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For immediate release: Doubling down on our data investments
We have no problem selling data. But would we buy it?
Suppose, this time, that you're a day trader. But you’re not one of those Twitch stream carnival clowns who scribbles conspiratorial lines on top of candlestick charts; you’re not a get-rich-quick sucker who was conned by a Clubhouse scam; and you definitely didn’t spend high school playing Xbox Live, college playing video poker, and your twenties in a basement reading r/WallStreetBets and fueling your destiny with Bang energy drinks and crypto pump and dumps.
No, you look for long-term value. You trade public equities and bonds; you only buy bitcoin as a hedge; you have just one Ape; you knew SPACs were a fraud from the beginning. You do your research, and buy long. You're the blue-collar Buffett; the Oracle of the everyman; the proletariat prophet.
Sitting at your trading terminal on January 14, 2022, you see a big announcement from JPMorgan Chase. Over the next year, the bank—the biggest in the United States—is committing to spending $12 billion on new technology and data investments. In 2023, they’ll add another $3.5 billion, pushing their annual technology budget over $15 billion. The announcement is littered with promises that could’ve been plagiarized from whitepapers about digital transformations and the modern data stack: “There are two kinds of corporations emerging from today’s technology revolution: the disrupted and the disruptor;” JPMorgan will use “machine learning to personalize the digital experience of its research platform” and an “AI-powered assistant to adapt to the clients' behavior over time and make insightful recommendations;” they’ll “push the limits in tech applications” to reinvent JPMorgan for this “once-in-a-generation transformation.”
Put more succinctly, the JPMorgan leadership team saw the hype that’s come out of Silicon Valley's data industry for the last decade, and they’re in—hook, line, and sinker.
Immediately after the announcement, JPMorgan’s stock got wrecked. It fell 6.2 percent that day, compared to a 0.6 percent drop for the Dow Jones, a 1.2 percent drop for Citi, and a 1.7 percent decline for Bank of America.1 So here's the uncomfortable question, for those of us who aren’t yet day traders but are instead representatives of that data industry, often selling the exact vision that JPMorgan is so enthusiastically buying: After reading that press release, are you long or short on JPMorgan’s stock? Though nearly unimaginable in scale, JPMorgan’s investment is the sort of support that data teams dream of. It’s the kind of initiative that data vendors want to galvanize. It’s the digital transformation that we say is necessary for every modern company’s survival. Here’s our chance to put our actual money behind what our marketing mouths are saying—and at a 6 percent discount, to boot. Do we do it?
Given that the majority of this blog’s audience is people who work in or around data, anything short of landslide in favor of buying feels like something between a low-key crisis and “our careers are a fraud.” And yet, my vote is to sell—and not just JPMorgan. I suspect I’d have the same reaction to almost any company that put out a similar announcement.
What do we make of this? The answer, I want to believe, is more complicated than “data is a waste of money.”2 So let’s do what everyone who’s performatively lost faith in Silicon Valley does: Pretend to go to Miami.
Take the talents from South Beach
Instead of trading stocks, now imagine that you bet on sports.3
Suppose that the Miami Heat—considered one of the best run organizations in the NBA—pull a JPMorgan, and announce that they’re going all in on advanced analytics. They say they’re going to do more to quantitatively assess talent and look for low-cost diamonds in D-I and D-League4 rough. They’ll do real-time in-game analysis to optimize substitutions, lineups, and defensive schemes. They’ll track player’s health metrics (and not just their age) to manage midseason workloads and reduce injuries. They’ll hire a team of quants to find more creative ways to structure their salary cap.5
After this announcement, would you bet on the Heat winning or losing more games over the next several years? Would you be a buyer of Heat shares—as measured by how good of a basketball team they are—or a seller?
This time, I’d be bullish.6 My instinct is that this investment—this “digital transformation”—would pay dividends, in the way that JPMorgan's wouldn’t. Obviously, my bets may well be wrong,7 but the reaction is curious. Why do I believe in one, but not the other?
The answer, I think, is that the Heat’s problems are clear. They need to find better players, design better lineups, and keep their roster healthy. Data and analysis can play a direct role in all of these things. Though the Heat may not be able to figure out how to do it, they at least know what they need to figure out. If nothing else, they have a hypothesis.
JPMorgan, by contrast, has only hope—hope that the hype is real; hope that there’s actually value in the data they have; and hope that, once they lay a new foundation, they can do something useful with it. Their investment is, as the cliche goes, a $12 billion solution in search of a problem, inspired, it seems, by fear and FOMO. Their path ahead is indirect and paved with faith, like building a bridge over a river, with hopes that there’s something worthwhile on the other side. According to one critic, “even Jamie Dimon, one of the best bankers of his generation, doesn’t get a free pass to increase investment spending by half over three years without giving more granularity about expected benefits.”
In other words, open-ended data initiatives suffer from, as Gwen Windflower put it in her new blog, an intention deficit. My skepticism about JPMorgan isn’t exactly about data; it’s about direction. If there was a better plan and a clearer roadmap—if there was intention rather than a competitive arms race—I might be a buyer.8
Forward to the future
Even if that’s true, it raises another unnerving question: How many JPMorgans are out there? Both my and the market’s reaction to JPMorgan’s announcement isn’t just because of a disappointing press release; it’s because we’ve seen this movie before. Data projects are famously hit or miss. Surely there are a bunch of companies that have stacked up a lot of losses.
At this point, I’m probably supposed to say that data teams and data vendors need to get back to the essentials; that we need to do a better job of defining our objectives and what success looks like; that we need to think about bUsInEsS vAlUe. If we don’t, this part should go, the bubble will burst, and we’ll pay for our thoughtless profligacy.
But I’m not so sure. We don’t have to back out of a problem the same way we came in. If this grand experiment in big data and all of its spinoffs bear fruit, I don’t think it’ll come from a return to the basics. It’ll come from the thrash, from the failed attempts, and the busted billions of companies like JPMorgan. Though most roads to uncertain destinations are bridges to nowhere, someone will find something transformative and unimaginable on the other side of the river. Ultimately, that will be the legacy of the modern data stack: the data industry’s creative phase. It’ll be the period when we write a bunch of bad drafts, and probably have to throw out most of them, but ultimately find something novel and profound in it.
In that, I think JPMorgan’s short sellers are right, but not predictive. Yes, the value of digital data transformations have probably been overpromised and oversold. Yes, we’ve created a bunch of unnecessary tools. Yes, there’s a bubble, and it may be bursting in this very moment. But our inventions are real, even if the money that created them was artificial.
I understand that investors value JPMorgan’s shares based on how much of a profit they think that JPMorgan will turn. Spending a bunch of money is the opposite of making money, so almost anything that costs $12 billion dollars—be it IT investments, a chat app, or a design tool—is unlikely to thrill Wall Street. Still, investors aren’t that short-sighted; their reaction is likely because of the price tag and because of what’s being bought.
Apparently, this is now called the G League because Gatorade sponsors it. Which I guess means we can look forward to BMW sponsoring F1 and changing it M1, and Apple sponsoring NFL and calling the championship game the iBowl.
In theory, there could be data on what “the market” thinks too, just as there is on the JPMorgan announcement. Vegas has over-under lines for team win totals—lines that would likely shift if a team signaled that they were committing to being more analytical. I couldn’t find an exact example like this, but if you know of one like it, I’d love to know what happened.