by
Anna Wiener
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In an effort to hype myself up, I developed the theory, however flimsy, that analytics was a natural extension of my liberal arts education. The e-book startup had used the analytics software to track our alpha users through the app, and I had enjoyed looking at some of the data: what our investors were reading, and abandoning; whether or not people read public-domain books with cover art designed by the CPO, which we had added to bolster the library. In a certain light, I tried to convince myself, business analytics could be seen as a form of applied sociology.
this is basically me with macro
Several months prior, a tech blog had published an article announcing the analytics startup’s first major fund-raising round of ten million dollars. When asked how he would spend the new funding, the CEO made his priorities clear: he would pay the first hundred employees far above market, he said, and spoil current employees to retain them. This was the language of customer acquisition, but I didn’t know. I didn’t think at all about the stratification, either; how the hundred-and-first employee might feel. I’d never worked anywhere with a hundred people—I’d never worked anywhere with twenty. I’d certainly never worked anywhere that wanted to spoil its employees and had the means to do it. Generous, I thought. I found the tenacity winning.
The solutions manager did not mention equity, and I didn’t ask. I did not know that early access to equity was a reason people joined private companies at the startup stage—that it was the only way anyone other than VCs and founders got rich. I did not even know that equity was an option. The company’s in-house recruiter would eventually intervene, to recommend that I negotiate to include even a small stake. His rationale was simple: all the other guys had some. No one told me how much it was worth, or how big the pool was, and I did not know to ask.
High on the feeling of being professionally desirable, I told the solutions manager I would sleep on it.
Not everyone knew what they needed from big data, but everyone knew that they needed it. Just the prospect incited lust in product managers, advertising executives, and stock-market speculators. Data collection and retention were unregulated. Investors salivated over predictive analytics, the lucrative potential of steroidal pattern-matching, and the prospect of bringing machine-learning algorithms to the masses—or, at least, to Fortune 500 companies. Transparency for the masses wasn’t ideal: better that the masses not see what companies in the data space had on them.
“This company is going to be worth a gajillion dollars,” the account manager said, taking a bite of potato salad. “We’re ripping up and to the right. We have the best and the brightest. We’re on an immutable path toward success. We’re all just fucking ready to give whatever needs to be given to make this thing happen. All anyone is asking is for us to pour our hearts and souls into this unstoppable behemoth.” He drained his iced coffee. “Frankly,” he said, “I think it’s a pretty good bargain.”
lol fuck u
Our team was penned off in a corner of the office, at a cluster of tables marked with a sign that read SOLUTIONS ZONE. I stood in the Zone and felt powerful. Because the products of our labor were intangible, meeting customers felt amazing—validating. They approached, gave us their company names, and asked for help running reports. We never asked for their corporate ID cards or any sort of validation, and none of them ever questioned why it was so easy for us to pull up their data sets. Their companies had customer-support teams, too.
Even so, the enemy of a successful startup was complacency. To combat this, the CEO liked to instill fear. He was not a formidable physical presence—he had gelled, spiky hair; he was slight; he often wore a green jacket indoors, presumably to fight the chill—but he could scare the hell out of us. He spoke in military terms. “We are at war,” he would say, standing in front of us with his arms crossed and his jaw tensed. Across the world, Syria and Iraq and Israel raged. We were at war with competitors, for market share. We would look down at our bottles of kombucha or orange juice and nod along gravely.
The tool should have been straightforward. It was, in theory, simple enough to be used by a marketing manager. At least, that’s what my coworkers said—a blessing upon modern software. For years, the catchphrase had been So easy, your mother could use it, but this had grown uncouth and politically incorrect, to be used only in meetings where women weren’t present, of which there were plenty. But our users were endlessly creative in their ability to implement it incorrectly. They activated their own code, only to find that ours was silent, unresponsive. They checked their dashboards, refreshed and restarted their browsers. Then they would email, angrily.
“There’s no menu, so you can’t just order, you know, a martini,” the engineer told me, as if I would ever. “You tell the bartender three adjectives, and he’ll customize a drink for you accordingly. I’ve been thinking about my adjectives all day.” What was it like to be fun, I wondered—what was it like to feel you’d earned this?
i still think about this paragraph
At the analytics startup, we never once talked about the whistleblower, not even during happy hour. In general, we rarely discussed the news, and we certainly weren’t about to start with this story. We didn’t think of ourselves as participating in the surveillance economy. We weren’t thinking about our role in facilitating and normalizing the creation of unregulated, privately held databases on human behavior. We were just allowing product managers to run better A/B tests. We were just helping developers make better apps. It was all so simple: people loved our product and leveraged it to improve their own products, so that people would love them, too. There was nothing nefarious about it. Besides, if we didn’t do it, someone else would. We were far from the only third-party analytics tool on the market.