The Big Data that Uber Ignored: Metrics on Sexism in the Workplace
Yesterday, as news of Uber’s latest scandal emerged, Uber users once again started to delete the Uber app from their phones. The first round of mass Uber app deletions happened just a few weeks ago following news that CEO Travis Kalanick had joined President Trump’s economic advisory council. The user protest eventually led Kalanick to step off the council. Clearly, he left based on the metrics of the situation. Now, another misstep is threatening to further erode Uber’s share of the ride-sharing market. Over the President’s Day weekend, a blog post about sexual harassment in the workplace written by a former Uber engineer, Susan Fowler Rigetti, went viral. Surprisingly, as reported in the New York Times, Kalanick said it was “the first time the issue had come to his attention.”
Kalanick’s claim that he just didn’t know what was going on at Uber should raise at least a few questions. After all, if Kalanick can use big data to reduce traffic problems in major cities around the world, why can’t he use big data to address problems closer to home, including in his own workplace? Is it really possible that in a big data era, this big data genius just couldn’t see what was happening at his company?
Fowler’s “Strange, Fascinating, and Slightly Horrifying Story”
In a detailed blog post, Fowler, who left Uber in early January, described her reasons for leaving Uber as a “strange, fascinating, and slightly horrifying story that deserves to be told.” Fowler joined Uber as a site reliability engineer in late 2015. She explained that after a couple of weeks on the job, she joined a team focused on her area of expertise, but then, things got weird. “On my first official day rotating on the team,” writes Fowler, “My new manager sent me a string of messages over company chat. He was in an open relationship, he said, and…he was looking for women to have sex with.” Fowler was no fool. She immediately took screenshots of the chat and reported them to HR.
Despite Fowler’s assumption that HR would do the right thing, she was told that this was a first offense for this “high performer” so nothing would be done. Worse yet, HR gave her only two choices: to leave the team or stay and face what would no doubt be a negative performance review. HR even told her that if she stayed and received a negative review, it wouldn’t be retaliation because she has chosen to stay on the team. In the end, Fowler chose to leave her team and join a new site reliability engineering project. She did great work and even turned out an award-winning book on the team’s work, Production-Ready Microservices. During this time, something else happened — Fowler started to meet other women engineers who had had the same interactions with the aforementioned manager. It became obvious that HR was lying about this “high performer’s” track record. Even as the women returned to HR with new complaints, they were routinely denied any support.
The final straw for Fowler, however, was her performance review. As she explains, “Performance review season came around, and I received a great review with no complaints whatsoever about my performance. I waited a couple of months, and then attempted to transfer again. When I attempted to transfer, I was told that my performance review and score had been changed after the official reviews had been calibrated, and so I was no longer eligible for transfer.” In the end, Fowler was told that she just didn’t have a “upward career trajectory.” Although HR assured her that the negative review would have no real consequences, she knew this was not true. She was enrolled in an Uber-sponsored graduate program at Stanford and the tuition would only be covered if her performance review was high.
As Fowler eventually concluded, Uber was trying to block her career and other women’s careers. If this is the company’s intention, the numbers suggest they have already succeeded. As Fowler observes, “When I joined Uber, the organization I was part of was over 25% women. By the time I was trying to transfer to another engineering organization, this number had dropped down to less than 6%.” On Fowler’s last day at Uber, she again calculated the percentage of women who were still in the organization: “Out of over 150 engineers in the SRE teams, only 3% were women.” But this again raises an obvious question: Was Kalanick really oblivious to the sexual harassment problems at Uber?
Uber Response: Too Little, Too Late
On February 19, Kalanick tweeted to his 186,000 followers, “What’s described here is abhorrent & against everything we believe in. Anyone who behaves this way or thinks this is OK will be fired.” He quickly followed up with a more directive post: “I’ve instructed our CHRO Liane to conduct an urgent investigation. There can be absolutely no place for this kind of behavior at Uber.”
While some people welcomed Kalanick’s decisive response late Sunday afternoon, on social media sites and in the mass media, reaction was mixed. Not surprisingly, Arianna Huffington was quick to support Kalanick. Notably, Huffington was invited to join Uber’s board in 2015 in an attempt to bring a bit of EQ (emotional intelligence) to an otherwise data-driven corporate climate. Huffington promised to support a full independent investigation. But for many people on Twitter, the CEO’s response was too little and too late, leading to another call for people to delete the Uber app. As one respondent wrote, “If you waited so long till she blogged about it, then who will trust u to do the right thing in other matters. Is anyone bothered?”
Ignoring Metrics on Sexism in the Tech Industry
Uber drivers are under intense surveillance. Just a couple of negative reviews, and they can be kicked off the app. At one point, any driver who fell just a bit below the app’s average driver rating of 4.8 (e.g., a 4.6 average rating) would be deactivated. They could, however, be reinstated if they paid Uber to take a “customer service” course. The point is that when it comes to Uber’s drivers, Kalanick has always been fully prepared to use big data to ensure only the highest level of professionalism and service. So if his company thrives on worker visibility — being able to monitor and regulate thousands of drivers around the world — why can’t he see what is happening at Uber’s headquarters in the Silicon Valley? The bottom line is that it’s a choice, and it appears as if Kalanick is just choosing not to look at some employee data while remaining focused on other employee data. Put bluntly, Kalanick is choosing to ignore data directly related to gender and talent retention in his own workplace.
In Kalanick’s defense, one may argue that he simply didn’t know that he should be looking at data concerning the retention of women employees. But again, since he has clearly not been living off the grid for the past few years, this argument does not hold much ground. As reported in Elephant in the Valley, which was released in late 2015 following a year that started with Ellen Pao’s high-profile lawsuit against venture capital firm KPCB, 60% of women working in the tech industry have experienced unwanted sexual advances and 65% of such advances come from superiors in the workplace. If Kalanick is unaware that there are widespread problems with sexism in the Silicon Valley, he is clearly living in a bubble. One might further conclude that his grand claims about the power of big data are simply flawed. It seems more likely, then, that Kalanick is simply selectively engaging with big data, and this is where training comes into play.
While some women are already taking the fight for workplace equality into their own hands with big data projects (e.g., InHerSight enables women employees to rate their companies using 14 key metrics), real change will likely come from inside organizations as they start to use their own data to find both positive and negative patterns to identify issues with individual employees and teams. Training HR leaders to use metrics to stop problems before they happen has the power to transform workplaces and prevent future problems, such as the one described by Susan Fowler. Of course, until CEOs, such as Kalanick, choose to prioritize the use of metrics to fight sexism in the workplace, current inequalities will no doubt persist.
The bottom line is that we can choose to harness big data to achieve many different goals. Kalanick and other corporate leaders can choose to use big data to drive business and promote equality. The first step is to recognize that these two goals go hand in hand, since ultimately, promoting equality and retaining great talent is better for business too.
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