Measuring the Cost of Uber’s Push Into Spatial Data
The recent “news” stories that Uber was doubling-down and developing a spatial database to support the company’s mission seemed anti-climactic to me. After all, over the last two years Uber has undertaken numerous (perhaps that should be “nuberous”) activities to support its strategic initiative in mapping. What was newsworthy, apparently, is the fact that Brian McClendon, formerly head of Google Maps, now head of Uber Maps), wrote a PR piece in the form of a blog focused on the importance of mapping to Uber.
Perhaps of more interest was another byline on the topic indicating that Uber was prepared to spend $500 million pursuing just the right set of geographic data that would be needed to meet the operational needs of the business. Of course, no one at the company would comment on the figure, where the number come from, or if it was in the ballpark. Hmmm. I suspect that the correct answer was, “No comment” and the truthful answer was, “More than you can say!”
(I note here that mapping may be of less interest to Uber than creating spatial databases, but, I guess if you want to think of spatial databases as maps, that’s OK, as long as you don’t think that autonomous cars are going to “image” the data they require for operating in order to use it.)
Uber was rumored to be interested in acquiring the mapping company HERE from Nokia, but not interested enough to pay the approximately $3 billion a consortium of German car manufacturers paid for the company. In McClendon’s blog titled Mapping Uber’s Future”, he indicated that, “Existing maps are a good starting point, but some information isn’t that relevant to Uber, like ocean topography.” I agree.
Many data elements of interest to users of Google and Apple Maps would appear to be irrelevant to the needs of Uber, just as they are to the data needs of HERE and TomTom. However, I hope McClendon was not implying that he could build a database that would meet Uber’s need for less than the sums spent by either HERE or TomTom. Further, I presume McClendon knows that Google spent more than $500 million to create the data that supports routing using Google Maps to support its worldwide markets.
I suspect $500 million is just the first tranche of investment required for Uber to meet its mapping goals. I base this on a simple requirements analysis. Uber will need to define the types of spatial data required to support its corporate goals in, at least, the following areas:
1. What types of spatial data (elements, items and attributes) will be required to allow autonomous cars to safely and legally operate on all streets and roads in the geographies where Uber desired to provide such capabilities?
a. In addition to answering questions about “why these geographies”, the company will need to estimate the cost of identifying, acquiring and sourcing the required data for each unique geography and for each specific autonomous technology utilized by the vehicles they eventually choose to operate.
b. There are no shortages of existing spatial data and navigation standards, but Uber has indicated that it will become its own standard and the interplay between how it hopes to collect most of its spatial data (technology and sensors) and formulating spatial databases that can provide the actual re-use of that data by its fleet of vehicles will be a challenge in a real-time environment.
2. What types of spatial data will the company need to provide their drivers in order to efficiently deploy this human resource?
a. In addition to the mapping, routing, navigation services, and software to support spatial queries, capturing traffic patterns and the precise pick-up and drop-off locations mentioned by McClendon will require a fiendish amount of work reporting data that changes with amazing rapidity (by day of week and time of day). These concerns, perhaps, would best be met by an acquisition, or several of them.
3. What types of spatial data will need to be supported in order to meet the needs of Uber’s riders?
a. While McClendon indicates that the company won’t need underwater topography, it will need data about users, where they travel, when they travel and where they go. It will need to develop location dictionaries that include the diverse names that users may call the same location and be prepared to translate foreign languages using foreign variant names for these locations. User preferences, user histories, travel trends, location biases and a host of other data might make this stew quite potent, but knowing which ingredients are worth the cost of collection is a complex undertaking.
4. What type of spatial data must be in the database to meet company needs?
a. For example if Uber goes into the package delivery business it will need to develop an infrastructure that would meet these needs. It would also need a business listings database for the millions of small businesses that are operating, but not retail establishments. What other geography-related goals does the company have that its spatial database will have to support?
5. What kind of spatial data handling infrastructure will Uber implement on a worldwide basis to meet the general needs described in the four categories of requirements described above?
a. Do you think you could build even this part of the equation for $500 million?
If Uber has done a requirements analysis that is focused on these and other issues that need to be addressed in the creation of the complex spatial database they will need to operate their business, they will have concluded that they are going to gobble up a ton of cash before they are operational. While HERE may not have been a bargain, its selling price is a not irrelevant estimate of how much it will take to start Uber’s effort at building a functional, extensible spatial database.
Wow, this will be fun to watch!
And Something Else
On a completely different topic – earlier this year, I received The Distinguished Career Award of the Cartographic and Geographic information Society (CaGIS). If interested you can read about the award at the society’s website . I am not sure if this is the organizational equivalent of the “Old Geezer” award, but I was thrilled to receive it. Adding to the fun, I received the award in San Francisco, my birthplace. Does this mean I am spatially autocorrelated? Who knows?