Most progressive support organizations are now moving to leverage Big data to become proactive. They want to put behind the days when support teams were always behind the curve, with the customer knowing about problems much before support knows about it. Further its takes days or weeks for support teams to understand what is going on based on logs uploaded. Tools to analyze logs and determine possible issues have helped but they are mostly single user tools which help highlight simple keywords and when a log file consists of bundles of files with multiple sections and formats a simple search does not help.
According to a recent survey among the support groups of 3 of the world’s top selling storage vendors, it takes an average of 12 hours to identify and resolve any issue. Of this, up to 30% of the time is spent in determining root cause - finding, organizing, and making sense of the glut of diagnostic data coming back from the product.
At Glassbeam we have been working on some very interesting solutions that dramatically improve support productivity. What if you could parse multiple sections of a log file or a set of files, whatever format they in, and apply business rules on data within the file to determine if there were known issues. As an example – say your customer uploads a log file into you salesforce.com instance( or whatever CRM you use) while creating a case. What if the log file can automatically be parsed, a quick summary of current configuration shown, rules from your knowledgebase applied to the file to determine possible issues and even recommend solutions based on previously known cases? That's a potential savings of 11 hours and 55 minutes per case!
At Glassbeam we are applying big data to solve thorny problems for the enterprise and its executives.