The browser version you are using is not recommended for this site.Please consider upgrading to the latest version of your browser by clicking one of the following links.
We are sorry, This PDF is available in download format only
Reducing Client Incidents through Big Data Predictive AnalyticsIn 2013, Intel IT set a target to reduce all reported IT incidents requiring our attention by 40 percent by the end of the year. We devised a client incident prediction Proof of Concept (PoC) using Intel® Distribution for Apache Hadoop* software. Applying text analytics to millions of client event logs and thousands of client incident reports, we identified correlations enabling us to anticipate and solve client problems before they become widespread.In performing the PoC, we realized a number of accomplishments.• Developed a big data predictive analytics solution capable of deriving value from the millions of previously rarely-used Windows* event records generated daily by 95,000+ client systems• Applied advanced natural language processing and information retrieval techniques that enabled correlation of machine information (event data) with internal customer information (incident reports)• Sorted through millions of events and thousands of incidents, achieving 78-percent accuracy in predicting the occurrence of incidents in additional clients• Created data visualizations that helped IT support staff quickly determine the likelihood, severity, and distribution of a problem and more accurately target fixes and other proactive supportRead the full Reducing Client Incidents through Big Data Predictive Analytics White Paper.
Intel CIO Kim Stevenson takes stock of the accomplishments of 2013 and looks at the year ahead.
Value of visualization tools for users and IT.
Learn about the valuable resources for IT pros.
Shaping the modern enterprise IT organization.
KC Quah shows how Supply Chain IT supports application development and production services.
Intel IT’s Aziz Safa talks advanced analytics for solving problems instead of mining data.