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September 24, 2019

Convenient Data is King

By: James Gill

Ridesharing’s influence on more than transportation

Unless you’ve been living under a rock for the past decade, you’ve undoubtedly been in contact with some form of rideshare vehicle. And even if you are under a rock, I’m sure you’ve still noticed an abundance of sticker-clad windshields cruising about.

While there are as many opinions on rideshares as there are vehicles on the road, the single consistent trend I’ve noticed is the Ubers, Lyfts, and Sidecars of the world are helping people get from point A to point B (or from A to C to F to B if you’re riding in a pool) more effectively than ever before. 

The shortest distance between two (or more) points

Way back in 2014, before my time at Narrative Science, I used to bartend in San Francisco. I’d finish work around 3 a.m., after public transportation stopped running but before the raccoons gave back the streets. I didn’t have a car, my bike had been stolen in my first week, and I lacked the burning desire to walk several hilly miles home. So, Uber Pools were my saving grace. While the technology wasn’t perfect—it would sometimes take 20 minutes to get a driver or my pickup location would appear somewhere in the middle of the Pacific Ocean—it was convenient.

I grew up familiar with the adage “Convenience is king.” Given the rip-roaring times we live in since the advent of the internet, I’ve heard the phrase evolve into “Data is king.” Now, I don’t think one is more correct than the other, but rather, they’re separate sides of the same bitcoin: “Convenient data is king.”

If you asked a random stranger on the street their feelings about rideshares, they would probably walk past you because they’re looking at their phone. But if you did manage to catch their attention, they might say they love them, hate them, or they fall somewhere in the middle. This is not fascinating. In fact, it’s painfully human. However, If you dig in and ask that same person why they feel that way, then you’re getting into the impossible meat of the matter.

People in Chicago likely won’t answer that they prefer Lyft because the average ride time is 13 minutes from Lincoln Park to Bronzeville or that they dislike Uber because the average pooled ride adds an additional 3.6 stops. No, what people likely will tell you is that they don’t use Uber Pools because of the one time that guy tossed his cookies in the backseat or that they love, love, loved their Sidecar driver who knew the same indie reggaeton artist as them.

Why people will tell you this is because what humans find impactful, meaningful, and memorable rarely has anything at all to do with logical reasoning. Is this because we are inherently illogical? I don’t think so. At least not when it comes to decision-making. Rather, in the high-paced, information-overloaded world we live in, it’s becoming increasingly difficult to trace back the causality of our decisions.

Enter data storytelling

Here’s a concept that up until now featured two seemingly adversarial entities: data, which provided the logic, and words, which told the story. Where these two concepts were previously disconnected by the limitations of our own computational power, technology now allows computers to do much of the thinking for us—given that we are able to define what’s important for the system and point it toward specific outcomes. That frees us up to focus on the decision-making.

Think of data storytelling like the rideshare map on your phone. It shows you where your driver is located, enabling you to consider:

  • Are they three minutes away or 11? 
  • Are they completing a current ride or picking up another rider? 
  • Do you have time to use the bathroom? 
  • Are you going to be late for your appointment?
  • How much longer do you have to be in the car with the guy that ralphed?

All of this data can now be used to inform your decisions, provide guidance on your path moving forward, help you plan and account for time and resources, and it all happens instantaneously. But do you need to understand the algorithm to make use of it? Not at all, the output simply needs to be easily ingestible.

Fast forward to spring 2019. I’ve just started as a solution consultant at Narrative Science. My first project involves making use of publically available data on Chicago’s Transportation Provider Network. All rideshare operators are required to submit their operating metrics to this portal for reporting purposes, but the data is publicly available—all 43 million data sets.

With that much raw data, there has to be some interesting insights and relationships to be gleaned. But where do you even begin to sort it all? Remember that whole advent of technology thing I mentioned earlier? Well, it turns out that computers are significantly better at crunching numbers than humans, and by properly formatting schemas to inform these calculations, we can leverage this computing power to help us make better decisions. The goal here, remember, isn’t to make people dumber but to remove the barriers from smart decision-making. 

At the risk of oversimplifying what is an extremely complicated process, imagine that you want to know when the best time to call a rideshare is on Friday nights in the West Loop. 

It’s not quantity, but quality

The methodology to come up with this solution is not new. In fact, reference class forecasting—which is just a fancy term for comparing performances—has been around since John Venn began radically diagramming circles back in the 1870s (yes, that Venn diagram). But the tools to delve through tens of millions of rides in a matter of seconds have only come into existence quite recently.

In this modern era where we monitor and report on everything—calories, spending habits, step count—there is no shortage of insightful information. But if our data is evolving exponentially, so must our delivery systems. Otherwise, we risk falling behind in our understanding. The information we crave is out there, but because it’s not convenient, we don’t know how to use it. It’s quite literally the difference between eating a whale and eating a caterpillar roll. 

Data storytelling is the primary mission of Narrative Science. Our purpose is to alleviate these pain points so that end users, decisionmakers, and curious minds of the world can get where they’re going as efficiently as possible.

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