Using the Productivity BI Report, you can view the most and least productive users in your organization. You can also build behavior policies and rules and anomaly rules to detect user behavior affecting productivity.
Here are some examples:
1. Conduct Productivity Analysis with the BI Reports
Here’s an example of a customized BI report that shows a snapshot of the overall productivity of an organization:
Each widget is configured to measure particular data points important to the organization. For example, in addition to showing the top productive/unproductive employees (by their activity level), there are other widgets that show departmental productivity, productive/unproductive tasks and projects, monthly trends and identifying which specific apps or websites are least productive in a day-to-day basis.
The report can be customized with detailed filters - for example, so that you can focus on specific dataset(s) that interests you - filtering out any noise. For example, in this case we wanted to focus on some specific departments and eliminate any tasks that don't concern us for this analysis.
At the bottom, a Grid widget shows us details of all the selected data points. We can easily export this in Excel or other analytics apps for further analysis.
2. Detect Productive/Unproductive Behavior Automatically
Using behavior rules, productive and unproductive activities can be flagged automatically:
In the above example, two behavioral rules are shown. The first one flags when employees are focused on a particular job (app/website) or active on approved apps/websites for too long.
The second example detected various schedule discrepancies such as if employees arrive late, work for less hours than scheduled or idle for too long.
3. Detect Productivity Anomalies & Drip Incidents through Autonomous Baselining
You can also create anomaly rules to automatically measure continuous productivity trends against an automatically calculated baseline:
Anomaly Baseline uses an algorithm to determine if certain user behavior is outside a baseline. This can be the user’s current behavior compared to their past behavior; an employee’s behavior compared to their departmental baseline; or an employee’s behavior compared to the baseline of the entire organization. Using a baseline lets you, for example, set an anomaly rule to notify you when a user’s activity drops than their normal regression in a day-to-day basis.