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This is entirely possible. However, when the data being measured is about complex human behavior (i.e. time allocation, productivity, work habits, etc...), I don't think we are even a little bit close to accurately representing reality. If we were "pretty close," then sure you could justify making decisions based on that data, but I don't think we are close enough for that. Even just the "time worked on X" example is too complex to track. It is deceptively "simple." It seems like (especially to managers) employees should be able to work on a task for awhile, and afterward record how much time they spent working on it. However, it isn't that simple. In reality, "working on X" might actually mean working on X along with several other things such as email or web browsing or talking to a co-worker or answering the phone. With reliance on self-reporting, and without some sort of monitoring system, it is unreasonable to expect this metric to be accurate, yet this is how many (most?) time reporting systems work. Managers are making large-scale decisions based on this data. It looks like accurate data, it has fancy graphs and charts and reporting...but it isn't actually very accurate. An employee might report an hour spent on a project, when really only 40 minutes of that hour were spent on the project work, while the remaining 20 minutes were spent on various interruptions and extra tasks. This inaccuracy isn't much of an issue for informal uses, such as sticking to a personal schedule, but for driving decisions as part of a greater pool of data, it is misleading. At my organization, upper management is trying to use time reporting data to come up with a total cost for various initiatives. This is the kind of scenario I am talking about. Managers don't typically sit and watch everyone work, nor do they discuss time reporting entries individually, so all they end up seeing is the data. This separates them from how that data is generated and leads to inappropriate reliance on that data. From their perspective, it feels like reliable data that can be used to assign a cost to various projects, but the data probably doesn't adequately support that use case. |
But sometimes there are ways to make it work. At Pivotal Labs for example, you are allocated to a pair daily and then to a project or product over a few weeks. There are no interruptions or meetings beyond townhalls and daily stand ups... Unless you and your pair occasionally want a ping pong break. Productivity and resourcing becomes a bit more reasonable in this culture as its related to stories/requirements in Pivotal Tracker being completed relative to the number of pairs rather than tying it to individual worth. It's like an incredibly kind and collaborative culture that also gets good data.
Stepping back to a general management perspective, To me the most effective data is if you keep it relatively simple and do controlled experiments that are systemically tied to an external market result. A lot of companies in the logistics (and now software services) space get good at this.