There is a lot to feel grumpy about at the current time and as a rule I try to look on the positive side – because when things simply get too overwhelming it rarely pays to search out more bad news. One thing that I do struggle with on a regular basis is the intentional use of exaggerated language in the reporting of the current situation – even by some of the most respected of organisations.

A case in point is an article I was reading yesterday which talked about the number of coronavirus cases “soaring” in the workplace after the Christmas period. This assertion was then used as the basis for the delivery of a whole series of opinions and beliefs which clearly were the journalist’s own. A quick look at the source data showed that the number was exactly the same as in November, there had just been a temporary decrease over the few weeks over Christmas. Perhaps because more people were on leave or there were closures and shut downs?

The point I want to make isn’t about cases in the workplace, but that the language used and the selective use of data that would lead most people to believe that this was a significant problem and therefore the subsequent beliefs were based on the solid use of empirical evidence. In many ways, the imprecise use of language in this context is of little consequence, other than my annoyance. But when we extend this into the workplace we run the risk of making decisions that have implications for peoples lives.

It isn’t unusual to hear phrases such as, “everybody is up in arms”, or “we’ve been inundated by” or, “nobody likes” (the list isn’t exhaustive, feel free to add your own favourites). Normally followed by a suggestion of an action that needs to be taken…RIGHT NOW! A simple enquiry of, “Everyone?”, “Inundated?” or “Nobody?” is sufficient to start a conversation that leads to better understanding. Who exactly has a problem? What size is it? How many people are really impacted? What is the basis for proportionality?

There will be those that tell you this is the reason we need better data and analytics in the profession and of course this is entirely true. But equally important is the way we describe and interpret them. The way in which we present that data to others and the inference we choose to put upon it. Language is hugely important in work, we can use it as a force for positive change but to do so requires as much thought as any set of data that we share. Being lazy and careless with language simply isn’t acceptable. And if it isn’t acceptable in the world of work, it really shouldn’t be in journalism either – but perhaps their motivation isn’t to help understanding and build knowledge, whereas ours certainly should be.