Covid-19 and the Big Data Dilemma

We have all faced the same burden since the pandemic started in March. We have too much information and not enough of it. Living with both scenarios might sound strange. However, that’s the dilemma we’re in.

Tons of data is being generated, collected, and analyzed. Yet, some questions are never answered. Sometimes the answer changes. Other times the answer generates new questions.

Too much data with too many discrepancies and different variables creates challenges. Equally challenging is having an abundance of data, but not the right data. Many questions are still unanswered.

The Challenge with Too Much Information

Each country has their own method for testing and reporting on Covid-19 cases. Within each country, testing/reporting differences sometimes exist between states/provinces. Or variations at local levels.

Additionally, lots of other data is being generated or collected on issues related to the pandemic. One example I found interesting was a decreased number of babies being admitted to the NICU, in some places.

A lot of data is available, but it can’t be analyzed accurately. For example, several countries have started reopening their school systems. However, each country used a different method and reported on various elements. Some countries did more testing. Others did a more phased approach. Others are using a mix of online and in-person education.

Countries looking to reopen schools must review the wide range of options available. Then try to create a policy and propose a plan to keep children safe. But couldn’t this be simpler if some common elements existed between all the data sets? Or if some of the collection methods were standardized?

Basing decisions on available data comes with risks, especially when there are inconsistencies in the data creation process.

The Challenge with Too Little Information

We still don’t have enough information yet to answer important questions. For example, how long are people immune? What percentage of the population is asymptomatic? Do we still need to wash our groceries? Or transfer takeout food to different containers? Why can’t we do a better job of tracking and distributing PPE to people who really need it?

The challenge of having so much data available is making sure the right data is being collected. Otherwise, we’re all just trying to make sense from all the “noise.” Too much information and not enough of it is useful to give us the answers we need.

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