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  • Daniel Sarfati

There is a big difference between tracking employees and analyzing activity logs.

Updated: Jan 11


Tracking people activity is a big no-no.


No one wants to feel there is a big brother looking from behind their backs. It’s been tried many times, using RFID embedded in badges, with cameras both hidden and in plain view or by other means. And it doesn’t work for two reasons: first, active tracking has a conscious effect on people behaviors and second, no one can really make any sense of the collected data. It certainly doesn’t help promote good vibes in the workplace, and it is an illegal practice in many countries.


On the other hand managers have the duty and the right to oversee how the people reporting to them are doing, when they check-in in the mornings and when they check-out in the evenings. But also to understand if they are committed, interested in what they are doing and if generally happy or if they are distracted or frustrated instead. A task that has become challenging if not impossible when a lot of us is smart working, visiting offices on and off and in general when the daily human interaction is largely absent.


Under the "new normal" it is legitimate to take a look at activity logs to understand what is going on, beginning with attendance and including any other entry logged into software applications.


Activity logs are by and large metadata and as such they don’t contain sensitive information. They are automatically collected by most software applications and are free to use with no cost. Unlike employee's questionnaires activity logs analysis is non invasive and as such has no influence on measured behaviors. Activity logs provide a very reliable and granular picture of what people do at work.


Activity logs provide an automatically normalized baseline to compare people activities not only among peers but also for the same person over time. They can be interpreted as a clear signal of a person happiness or frustration and can usefully be related to productivity and effectiveness. When correlating activity data with significant events, the trends measurable through activity data can be used to predict frustration and even dramatic outcomes such as someone's intention to resign.


Sophisticated AI algorithms can be trained to find recurrent behavioral patterns and predict in advance future events so that managers can be tipped on possible issues and negative outcomes can be mitigated, improving happiness and effectiveness at work.




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