LogRhythm for Business Intelligence
The value of information that can be derived from log data is no longer restricted to the traditional applications of operations, security and compliance. Through the use of extended metadata fields that capture such data points as quantities, amounts, session, bytes-in/bytes-out, file size, etc., the insight that can be gleaned from log data can be highly impactful to other areas of the business.
Whether your business is in online retail, financial services, insurance or any other industry, there is likely a wealth of business intelligence information sitting in log data just waiting to be tapped.
Consider the following sample use cases:
The operations group of your online retail business is capturing web server logs that, if properly analyzed, could tell you the average time it takes potential customers to receive an updated quote once they modify an order. That log information could be correlated with other web server logs to determine the average point at which customers leave your site because the refresh rate for a new quote is too long. Such insight could be invaluable when conducting IT resource planning for that ecommerce infrastructure and maximizing sale conversion rates.
The marketing department of your financial services firm may be interested in knowing if there are common actions taken by your customers immediately preceding or following stock transactions (e.g., do most customers perform stock research on your site immediately following the sale of an existing position? If so, you may consider highlighting the benefits of a specific alternative investment vehicle on the screen that follows the transaction confirmation screen). Logs from your online trading system could hold the answers.
A LogMart trending report of logs from the automated claims processing server of your insurance company highlights that response times from that server have tripled over the last three weeks. Reviewing the IP telephony server logs for the same period shows a similar spike in inbound calls to your customer service department. Armed with this correlated insight you drill into the log data to investigate and discover the root cause of the delayed response time. You fix the problem, response times drop back to acceptable levels and you experience a substantial reduction in calls to your customer service group. Once again, log data holds the key.
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