It’s been interesting over the past weeks and months to observe the different approaches taken by countries in response to the Coronavirus pandemic. Several countries such as the UK and US have been providing CoVID-19 data at a granular level, enabling their populations to stay much more accurately informed. For example, the UK health department has been very transparent with their data regarding where the coronavirus cases are located, by area/postcode. This allows for more effective management and planning.

Innovative workarounds have been developed in Australia, (such as SnewPit and AIBuild), but it’s a shame that in Australia we’ve had to resort to approaches such as reverse engineering geolocation data by scraping social media feeds, to be able to more accurately map information related to CoVID-19.

While the current situation raises some data governance/ethics questions, it’s interesting that European countries governed under legislation such as GDPR have been more open with their data than Australia.

As we say in business, “if you don’t measure it, you can’t manage it”. Better access to more timely, more accurate, information invariably leads to better decisions.

With better access to data similar to that provided by other countries, data experts can provide local communities with much better information, which could help reduce the sense of panic and frustration being caused by state and federal government’s sometimes confusing messaging and misinformation.

The data visualisations below were built using some datasets recently released by the NSW Government. While this data is by no means perfect (I uncovered numerous data quality errors while building these dashboards), it at least a start, allowing us to keep our local communities more accurately informed.

Note: the base COVID-19 case data was enriched by adding Australian Bureau of Statistics demographic data (Census 2016), including population, median age, median weekly income to help identify, for example, ‘at risk’ suburbs (suburbs with a high proportion of the population over 65)

Author: Patrick Spedding

Experienced, innovative product leader in business software industry with global, cross-functional data analytics expertise