As a British expat, I am obsessed with the weather back home. When I see headlines about frozen London it brings back all the excitement of waking up to “snow days”. In my last job, those were the only days I was allowed to work from home without questions being asked.
So I was really interested to read about Yahoo CEO Marissa Mayer’s decision to force her staff to come to the office because the metrics convinced her that the Yahoos (really?) weren’t working hard enough. Apparently they weren’t logged in to their VPNs as often as they should be. How short sighted can you be? Slacking off so hard when working from home that you can’t even be bothered to log in? If I was one of those conscientious slackers who diligently logged in and played minesweeper all day, I would be truly annoyed.
You may agree or disagree with Mayer’s decision. Personally, I don’t have a useful view because I don’t have enough information to have an informed opinion. I do have my personal opinion based on my own experience; people who work from home tend to spend most of their day building disappointingly wonky snowmen.
What I am impressed with is Mayer’s willingness to make a difficult decision based on data, not least because I guess she didn’t have the convincing snowman evidence to hand that I have. I am also going to guess that she knew that there would be some people with genuine reasons to be working from home and who were productive, but she took the decision that this was a change that Yahoo needed to make in difficult times.
Too much information
In an effort to reduce the risk of a making a wrong decision, librarians are often guilty of trying to pin the world down with numbers which are so far removed from our experience that we can’t trust them to inform our point of view. The most natural response to something so complex is to pick holes in the data as a reason not to use it , or to bluff our way through and take the lowest risk action possible. In most cases, we put a huge amount of effort into maintaining the status quo.
Karen talks about “not knowing” and, contrary to the way many people think about data, this is what working with numbers can be all about. Being comfortable with uncertainty; if we want to persuade people of the case for change, the evidence we gather doesn’t have to be complete or cover every eventuality so long as it is comprehensible and comes with a point of view.