Pandemic Perspectives on the Value of Big Data
Numbers. The stories they tell us are so revealing. The coronavirus crisis is generating a river of data – flowing with everything from evolving economic forecasts to minute-by-minute tallies of coronavirus cases and deaths.
During the launch of Analytics at Wharton in 2019, a merging of research, teaching and resources that shows the business school’s commitment to the exploding field of analytics, Dean Geoff Garrett said, “In the 21st century, leaders will increasingly use data and analytics to develop insights that will help them make better decisions and become better leaders.” Little did he know that analytics would soon become so critical to helping us solve problems during one of the worst business and economic crises of all time.
Eric Bradlow, Wharton’s vice dean of academics, as well as a marketing professor, joined the Wharton Business Daily show on SiriusXM this week to discuss how analytics are impacting our culture right now. Here – in his own words — are three of his top takeaways:
Calculated risk: “Analytics, statistics, projections, testing, random sampling…these are all crucial to making an informed decision,” says Bradlow. “At the end of the day, analytics is a decision-support tool. People who make billion-dollar decisions in industry all the time have to decide what are the risk factors that could make a projection untrue, what are the risk factors that we could end up seeing a larger downside than we’re expecting. I believe that analytics is exactly the right decision-support tool to policy makers, to businesses and to us as individuals about the risks that we may be willing to take or not.”
Customer intel: Small businesses are “going to have to use analytics to understand that all customers are not created equal,” notes Bradlow. “80% of your revenue comes from 20% of your customers. And then the question becomes, which customers? Small businesses are going to be forced to find out very quickly.”
Sports statistics: “If sports are going to start, let’s take advantage of this data opportunity to understand things better,” says Bradlow, a statistician expert who co-hosts the show about sports data on SiriusXM. “Most sports will have no fans or an extraordinarily limited number of fans to start with. We’ve got 50 years of data with fans in the stands. Now we can look at, well, does a pitcher not get as amped up without fans? We can look at pitch speeds. Does a golfer maybe not hit the ball as far or maybe he doesn’t have to worry about hitting the crowd, so he can shoot the shot differently. One of the biggest estimated effects in sports analytics is home-field advantage. Is home field due to rest and being at home or is it due to the fans? Interesting data could emerge from this tragedy that we as analysts will look at for years to come.”
Related Links
- Penn Wharton Budget Model: Coronavirus
- Wharton: Making Sense of Coronavirus Statistics
- Wharton Sports Analytics and Business Initiative
- Analytics at Wharton
- Penn Wharton Budget Model Projects Effects of Reopening States
Conversation Starters
Eric Bradlow says, “I believe that analytics is exactly the right decision-support tool.” What does he mean by this?
Do you play sports? How would playing a game without fans impact your experience? What other statistics might emerge from this scenario?
Have you become more interested in data analytics during the COVID-19 crisis? For instance, a big part of the policy discussions around reopening the economy and other decisions have been based on forecasts. Forecasts are based on assumptions, often supported by data. Have you found yourself exploring the numbers at all?
I am a huge hockey fan, and it’s been very sad for me to see the NHL season suspended. Usually, I would have already watched the NHL playoffs, but the coronavirus pandemic has squashed this dream, at least in the traditional way. While there will be playoffs held this summer, there will not be any fans. The games will be broadcasted, unlike the sold-out arenas that usually accompany the games. In my opinion, it is not the same experience, even just watching the game from home. Although I have yet to see one of the July hockey games, I can imagine that the arena will not be nearly as loud when goals are scored and in general. It will feel more empty, even from home. While I do not doubt that many people will still watch these games, I would not be surprised if statistics come out this year to show a lesser satisfaction rate.
Throughout what we have seen in the past few years with the pandemic which has finally slowed down, big data sets have been altering the way we perceive information. I believe that Bradlow put this perfectly, in my view, during the pandemic limited communication meant a rise in the technology usage especially in sports fanatics like myself. Sports analytics and analysis of data was a perfect way to engage the fans. As you said(Tomas C), sporting activities have declined since the Pandemic, stadiums are less crowded and fan spirit is lowered. Big data sets can be used to see this because with the lowered amount of fans and the small trail of the pandemic, obtaining the data alone is hard.
The Covid-19 outbreak happened when I had just graduated elementary school, just before I got into middle school. As we all know, the pandemic stopped us from seeing each other for a whole three years, and it was no different in my middle school. All classes were switched to online Zoom classes, and I can’t even remember the time when I first saw my classroom in real life. We knew nothing about online Zoom meetings, but had no other option because of the skyrocketing number of confirmed causes. The fact that there was no data from the past did not help. I felt that the Covid-19 outbreak was not something to be taken lightly, and this is where Big Data comes in.
Big Data is basically what it sounds like: large, complex, and diverse sets of data that are consistently growing in volume. One of the main reasons the Coronavirus could infect so many people was because there wasn’t enough data about the disease yet. It was a new type of disease that nobody knew about, and this led to massive amounts of confusion and chaos during my middle school experience. Not to mention the entire year during which we weren’t allowed to go to school, but also after the pandemic started to dim down. For example, we had things like entire classes missing out on coming to school because of one identified case in the class. I felt that this was very inefficient and time-consuming. If we had data analyses of the infected student, we could have easily distinguished the people the student contacted with. With this data, we would have had a head start on the virus, catching those who were infected before they spread the disease to anyone else.
Big Data wouldn’t have just helped those in the educational system. If we had a better system of Big Data during the first few known causes, we could have easily identified the compositions of the virus and taken actions accordingly. For instance, it would have sped up the making of the vaccines much, much faster. This way, we could have prevented the loss of countless lives. Data analysis is so important especially during crises like the Covid Pandemic, as it can help identify known cases; track patients’ paths to find out extra infections; and develop vaccines.
Likewise, the values of Big Data during pandemics are massive. Not only does it help us with analyzing data sets, but it also helps us prevent the things coming in the future. We should take advantage of this fact and use Big Data more often in the future, preparing for tragedies like the Covid-19 Pandemic. Big Data should be used more in the future, and we should make better use of it.