If you’re one of the 75 million people worldwide who actively use Uber, you know how critical it is that your ride shows up on time. Equally important is that the app functions properly—what happens if you have just 30 minutes to get to the airport and you aren’t able to sign in and request a ride?

These seem like straightforward things to plan for, but for Uber and other network transportation providers it’s anything but. They must account for supply and demand fluctuations like holidays or local events, technology challenges across every mobile operating system, and regional issues that could impact service, all while monitoring a staggering number of signals.

It’s a large remit, but it’s one that Uber’s Director of Data Science, Franziska Bell, plans to solve by making data analytics more pervasive throughout the organization.

Data Science at the Touch of a Button

Speaking at Transform 2019 in July, Bell outlined her vision for providing “data science expertise at the touch of a button.” Her approach—matching Uber’s data scientist teams with their counterparts in engineering, product, and design—has resulted in new tools and platforms that can be utilized by anyone in the organization, regardless of their degree of technical skill.

She described one of these in detail during her Transform talk: a forecasting platform, jointly developed by cross-functional teams, that Uber uses to predict supply and demand, detect outages in real time, and undertake hardware capacity planning. VentureBeat quotes her describing the platform, “Absolutely no forecasting expertise [is] required. The only input that’s needed from the user is historical data, whether it’s in the form of a CSV file or a link to a query, as well as the forecast horizon…Everything else is done completely underneath the hood.”

It’s a fascinating example that underscores how data can transform business processes and how non-technical stakeholders can take an active role in driving this transformation. But in order for organizations to follow in Uber’s footsteps, it’s critical that their data be cleansed, integrated, and made accessible and actionable.

Here at APEX of Innovation, this is a topic we frequently discuss. Check out some of our previous posts on enabling a self-service analytics culture here, here and here.

And next time you summon an Uber, take a moment to appreciate the work Bell and her team put into ensuring you reach your destination as scheduled.