As a follow up to his presentation at the 2013 itSMF Norway conference, Stuart Rance of HP has kindly donated some practical advice for those struggling with availability.
Many IT organizations define availability for IT services using a percentage (e.g. 99.999% or “five 9s”) without any clear understanding of what the number means, or how it could be measured. This often leads to dissatisfaction, with IT reporting that they have met their goals even though the customer is not satisfied.
A simple calculation of availability is based on agreed service time (AST), and downtime (DT).
If AST is 100 hours and downtime is 2 hours then availability would be
Customers are interested in their ability to use IT Services to support business processes. Availability reports will only be meaningful if they describe things the customer cares about, for example the ability to send and receive emails, or to withdraw cash from ATMs.
Number and duration of outages
A service that should be available for 100 hours and has 98% availability has 2 hours downtime. This could be a single 2 hour incident, or many shorter incidents. The relative impact of a single long incident or many shorter incidents is different for different business processes. For example, a billing run that has to be restarted and takes 2 days to complete will be seriously impacted by each outage, but the outage duration may not be important. A web-based shopping site may not be impacted by a 2 minute outage, but after 2 hours the loss of customers could be significant. Table 1 shows some examples of how an SLA might be documented to show this varying impact.
BI professionals spend a significant portion of their time trying to instill the discipline of datadriven performance management into their business partners. However, isn’t there something wrong with teaching someone else to fly when you’re still learning to walk? Few BI pros have a way to measure their BI performance quantitatively (46% do not measure BI performance efficiencies and 55% do not measure effectiveness). Everyone collects statistics on the database and BI application server performance, and many conduct periodic surveys to gauge business users’ level of satisfaction. But how do you really know if you have a high-performing, widely used, popular BI environment? For example, you should know BI performance
Efficiency metrics such as number of times a report is used or a number of duplicate/similar reports, etc
Effectiveness metrics such as average number of clicks to find a report and clicks within a report to find an answer to a question and many others
Metric attributes/dimensions such as users, roles, departments, LOBs, regions and others