Authored by: CK Tan, Senior Director, Qlik
The global COVID-19 pandemic has undoubtedly highlighted the critical role that data plays in our lives. As many countries in ASEAN are struggling through a second wave, everyone is collecting, sharing and analysing data to fight the spread of the virus and create a vaccine against it. Not only that, but with many of us confined to our homes for the last few months, data is also helping to inform procedures and policies so we can safely continue to emerge from lockdown around the world and aim to get back to some form of normality, as much as that will be possible.
The global pandemic has also meant that we are all being bombarded with more data and statistical language than ever before, whether that be the headlines and articles that we read in the media, Whatsapp, Telegram or across our social platforms, with new terms popping up on a weekly basis as the pandemic evolves. Not to mention the plethora of charts and graphs being created and presented to us almost daily to keep us abreast of coronavirus developments in our own countries and around the world.
But unless you are a data scientist, mathematician or epidemiologist, chances are you may have been left wondering over the last few weeks and months what the data and terminologies in the headlines actually mean?
Many experts have come forward to help people decipher the data we are being served. One example is the Data Literacy Project series, ‘Know Your Data’, where common terms and phrases we’re seeing in the news around COVID-19 are explained. These have included the likes of:
“Exponential” curve: A curve that rises very quickly
“Flatten the curve”: The curve has to plateau at some point, the question is when it will and when do we want it to plateau
“Samples”: Selective sampling does not equal random sampling and as that relates to patterns, when you have selective sampling it limits our ability to identify these patterns
“Incubation period”: The time between when a person is infected with the virus and when they start to show symptoms of the disease and how this is impacting the data that we’re all seeing all over the news
“The R number”: The ‘reproduction number’ is the amount of new cases that existing cases generate on average while people are infectious. This is not fixed and fluctuates over time
“Contact tracing”: With many countries starting to re-open, we know that the ‘R’ number will go up. Contact tracing helps determine who has come into contact with an infected person already, so that we can isolate them as soon as possible to keep the ‘R’ value down
A catalyst for a data literate society?
For me, the pandemic is the latest catalyst that will lead many of us to question ‘is this data correct’, ‘can I trust the source of the data’ and ‘what impact will the data have’?
In both our personal and professional lives, we are exposed to such a vast amount of data – and this just keeps on growing. Yet, just 21 per cent of the global workforce are fully confident in their ability to understand and work with data. There is so much access to instant information in our lives, but this influx of data is meaningless if we cannot decipher it to make smart, data-informed decisions.
Data literacy is something we have been speaking about for several years. It is a necessity in today’s digital world. And in our current climate, it has never been so pertinent either. This does not mean understanding the depths that data scientists do. Instead, it’s about being able to read, work with, analyse and argue with data – and that goes for us as consumers and employees. At Capital & Coast District Health Board (CCDHB) for example, they use Qlik Sense in “an attempt to make complex data contexts transparent, so as to try to narrow the gap between the meaning constructed by the writer and the meaning interpreted by the reader.”
When we define data literacy in this way, this gives us a series of things we can be studying and learning. How well can we read data to understand a situation and then predict future outcomes? How comfortable are we in working with data or are there still knowledge gaps keeping us from making use of it? How well can we analyse and ask questions of data? Finally, are we comfortable communicating our results and analysis?
Fundamentally, data literacy is no longer nice to have. It is imperative. We are living in a time when it has never been more important for us and our organisations to develop skills in data literacy to ensure we are able to understand the information being presented to us every day, not only to help us now but to equip ourselves for more data-informed decision making in the future.