February - 2012 issue > Business

By Puneet Pandit

Traditionally, we have lived in a world where technology manufacturers build, ship and install their products at customer sites and then tend to ‘lose sight of it’. There is little information on products’ actual configuration, performance, and related usage characteristics - that is on how customers are actually using different features. Today, the sources of feedback that typically relay back such information on a product’s current state could be CRM systems, Databases that log support cases, emails and conference calls with field sales engineers or customer contacts. Needless to say, these sources are archaic in nature and are unsuitable for a rigorous and timely analysis of a product’s operational state.

This old paradigm is changing rapidly where today’s world is characterized by intelligent, networked products that routinely generate and send back machine data to a centralized location. This data (sometimes referred to as “call-home data” or “support bundles”) contains valuable information about performance trends, defects, configuration changes, and on the state of the installation at client sites. If harnessed correctly, it contains information that can provide immense value to an organization -- helping them resolve support issues more efficiently, building smarter product roadmaps, understanding the extent of exposure to a product defect, creating new service revenue opportunities, and in enhancing overall product differentiation in the market place.

Acting on this market need, Glassbeam has developed a new, patent-pending technology to collect and parse product operational data, and to present deep analytics to product manufacturers in a compelling, graphical format. The platform lets one define a scalable data warehouse from any kind of semi-structured data in a single development step, at a fraction of the cost, that would otherwise take multiple people number of months to execute as a complex IT project.

There are three broad functional areas that can greatly benefit by an analysis of log file data emanating from devices in the field:

1. Support

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