We all have situations where we wished we could instantly analyze data in unstructured formats. For example machine log data or web log data, or log data from applications. Over the years tools for analyzing weblogs (for example Google analytics) and most recently some machine data search tools( Splunk) have made the task easier. However there are files with multi-structured data with each section having multiple formats. Further in many enterprises, log data need to be combined with other structured data to make sense. For example support logs from devices or software applications are typically associated with CRM and installed base data such as tickets, bug numbers, customer and case information etc. There is a need for a way to not only structure this data but associate existing structured data with machine data. Glassbeam has been doing this for some time now with large enterprise customers.
Our overall approach is using our patent pending SPL™ for defining the semantics & semiotics of the unstructured data and creating a platform and set of applications that can then create a normalized structure and a pre-defined set of applications to visualize that data.
As an example lets say you want to see all customers on a particular version of your product with a specific known defect and you want the trends of how often this defect as affected other attributes of the product such as performance over time and you want to create rules to alert any time such events happen. This involves parsing machine log data, combining it with a knowledge base and integrating with a CRM application. Glassbeam dramatically reduces the time to deliver this business value by providing pre-built platform components as well as a pre-defined yet configurable set of applications to report on such data.