Dataviz (or data visualization) should help us understand the current pandemic, but is it giving us the right version? Aagje shares his opinion and sorts the wheat from the chaff about data visualizations, and the lack of data maturity. What can we learn from this?
In recent months, we have been exposed to all sorts of data visualizations that are supposed to help us understand the impact of the COVID-19 pandemic. Graphs, dashboards and infographics of all kinds present us with the facts from sometimes very different angles.
Data quality
In Belgium, the debate on the number of reported cases of COVID-19 has resurfaced: should we take a 7-day average or the daily figures? At the same time, questions are being raised about the reliability and freshness of the data.
Of even greater concern is the large number of "not applicable" values. We do not know the sex or age of the person for almost 28% of the deaths. For Flanders, this is the case for more than 50% of the deaths. We know what causes this, but we are not able to address the root causes of the problem.
It's becoming clear that central and local organizations were not ready for this pandemic from a data maturity perspective.
Global Challenges: Are we speaking the same language?
It also became clear that in order to be able to create a graph that compares data from different countries, several obstacles had to be overcome. Not all countries share the same definition of a positive case, resulting in errors in ranking countries based on the number of positive cases or mortality rate.
Moreover, data are not always available or comprehensive, as not all countries or regions are willing to share the data in question or have reached a sufficient level of data maturity to collect and store the data.
Data Maturity Assessment
Several foundations of data maturity (organization, data availability, technology...) should be assessed. The result of the analysis can help to create a strategic plan, in order to be able to overcome the problems encountered throughout this pandemic.
Because at the end of the day, these challenges should not prevent us from visualizing the data. We need to ensure that the visualization is reliable, for example by clearly indicating differences in definitions. While this makes the visualization more complex, it should not be a barrier.
As Alberto Cairo states in his book The Functional Art: An Introduction to Information Graphics and Visualization: "Graphics should not simplify messages. They must clarify them, highlight trends, reveal patterns and reveal realities that were previously invisible.
If you want to create a graph from the COVID-19 data, you can take these points into account.
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