Highlights §
- Well, not quite. Data is not a Ponzi scheme. Financial reporting and business intelligence are the only senses that many businesses have, and even the biggest cynics can’t contort themselves into say that this stuff isn’t important. And operational telemetry, and the automated algorithms built on top of it, generates trillions in revenues in Amazon’s warehouses, on Google’s ad networks, and across factory floors and insurance companies and trading desks around the world. These are all real and enormously valuable applications of data. (View Highlight)
- But within the field of analytics—the practice of trying to make better strategic decisions with data and manual analysis—the track record is less compelling. Here, the animating narrative has long been the same: Data is full of useful insights. Companies that don’t find them are doomed to fall behind and die. Analysts are the experts who can extract those insights. Sure, we can’t live up to our full potential today—our data is too messy; our data is too unreliable; engineers are too busy to help keep it clean; our tools are broken; our workflows are broken; we are buried behind less important work; we distract ourselves; we don’t know how to organize ourselves; executives don’t support us; they treat us like second-class citizens; people don’t know how to work with us; people don’t invite us into the important rooms; people are illiterate.4 But on the other side of these problems, just over the horizon, there is an oasis of scholarship and shareholder value. (View Highlight)
- Keep plugging, and we’ll finally clean up this mess.5 Keep plugging, and our potential energy will become realized energy. Keep plugging, and we’ll fulfill the long-told prophecy that data will become every company’s single most important asset. As data teams, this is our gospel: One day, when the glory comes, it will be ours; one day, when this war is won.6 (View Highlight)
- As a philosophical point, I suppose this might be true. Though I have my doubts, it’s impossible to know what could be, given more time to build better tools, cultivate better cultures, educate more coworkers, and enlighten more executives. As a practical point, however, I’m not sure how much these hypotheticals matter. The question is not whether such a world exists, but if we will ever get there? (View Highlight)
- More pointedly, it’s this: Would you, as a reader of this blog and presumably someone who is at least vaguely optimistic about this whole analytics endeavor, bet your entire bank account7 that we will—collectively, in general, on average—reach that promised land, in which data is democratized and insights are liberated and businesses are empowered and various other nouns are actioned? Or would you take the other side, and bet that most analytics teams slog along, heaving slowly forwards through the same briar patches that we’ve been stuck in for years? A decade from now, will most analytics teams—not your team, of course I have faith in you—be seamlessly driving critical corporate decisions? Will they be plucking new business opportunities out of haystacks of data? Will they be invited to weigh in every key debate, and have a seat around every key table? Will they be a competitive advantage? (View Highlight)
- Or will they continue to toil on the fringes, responding to queues of feckless questions, adding bits of value here and improving some marginal thing there, while continuing to say, to others and to themselves, that better days are ahead? (View Highlight)
- If my life savings were on the line, my answer is that our hope is a mistake, and most analysts will never escape their current lot. As we’ve talked about many times before, analytics might be an inherently flawed enterprise. Data is fundamentally messy and fundamentally biased. Turning this data into useful information requires rare analytical skills; turning information into better business results requires courage and a knack for persuasion. Unless we can teach these skills at scale, which we probably cannot, there will be little demand for the everyday analytics team’s work, and little advantage for the everyday company to invest in analytics. (View Highlight)
- In short, my answer is that analytics—not as an industry or as a technology ecosystem, but as a discipline—might not work. The average company may never be able to make better decisions by hiring a team of average analysts. We can make dashboards and be operational accountants. But the fun, exploratory, “valuable” work may always be an indulgent, empty dessert, and never the entrée we want it to be. (View Highlight)
- Which is an awkward answer, because in some rough way, my life savings is on the line. For many of us who work in data, we were hired to build this data-driven, insight-rich, collectively shared future. If we don’t, the whole apparatus—our jobs, our conferences, our startups, our academies, our statuses, all of it—will fall apart. (View Highlight)
- Though no, that’s not exactly right. People in the analytics profession haven’t bet their life savings on building that future; we’ve bet our life savings on other people believing in that future. As long as people have faith in its potential, the machine can stay upright. Our executive overlords, many of whom probably feel that they aren’t getting their money’s worth out of what we are, can mentally reclassify our salaries as down payments for what we will be. Vendors can keep promising future insights. We can keep trotting out explanations—data quality, data literacy, executive support, the distractions from AI, whatever—for why we aren’t yet a true analytics team. Our Ponzi scheme can stay solvent, until I’ll be gone and you’ll be gone, and another generation can worry about what comes next. (View Highlight)
- Honestly, it might work. People unquestionably accept that data is valuable; it wouldn’t take much to keep that faith alive.
But what if we assume we can’t perpetuate it anymore? Suppose money gets tight, and the SEC (Stingy, Efficient CEO) runs out of patience. They demand to know how we plan on making our work worth what we’re paid. They’re onto our scheme, and our usual explanation—that it’ll all get better, once we complete a few eternal prerequisites—will no longer fly. This sounds like a Ponzi scheme, the SECEO will say, and in years of efficiency, Ponzi schemes get fired. (View Highlight)
- One obvious answer is to stop doing the Ponzi scheme. Dissolve the analytics team. Trade in our big promises for something smaller. Instead of getting fired, take a reduced sentence: Twenty years to life of building reports and maintaining operational infrastructure. Become more like IT or HR—invaluable components in the corporate machinery, but with fewer delusions of grandeur. Accept our lot in life as bean counters, concede that our “insights” aren’t business-critical, no matter how much we beg people to use them, and count the beans. (View Highlight)
- I mean, maybe, but man, that is unsatisfying. People make decisions all the time, those decisions are rooted in facts, and facts are rooted in data. This basic logic seems so sound. Surely there’s a way to make this work? Surely there’s something more legitimate, and more reliably useful, that we could gracefully pivot into? (View Highlight)
- Though small statistical details matter for operational optimization problems, the analytical decisions that executives make are mostly based on these sorts of directional vibes. The line is wiggling; is it noise or is something changing? Customer retention is declining; let’s hire more account managers. Everyone is talking about AI; let’s send dump trucks of cash to the houses of famous AI experts. We struggle to sell to women; let’s make a horror movie about it. (View Highlight)
- This is part of why customer interviews are often more valuable than analyzing a bunch of data, even though the latter is the stuff of scientific inquiry, and the former is gossip. In so many cases, gossip is what people want.9 CEOs want to know what their customers are thinking. Behavioral data isn’t “truth;” it’s an observable proxy, the input to a kind of analytical alchemy that attempts to turn individual outcomes into generalizations about intentions. In some sense, the careful rigor of data analysis is a red herring—it’s what we were trained to sell, but not the main thing our customers want to buy. (View Highlight)
- Perhaps that’s actually what’s held us back for so long: numbers, more or less. Though most business decisions are driven by numbers, those numbers matter because they define people’s loose mental models for how the world works, not because people need to know about the often-meaningless tedium of things like statistical significance. By talking primarily in the language of exact facts and figures, we make ourselves dismissible, because nobody really cares about the precise amount some metric went up or down. At the end of the day, executives care if the world still works the way they think it does. Everything else is just bluster and bikeshedding. (View Highlight)
- Disband or rebrand—that may be our choice. Analytics teams don’t have to be bound to data teams, and that association might be our problem. We might be better off finding other departments to operate in, and advertising ourselves as majors in that field with a minor in analytics, rather than the other way around. It might not be the fun thing we imagined, but it’s the useful thing we would get paid and promoted for. Because after flirting with a life of crime, we might need to start keeping our backstory a secret, and learn to blend in with everyone else. (View Highlight)