No one is invincible!

Companies that sell complex and expensive equipment, usually do so with the promise of enhanced performance over their competition.  In order to deliver on these promises, companies usually gather performance metrics from their customers, and track these metrics so as to meet their performance goals.
If done properly, these performance metrics can allow you to watch for trends and spot problems in the following areas:

• Trends in the utilization of system resources. This information can be used to plan and tailor changes and   upgrades to the configuration of the system.
• Identification of stress on specific subsystems and elements of the system.
• Balance the use of system resources during peak and normal usage.
• Identify stress points on specific subsystems and elements of the system.
• Use historical data to accurately predict the effect of specific changes to the system.
• Identification of specific actions which may be causing problems with other activity on the system.
• Efficiently manage utilization levels and trends for available resources.

The use of simple pass/fail criteria should be avoided.  These levels can be arbitrary set to a level that is twice that of a worse-case scenario so as to provide a margin of safety.  As time passes performances levels can gradually slip until they hit that level. Since the level was arbitrarily set, it is not clear when the actual problem occurred that allowed the system to reach this unacceptable level.  Therefore, when problems occur, it is essential to have performance data from before and after the incident to narrow down the cause of the performance problem, and to find an appropriate resolution.

However, customer databases can be inflexible and hard to use.  Data input might be done manually.  Reports can be time-consuming and tedious to generate.  As a result, customer performance data can take hours to research. Instead, the input of the data needs to done automatically and routinely.  Historic tracking of performance data cannot be done if data is spotty or insufficient.  Organization of collected data needs to be flexible and dynamic.  Archives that are too rigid quickly become tedious to use.

Report generation also needs to be automated.  Real-time dashboards should be available to present data in a quick and customized manner.  Reports for important customers might look different than reports for non-critical customers.   Researching customer data should take a matter of minutes, not hours.

This entry was posted on Wednesday, June 10th, 2009 at 7:38 am and is filed under Engineering Practices. You can follow any responses to this entry through the RSS 2.0 feed. Responses are currently closed, but you can trackback from your own site.