There is a growing field called auto-analytics. Essentially it is a way to measure activities and events in real time by automatically recording the result. Lots of athletes use the technique. If you have ever watched the Tour de France you probably know that the riders are covered with wires that constantly measure everything about them and their bike. This kind of feedback helps them find maximum performance thresholds and helps them adjust when things are off.
The growth of cell phones and other simple personal electronic devices has made auto-analytics more accessible to everyday people. But the growth of the technique goes beyond the measurement of physical endurance. It can be used to track work performance as well. H. James Wilson in his recent HBR blog stated:
There's also potential for business leaders to champion expert-run communities in-house, though ensuring the privacy and security of participants' data needs to be the first design principle. For instance, hundreds of employees at a successful global technology company I studied participate in auto-analytic experiments to find correlations between knowledge work productivity (the thinking self) and improved health (our physical selves). For instance, some participants are seeing if treadmill desks measurably boost output as they write computer code or marketing copy. With strict research protocols in place, every employee who chooses to participate has her data anonymized and aggregated. As a result, staff researchers have a radically new source of data to inform the way the company should support and innovate knowledge-work. The Social Side Of AnalyticsHe goes on further to state:
This heralds an important shift in how we think about tracking work performance and even career planning. Employees have long been measured, but managers have traditionally chosen the tools and the metrics—and, more important, decided how to interpret the findings. With auto-analytics, individuals take control. They can run autonomous experiments to pinpoint which tasks and techniques make them most productive and satisfied—and then implement changes accordingly. You By The Numbers
One of the really interesting things about auto-analytics is that what we generally think is the case often is not. We might think we are working as hard on the bike as we can, but the data shows otherwise. This holds true for the workplace as well. We often have beliefs about what conditions at work make us work better – but the ability to actually see and measure it may show something all together different.
I think you will see a lot more happening in this field over the next few years.
I, for one, am looking forwarding to proving that my intake of dark chocolates is directly related to the quality of my output.