There’s a flavor of puzzle in which you try to determine the next number or shape in a sequence. We’re living that now, but for naming the data field. “Predictive analytics.” “Big Data.” “Data science.” “Machine learning.” “AI.” What’s next?
It’s hard to say. These terms all claim to be different, but they are very much the same. They are supersets, subsets, and Venn diagrams with a lot of overlap. Case in point: machine learning used to be considered part of data science; now it’s seen as a distinct (and superior) field. What gives?
Since the promise of “analyzing data for fun and profit” has proven so successful, it’s odd that the field would feel the need to rebrand every couple of years. You’d think that it would build on a single name, to drive home its transformative power. Unless, maybe, it’s not all it claims to be?
Resetting the hype cycle
In a typical bubble—whether in the stock market, or the Dot-Com era—you see a large upswing and then a crash. The upswing is businesses over-investing time, money, and effort in The New Thing. The crash happens when those same groups realize that The New Thing won’t ultimately help them, and they suddenly stop throwing money at it.
In finance terms, we’d say that the upswing represents a large and growing delta between the fundamental price (what The New Thing is actually worth) and the observed price (what people are spending on it, which is based on what they think it’s worth). The ensuing crash represents a correction: a sharp, sudden reduction in that delta, as the observed price falls to something closer to the fundamental price.
Given that, we should have seen the initial Big Data hype bubble expand and then burst once businesses determined that this would only help a very small number of companies. Big Data never crashed, though. Instead, we saw “data science” take off. What’s weird is that companies were investing in roughly the same thing as before. It’s as though the rebranding was a way of laundering the data name, so that businesses and consumers could more easily forget that the previous version didn’t hold up to its claims. This is the old “hair of the dog” hangover cure.
And it actually works. Until it doesn’t.
Data success is not dead; it’s just unevenly distributed
This isn’t to say that data analysis has no value. The ability to explore massive amounts of data can be tremendously useful. And lucrative. Just not for everyone.
Too often, companies look to the FAANGs—Facebook, Amazon, Apple, Netflix, Google: the businesses that have clearly made a mint in data analysis—and figure they can copycat their way to the same success. Reality’s harsh lesson is that it’s not so simple. “Collect and analyze data” is just one ingredient of a successful data operation. You also need to connect those activities to your business model, and hand-waving over that part is only a temporary solution. At some point, you need to actually determine whether the fancy new thing can improve your business. If not, it’s time to let it go.
We saw the same thing in the 1990s Dot-Com bust. The companies that genuinely needed developers and other in-house tech staff continued to need them; those that didn’t, well, they were able to save money by shedding jobs that weren’t providing business value.
Maybe data’s constant re-branding is the lesson learned from the 1990s? That if we keep re-branding, we can ride the misplaced optimism, and we’ll never hit that low point?
Why it matters
If the data world is able to sustain itself by simply changing its name every few years, what’s the big deal? Companies are making money, consumers are happy with claims of AI-driven products, and some people have managed to find very lucrative jobs. Why worry about this now?
This quote from Cem Karsan, founder of Aegea Capital Management, sums it up well. He’s talking about flows of money on Wall St. but the analogy applies just as well to the AI hype bubble:
If you’re on an airplane, and you’re 30,000 feet off the ground, that 30,000 feet off the ground is the valuation gap. That’s where valuations are really high. But if those engines are firing, are you worried up in that plane about the valuations? No! You’re worried about the speed and trajectory of where you’re going, based on the engines. […] But, when all of the sudden, those engines go off, how far off the ground you are is all that matters.
—Cem Karsan, from Corey Hoffstein’s Flirting with Models podcast, S4E1 (2021/05/03), starting 37:30
Right now most of AI’s 30,000-foot altitude is hype. When the hype fades—when changing the name fails to keep the field aloft—that hype dissipates. At that point you’ll have to sell based on what AI can really do, instead of a rosy, blurry picture of what might be possible.
This is when you might remind me of the old saying: “Make hay while the sun shines.” I would agree, to a point. So long as you’re able to cash out on the AI hype, even if that means renaming the field a few more times, go ahead. But that’s a short-term plan. Long-term survival in this game means knowing when that sun will set and planning accordingly. How many more name-changes do we get? How long before regulation and consumer privacy frustrations start to chip away at the façade? How much longer will companies be able to paper over their AI-based systems’ mishaps?
Where to next?
If you’re building AI that’s all hype, then these questions may trouble you. Post-bubble AI (or whatever we call it then) will be judged on meaningful characteristics and harsh realities: “Does this actually work?” and “Do the practitioners of this field create products and analyses that are genuinely useful?” (For the investors in the crowd, this is akin to judging a company’s stock price on market fundamentals.) Surviving long-term in this field will require that you find and build on realistic, worthwhile applications of AI.
Does our field need some time to sort that out? I figure we have at least one more name change before we lose altitude. We’ll need to use that time wisely, to become smarter about how we use and build around data. We have to be ready to produce real value after the hype fades.
That’s easier said than done, but it’s far from impossible. We can start by shifting our focus to the basics, like reviewing our data and seeing whether it’s any good. Accepting the uncomfortable truth that BI’s sums and groupings will help more businesses than AI’s neural networks. Evaluating the true total cost of AI, such that each six-figure data scientist salary is a proper business investment and not a very expensive lottery ticket.
We’ll also have to get better about folding AI into products (and understanding the risks in doing so), which will require building interdisciplinary, cognitively-diverse teams where everyone gets a chance to weigh in. Overall, then, we’ll have to educate ourselves and our customers on what data analysis can really achieve, and then plan our efforts accordingly.
We can do it. We’ll pretty much have to do it. The question is: will we start before the plane loses altitude?