By: Eric Siegel, Founder, Predictive Analytics World
In anticipation of his upcoming conference presentation, Applied Predictive Analytics to Workload Automation, at Predictive Analytics World Chicago, June 8-11, 2015, we asked 
Arcangelo Di Balsamo, IBM Workload Automation Chief Architect at IBM, a few questions about his work in predictive analytics.
Q: In your work with predictive analytics, what behavior do your models predict?
A: As the Workload Automation architect, I'm applying predictive analytics mainly to IT Automation-focused use cases. Those use cases are about:
1) Predict job duration using history
2) Resolve issues quickly by learning from the past
3) Improved insight by detecting relationships across resources
4) Optimize performances
Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?
A: In my case, predictive analytics is delivering value to the product I develop (it's a workload automation platform which includes also a job scheduler). The predictive analytics is adding to the product a reliable Operational Level agreement monitoring capability (such as being sure that a critical workload completes within a given deadline). Moreover it provides the knowledge required to reduce failures by discovering hidden relationships between jobs and resources, so that if a resource is failing, it's possible to also predict the failure of the job bound to it.
Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?
A: When you run a million jobs per day, a failure rate of 1% brings to 10.000 failures per day. Imagine how expensive it is to find the root cause of all of these and take the remediation action! We have customers that with the enhanced predictive capabilities in the product have reduced the failure rate to 0.01%, which is a huge operational cost savings for them.
Q: What surprising discovery have you unearthed in your data?
A: Frequently, we discover recurring patterns in job duration (i.e., a job taking longer at the end of each month) that are not obvious. We also discover correlations between job failures and resource unavailability (like job1, job2 and job3 fail when the RDBMS is not available).
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.
A: Predictive analytics applied to workload automation is a tremendous opportunity to better know what's happening within your data center in order to reduce costs and prevent failures and delays in your critical business applications.
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Don't miss Arcangelo Di Balsamo’s conference presentation, Applied Predictive Analytics to Workload Automation, and workshops at Predictive Analytics World Chicago on Tuesday, June 9, 2015 from 3:55-4:40 pm. Click here to register for attendance.
By: Eric Siegel, Founder, Predictive Analytics World
Eric Siegel is the founder of Predictive Analytics World (www.pawcon.com) — the leading cross-vendor conference series consisting of 10 annual events in Boston, Chicago, San Francisco, Toronto, Washington D.C., London, and Berlin — and the author of the bestselling, award-winning book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.