algorithm translates from a mathematical relationship to a causal one.
To capture the significant gains from machine learning & the industrial IOT, companies must automate underlying processes that will help them scale with ease for ‘insights.’ This becomes even more complex as it is the unusual or unexpected data gaps that can be the actual trigger for an equipment failure. This is where traditional data science within the IIoT fails.
Most traditional industrial organizations rely on supervised learning, which is not only extremely limited in scope but is not scalable with the pace of industrial growth. Today’s complex industrial systems need a robust unsupervised learning format which can teach machines how to self-learn from past experience.
That’s where unsupervised learning combined with cognitive predictive maintenance comes into play. By combining these elements, the improved machine learning techniques can help companies achieve a far quicker time to market. This kind of approach is imperative to be competitive in a cognitive-first world.
This is not merely about agility, but converting this speed into valuable decision making using an unsupervised learning structure. This is precisely how our Cognitive Predictive Maintenance (CPdM) platform works to help save thousands of valuable hours, automating tasks done by data scientists to make them vastly more efficient. By getting predictive models ready in days using cognitive machine learning rather than through months and weeks of manual testing, companies can easily translate the real value of that agility into critical decision making. This generates savings in both time and resources, getting you to market faster and with greater precision.
We’re not discussing the distant future—the technology to do this is already here. Curious to learn more? You can find out more about how our CPdM solution can help you here.
Learn More about CPdM
Sundeep is a highly accomplished Data junkie, innovator and entrepreneur with over twenty years of experience in delivering insights and advanced analytics. Known for his what-if mindset, he co-founded DataRPM, a Progress company, with the goal of providing a platform that delivers hyper-fast cognitive data products to organizations challenged by the volume, velocity and variety of their big data and machine learning.
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