In a previous blog post, we discussed that granular use data supports a number of use cases to justify the cost of implementing and maintaining the data collection process. This post will focus on the value of trending data and how it can apply to short-, medium- and long-term planning. We’ve selected three use cases based on analyses we’ve had from customers and prospective partners. These are:
- Trending analysis for cause/effect
- License use ranking
- Service level planning
Trending analysis looks at how a measurement, such as application use, changes over time. For long-term planning, managers look at a long-term trend line, perhaps 3-6 months, to average out use peaks and valleys. For short-term analysis, analysts want to look at the spikes and troughs of use to understand the specific conditions that caused each. Spikes represent high water marks for licensing, and troughs represent relative excess license availability.
To be able to accurately understand short- and long-term use, managers need to know who (individual users and groups) was using licenses, what the state of the licenses were (active, inactive or timed out) and what the project(s) were that drove use. Depending on the vendor’s licensing model(s), peak or trending use may need to be subdivided into the different licensing models, such as perpetual vs. subscription, or Suite/different functionality packaging. Long-term trending analysis tends to look at how different components (groups, license types, etc.) change as a percentage of the total.
Short-term analysis tends to be driven by a peak or otherwise identified event. Managers then identify what caused the anomaly to see what triggered it, how repeatable it is, and whether the conditions that precipitated it can be changed to reduce impact frequency or peak impact in the future. Short-term analysis also tends to require more flexible analytic capabilities, as managers and analysts may have no idea what caused the anomaly.
Ranking provides the ability to separate aggregate data and users into informal groups. It’s easy to look at logical groupings, such as use by offices or work groups, but it’s often more helpful to be able to create ad hoc groups, such as by title type, or application use, to identify underlying differences in use that are driving long-term trends. Typical analysis could include ranking users by frequency or total use duration, and then comparing use within and between groups, titles, projects, applications used, etc. to see if there are underlying relationships that otherwise wouldn’t be visible.
Trending data analysis also is helpful in providing service-level planning. Service-level planning is identifying the minimal acceptable level of license availability and what can be done to avoid hitting that low point. If you know what your overall use trend line is like, and you have identified the key drivers or characteristics of use, it’s relatively easy to project when your service levels will be affected. Ranking use by user title or application type lets you identify in advance the impact of new hires, or changes in projects you pursue. You don’t need to wait for angry users complaining about license unavailability to provide cost justification. In addition, you can take appropriate actions to delay obtaining additional licenses, set expectations of frequency of license unavailability, or visibility into license use to help minimize the impact of unavailable licenses when 100% license availability is not the goal.
Cetrus is incorporating a Business Intelligence/Analysis solution to provide embedded business information to support both long-term and short-term trending analysis and visualization. We’re happy to answer your use questions or provide a trial if you’d like to see the data Process Meter can generate, as well as analytic capabilities the solution provides.