Crucial Data Analytics Lessons That Came With The Pandemic

Now that businesses dealt with the phase one of the pandemic, here are data analytics lessons for phase two.

Data analytics came as a boon to businesses when they were sitting hand-on-head during the beginning of the pandemic. Data analytics helped organizations sieve through tons of data to get insightful information that helped them understand the changed consumer wants. But the on-going COVID-19 pandemic taught some data lesions that are practical and provocative, ranging from the importance of trust, collaboration, and addressing the limitations and misinformation.

The Teachings Of The Pandemic

Data Points Represent People

The logic is simple, data is generated by people. So, the lesson here is to think about what good practitioners can do through data and the unintended consequences of the published data at a policy level decision.

Data that is used to inform broad public decisions like health and safety measures should be treated with caution than normal public datasets

Wrong representation of data can minimize the intensity of the information and influence their decisions around important regulations. A common example for this is what is happening around the vaccine numbers. By sharing misinformation about the vaccine numbers, people responsible are creating a problem by encouraging people to take up vaccines while having supply issues.

Data can show the true picture of the intensity of a tragedy

COVID-19 showed the true power of data visualization, not on screen but via symbolic representations like candles lit for every life lost or flags meant for social distancing. While data on screen was there, nothing came close to the visual representation and that is the takeaway. Though a data analyst has the numbers, the understanding of those numbers will only come via proper representation.

Bias and inequalities in data shouldn't be tucked away

In the US, COVID-19 data is represented at national, state, and district levels, but it took a lot of months for states to release data race-wise. States were insisted to do so because indigineous, Black, and Hispianic communities constitute the essential workers group who were under the risk. The data then showed inequalities in the impact faced by privileged people who had the liberty to work from home and those who had to be on the line daily. Only when analysts don't hide these inequalities in data, people work on understanding the cause and come with a remedy.

Don't Rely On Just One Data Source

Lots of reports saw light during the initial days of COVID-19 regarding positive cases, hospitalizations, and deaths. And towards the end of 2020, different reports came out talking about the mortality and recovery rate which, when compared, showed a complete picture of the impact. This implies that one shouldn't trust data from one instance. Keeping in mind data's dynamic behaviour, results should only be judged after a thorough collection.

Data transparency matters. While the world is grappling with challenges about the case counts and bias, a lot of mistrust is being created. To make the right data more accessible, the above mentioned issues should be fixed.