As License Statistics has become a fast and stable tool, our technical experts have now focused their attention on providing greater accuracy and a higher detail level of the data gathered. Over the past few days development has successfully improved the algorithms used for computing the data that is later presented in License Statistics Reports. Let’s take a look at an example scenario to understand the change:
User John begins working at 8:00. He starts up his application and works until 13.00, the time when he goes out to lunch. Afterwards, he attends a meeting and finally returns to his desk at 15:00. He then starts up a second application and checks out 2 more licenses. And finally at 16:00, he finishes work. Presented below is a graph of his license usage:
Looking at the graph we can see that user John had licenses checked out for a total of 6 hours (from 8 to 13 and then from 15 to 16) and his highest usage of the day was 3 licenses. Though this information is valuable, it is, however, incomplete. What we really want to know is how long was John using these licenses, that is what fraction of license time available to the company did he use up. So, what we need to see is:
This is the value that is relevant because it is exactly what we are paying for. In other words, if we purchase 10 licenses for a month, we’re paying for 10 licenses x 30 days x 24hrs = 7200 license hours. And naturally, the goal is to utilize as much of these license hours as possible. Otherwise, we’re wasting money on licenses we’re not using.
For this reason, our updated algorithms compute license time weighted by the number of used licenses and thus providing finer granularity of license usage information that is more valuable to you. And though not all this information is yet visible in the GUI, it is continuously being gathered and stored in the License Statistics database and will soon be accessible through the reports, which we’re putting together for the following releases.
User John begins working at 8:00. He starts up his application and works until 13.00, the time when he goes out to lunch. Afterwards, he attends a meeting and finally returns to his desk at 15:00. He then starts up a second application and checks out 2 more licenses. And finally at 16:00, he finishes work. Presented below is a graph of his license usage:
Looking at the graph we can see that user John had licenses checked out for a total of 6 hours (from 8 to 13 and then from 15 to 16) and his highest usage of the day was 3 licenses. Though this information is valuable, it is, however, incomplete. What we really want to know is how long was John using these licenses, that is what fraction of license time available to the company did he use up. So, what we need to see is:
5hrs x 1 license + 1hr x 3 licenses = 8 license hours
This is the value that is relevant because it is exactly what we are paying for. In other words, if we purchase 10 licenses for a month, we’re paying for 10 licenses x 30 days x 24hrs = 7200 license hours. And naturally, the goal is to utilize as much of these license hours as possible. Otherwise, we’re wasting money on licenses we’re not using.
For this reason, our updated algorithms compute license time weighted by the number of used licenses and thus providing finer granularity of license usage information that is more valuable to you. And though not all this information is yet visible in the GUI, it is continuously being gathered and stored in the License Statistics database and will soon be accessible through the reports, which we’re putting together for the following releases.
