The (data) transparency problem

We often tout data and statistics as a strong take off point for communications. While true, the Corona pandemic shows how skewed data can become in the eyes of the public if not presented correctly. Here are our key takeaways from what is probably the most watched datastream on the planet – the covid disease dataset.

In our experience most people, journalists included will not take the time to understand or represent data for what it is. In the case of covid, the data we’ve seen has been a mostly flawed insight into a developing pandemic. Data needs to be treated like communication. A publicly traded company wouldn’t survive a week of unfactual reporting. Why should we have to deal with it during what might be the largest health disaster of our lifetimes? 

Data is communication, treat it as such.

Like everything in communications we need to ask ourselves what we want to achieve. For corona that objective is crystal clear: to inform and guide the public and governmental institutions to act in the best way possible. Whether communicating statistics at large; deaths, infected etc. help guide the general population is debatable but perhaps unavoidable. We would argue that to avoid fear mongering and misinformation institutions need to be more careful in selecting the data they provide and how it’s presented. It’s better to wait and be sure rather than rush to publish the latest information.    

Fathomable and contextual metrics

For one of our clients, a leading hotel chain in the nordics we setup and defined their digital sustainability platform. It connects to over 300 hotels, reporting data on water, energy, waste, number of guests and heating consumption. A huge dataset with no clear key metric, but with clear communication goals and platform. Goals that helped us define the metrics we wanted to communicate. Still as – or arguably more transparent than if we had just released the raw data. Since the project was aimed at consumers and employees the numbers needed to be relatable. It’s hard to fathom 250,000 metric tonnes of water but 2,1 litres of water per guest, per night is understandable to most. With a clear metric and a clear goal (to become more sustainable) it becomes actionable and we can actually start seeing how our contribution helps.  

Example of actionable data representation
Prototype graphic of actionable sustainability graphic

Since a lot of the corona data lacks a standard for reporting its useless and does not hold up for national comparisons. Making the only relevant although still contested metric: the excess death rate – as noted by Financial Times.  But still, if all the data were accurate – what metric would we ideally communicate? An initial thought, approaching the problem from a user perspective would lean more towards something like a personal risk assessment based on age and preexisting conditions compared to other life threatening risks lurking around in society. When will we see a governmental or international institution produce one of these?

Raw data is not for everybody

We hope the WHO will setup clear standards for case reporting during pandemics – as outlined by Our World in Data. But also that they start thinking communicatively on how they want to present that data. Is it meaningful to highlight a faceless number of what soon will be half a million dead? That can only create fear and discord – something I would think is the opposite of what they want to achieve. We think it would be a valuable excersise to start understanding their user base and the impact of the data they present or choose not to. That they start to fundamentally think about and investigate how their statistics reporting impacts the media and public. While the intended audience for these statistics may not be the media and public it is inevitable in this day and age that it does reach them and will be misrepresented in to clickbait.