4 Things COVID-19 Has Taught Us About Data

We live in the age of information. It is literally, at our fingertips. You can pull many data sets from multiple data sources with a few taps on your phone. We are all learning a lot during this pandemic. One thing that has become clear over the past few weeks is the importance of data. Regardless of your profession or which industry you're in, you've probably heard your co-workers or bosses talking about "data-driven" this or that. Here are 4 takeaways about how data is used from our experience with coronavirus.


Incomplete data sets can be dangerous


It's now apparent that we didn't have the full picture of how dangerous the virus was from the data received from China and other early affected nations. In the early stages, we had no choice but to rely on the limited amounts of data that were available to make big important decisions. At first, we took this data at face value, assumed them to be correct, and took strong action based on the high mortality rates seen in other countries.


As the virus ripped through the United States, we gained a more complete set of data about contagion, mortality, and treatments. Although we still don't have a fully comprehensive data set, it's much better than it was even just a few weeks ago. The more we knew, the more confident we could be about the decisions we were making. This concept is exactly the same in business as well. It is the core concept of data-driven decision making.


Most data is useless without analysis


Raw data is nearly impossible to interpret without some thoughtful analysis. It's why data isn't often presented in massive tables but rather graphs and charts. The more complex the data, the more important it is to have a high level of analysis. In the case of the virus, Dr. Birx and Dr. Fauci are primarily responsible for gathering all the data, analyzing it, and providing feedback to the white house task force. Not only are the doctors trained to look at data and trends, they have decades of experience looking at infectious disease data specifically from across the world. Imagine if data was gathered and given directly to the task force without any analysis.


Generating and collecting data is the easy part. It gets difficult when you want to turn that data into action, which is the purpose of data in the first place. The first step is collection, next is analysis, then finally, action. Having multiple critical thinkers doing the analysis and providing feedback to the decision-makers is key to making prudent decisions. Having unilateral responsibility for all three steps is a recipe for disaster.


Data and truth aren't always the same thing

When you look at the data we are getting from places like New York, China, California, and Italy we see some drastic differences in mortality rates. This begs the question, which mortality rate is correct? Obviously, the truth is that every mortality rate can't be correct. Although data may say one thing, the truth could be something different. It could be slightly different or it could be drastically different. In most cases, the accuracy of data is very difficult to measure. This is why people are so vocal about the importance of testing. As the testing becomes more comprehensive, the denominator in the calculation for the mortality rate becomes more accurate.


This concept becomes abundantly clear when we look at how the projection models change over time. At first, the models were incredibly grim but as more data was gathered and analyzed the models became more accurate and less ominous. This is a great lesson for business leaders. Base your revenue projections more on historical data rather than adding in a slew of estimates and assumptions.


Context and consistency in delivery is key


The way data is presented has a massive effect on how it is interpreted. I've seen data shared that intentionally manipulates the scale on charts so that it tells the story that they want. Two different people can look at the same set of data and tell two very different stories. It's all about context. If I wanted to be pessimistic, I could say, "The US has the most coronavirus cases in the world." and that would be true. If I wanted to be more optimistic, I could say, "The US isn't even in the top ten in terms of infections per capita." and that would also be true.


In either case, the important thing with data is a comparison over time. When data is presented in a certain format, it needs to remain in that format in order to accurately portray the data. This is the only way to see improvement or deterioration over time. It can be easy to hide your performance in data if you don't do this. If I present a different set of metrics at each board meeting, how is the board supposed to know if we are improving on getting worse as time goes on?


It's okay, and actually useful, to be skeptical about data when it's presented to you. It's important to consume data and think independently, whether that's in business or your personal life. Always ask yourself where the data came from and how the data was presented. There is always a possibility that the person presenting the data has an agenda in presenting it. For example, I've been in sales leadership for 8 years and this is all I do. When I present data to my CEO it's to prove a point I'm trying to make. I'll show trends in the number of meetings booked over time to show that our new outreach strategy is working.


No matter what the situation, the important thing is truth. Data is supposed to give us insights into the actual truth in the world. With this in mind, I think we should all strive to present data that is comprehensive and honest rather than just presenting data that makes us right.




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