Jonathan Lerner WG ’16, VP of Data & Analytics Club
“If you don’t have analytics knowledge … there is not a glass ceiling,
there is a cement ceiling that is going to be sitting on top of you.”
JL: The amount of data available and the ability to house data has been exploding. I think McKinsey did a study that said that Data Science was going to be the sexiest job of the next decade. So, for current students, how do you see things evolving over the next few years?
EB: It’s funny, I gave a plenary speech at an industry conference for about 500 people in Erasmus University in Rotterdam. And that was the topic, Big Data. And I started out my speech in a way I don’t think they expected—and there were a lot of big companies in the room, Heineken and other big local brands—I said, the first thing when somebody sends me a big data set, the first thing I say, and, excuse my French, is “oh shit.” What the hell am I gonna do now? I have this massive data set, I can’t run heavy-duty mathematical models on it because the data set is too large, or it’s even hard to read in, or it’s hard to clean. So now, I need to think about making big data small data, but I don’t want to get rid of too much information. So what I gave, in this 60 minute talk, is how do you make big data small data, and not lose too much information?
So, in the statistics world, and I’m trained as a statistician, we talk about data sufficiency. Things like sufficient statistics. So can you take a large number of
columns and collapse them down into a small number of columns. That’s kind of what I call, horizontal compression. Can we take a large data set, and do a sample, maybe not a random sample, but a sampling in a smart way. I call that vertical compression. So what most firms do is, they think—erroneously—that they need to analyze data sets of 100 million rows, by 1,000 columns. You don’t, statisticians have been dealing with problems on sampling and data compression for a long time. So I think over the next three to five years what you’re going to see in technology-enabled data is great. I care about new data sources, not bigger data, because end of the day I’m going to take that big data and compress it anyway, but what are smart ways to compress data? And to get the most information out without having to deal with 1,000 columns or 100 million rows. That’s going to be what I’m working on over the next three to five years.
JL: Interesting. So that’s the cutting edge of what you think is the academic side of things?
EB: I think the cutting edge will be that. I think privacy is going to force us to do that, because, even though this is potentially collectible, you’re not getting your hands on it. So you have to be able to do what I call customer analytics inference with limited information. I don’t mean because you can’t collect it. I mean because you won’t get that data. In fact, we may be seeing the tipping point now where the data that is available to us is going to become less, not more. And that’s fine, as a matter of fact it’s fine for me because, number one, I think privacy concerns are legitimate. And secondly, it keeps me in my job! You know, if you had any data you want, you don’t need a statistician, you can just get someone to run some regressions and you know the answer. It’s when the data gets limited, or you have to compress it, or you have to sample it in a clever way, that you get the medium-size-dollar people like me!
JL: You seem so passionate that, not only is this where the intellectual opportunity is, this is where, frankly, the big bucks are too. For students right now who are looking for those opportunities how should they think about it?
EB: I have three parts that you mentioned. Number one is, I strongly believe that if someone doesn’t have analytics knowledge—and I’ll say what that means in a second—there is not a glass ceiling, there is a cement ceiling that is going to be sitting on top of you. You will have no ability to reach the C-suite unless you have what I call technology-enabled data, and algorithmic development. I’m not saying you have to be a programmer, I’m not saying you have to be a computer scientist. What I’m saying is, if you cannot talk to the people who are collecting data, who are making inferences from the data, then how can you make data driven decisions? You have to have an understanding of that. So I think that’s the first thing I would tell students, there’s a cement ceiling sitting on top of you if you don’t have knowledge of this.
The second thing I would say is there really are two populations. Population one is the people who want to be data scientists. That’s not really the Wharton MBA population. As a matter of fact, it’s maybe the Wharton undergrad population, for a short period of time, but that’s not really their career path. Maybe go from data scientist to product manager, etc. The bigger population is the reason why I think Wharton should never offer a masters degree in analytics, because that’s a technical degree. We’re training leaders. I think having a Wharton MBA with analytics knowledge—that’s the sweet spot where they roll up the Brinks truck to your door and start printing money for you. Because every company that we visit, they say, Professor Bradlow, that’s the shortage. ‘We can hire statisticians, nothing personal to you, Professor Bradlow. But you’re a dime a dozen.’ I hope not, but they say that. ‘We can hire MBA students. You produce 800 a year. Harvard produces 800 a year. Chicago, Stanford, we can hire those people. The Venn Diagram, the intersection of those two? A tiny sliver of space.’ So, what I would tell students is, that’s where the big money is. So get analytics skills and get your business training.
The third thing I would say to current students is, it’s industry agnostic. So thinking ‘I’m not going to work for Google, it doesn’t apply to me.’ Oh yes it does, it applies far more than most people understand. Healthcare, financial services; there are venture capital and private equity firms that are evaluating how much to bid on companies based on customer-based analysis. Give me your data stream, we’ll do customer lifetime value calculations on your data stream, forget what somebody says you’re worth, I want to actually evaluate your customer base and determine how healthy your customer base is. So, if you think you’re immune from it because you’re going into an industry that you think it doesn’t touch, bad thinking. It affects everything today.
So to recap:
- There’s a cement ceiling without an understanding of data and analytics
- It’s not just for the data and analytics types. It’s strategy and business plus analytics
- It’s industry agnostic
JL: What else do you think is interesting with the evolution on campus of data and analytics?
EB: I’ve been extremely encouraged by the student-led organization, the Wharton Data and Analytics Club. To be honest with you, I didn’t think that in my lifetime, here at the school that I would see student-run organizations around data and analytics. As you know, recently I was at two different conferences: one was the Marketing Conference, the other was the BizTech Conference, I led panels on analytics at both—every session, and I mean every single one, had somebody in it that had some relation to analytics. So it’s clear that to me that whether it’s the Marketing Conference, the BizTech conference, students are getting it. As a matter of fact, I think students are getting it faster than some faculty are getting it. And I don’t want to say that we’re playing catch-up, because we’re still the leader, of any institution, in analytics. We need to develop many more courses to meet the student demand. And the Wharton Data and Analytics Club is a perfect example of how strong the demand is there.