Traditional machine learning solutions fail at extracting value for most organizations. Here’s how you can use—and profit from—all your data effectively.
Machine Learning and AI have been generating a tremendous amount of hype for years now—it was 2011 when IBM Watson famously won Jeopardy—but most organizations have not benefited significantly from analytics yet. All the ingredients are in place, with incredible amounts of data being collected, more compute power available than ever and continually improving algorithms… yet gains remain limited.
The problem is that scarce data scientist resources can’t keep up with the number of complex and time-consuming tasks required to build and fine-tune analytical models. When new data is available, additional data scientist work is often required to update the models. With so much data available and everybody wanting to make the most of it, a lack of data science resources is the primary obstacle preventing organizations from successfully realizing a major benefit.
To solve this problem, Progress has set out to automate the hardest and most time-consuming aspects of the data science lifecycle. By enabling easier and more efficient analysis of data through automation, we turn the problem on its head—the more data you have the easier it is to analyze accurately, not harder.
Traditional Methods Leave Massive Potential Untapped
One area that analysts agree is primed to deliver benefits from newly available data is in the IIoT. Manufacturing, Oil and Gas, Automotive and many other industries are equipping more and more machines with sensors, and the successful leveraging of this data is estimated to provide billions of dollars of impact. But traditional approaches have not achieved this—not even close. The problem is the manual approach required by typical solutions.
To provide accurate results, these solutions require models to be built and maintained—manually—for every individual asset, increasing the cost and complexity of managing each model as well as the data that comes from every individual asset. The problem continues beyond the initial model and deployment stage too, because the models must be continually updated to adjust to dynamic environments. Throwing data scientists at this challenge is not a viable approach for most organizations.
Cognitive Anomaly Detection and Prediction for All
Our approach at Progress is different. We made it out goal to create a solution that is automated, massively scalable and self-learning. We use unsupervised learning to automatically label data, and take into account individual asset behavior, operating patterns and environmental conditions for every asset. Using automation and a digital twin approach, we create a model for each asset, and refresh these models in real-time so that they remain accurate even when situations change rapidly.
With Cognitive Anomaly Detection and Prediction (CADP), organizations can get early warnings about likely breakdowns and act accordingly. Repeated failure conditions are identified, but CADP also reveals the “unknown unknowns” by uncovering anomalies not previously discovered. This approach reduces downtime, improves performance and increases operational efficiency, all while reducing the burden on your data science and IT resources.
Unlike other solutions, CADP is automated, self-improving thanks to our patented Meta-Learning technology, and built on an open architecture. Perhaps best of all, it can deliver results in just days, rather than months.
Ready to learn more? Find out how you can profit from your data today.
Mark Troester is the Vice President of Strategy at Progress. He guides the strategic go-to-market efforts for the Progress cognitive-first strategy. Mark has extensive experience in bringing application development and big data products to market. Previously, he led product marketing efforts at Sonatype, SAS and Progress DataDirect. Before these positions, Mark worked as a developer and developer manager for start-ups and enterprises alike. You can find him on LinkedIn or @mtroester on Twitter.