Efficiently matching supply with demand is the bread and butter of tech platforms operating in the gig economy.
AirBnb matches spare rooms with short-term renters.
Deliveroo matches restaurants with hungry consumers, via a network of riders. Uber matches car owners with people who need lifts.
Each transaction creates rich data. When processed, that data becomes information. With Uber in particular, when analysed, the information paints an incredible portrait of the urban environment, which can be used to drive business, making the firm more competitive, and more profitable.
This data is not just a valuable asset, but the very crux of Uber’s competitive advantage.
Last week, Transport for London (TfL), in a thinly-veiled reference to Uber, following years of conflict, released a policy statement for private hire services in London. Buried on page five, it suggests: “operators should share data with TfL, so that travel patterns in London and the overall impact of the services can be understood.”
It’s not actually clear what sort of data TfL is after. A spokesperson said that the organisation hasn’t “got anything more to say than is in the policy statement for now,” which is very little. “More details will follow in the coming months.”
TfL is expecting the total cash from Tube and bus fares to drop by £56m in this financial year.
Data value
The data Uber collects from the thousands of journeys it facilitates is of unquestionable value to TfL. Gaining insight into how the city moves could save TfL money at a time when it is expecting the total cash from Tube and bus fares to drop by £56m in this financial year.
For example, understanding where hotspots of activity occur on the app – where people are using less public transport – could lead to TfL making efficiencies on underused bus routes. Knowing where and when journeys start and end could enable TfL to better react to demand, perhaps devising some sort of demand response service.
Likewise, with 40,000 drivers using its app, Uber has (it is widely accepted) exacerbated the capital’s growing congestion problem. Better understanding traffic trends could help TfL to plan roadworks and major public events. Quantifying the effect of disrupted public services could prepare TfL for incident response.
The list goes on.
“Nobody has a crystal ball to predict long-term needs,” says Nathan Marsh, intelligent mobility lead director for UK & Europe at urban planning behemoth Atkins. “Big data provides context and real-time accuracy about how people use them, which urban planners can utilise to better determine future trends, and to build in agility and flexibility”.
Hand it over
The usefulness of Uber’s data is clear. But why should it give up any part of its greatest – and arguably only – competitive advantage to the state? Diktats of this nature simply don’t occur in more established industries. It highlights TfL’s struggles with a new business model that doesn’t fit with existing regulations.
TfL will need to explain why disclosure of this data is necessary for it to perform its regulatory functions
“TfL will need to explain why disclosure of this data is necessary for it to perform its regulatory functions,” says Michael Stacey, senior associate at Russell-Cooke.
“TfL’s job is primarily to decide whether the applicant is a fit and proper person to hold a private hire operator’s licence. It is not clear that detailed journey data is necessary to enable it to judge whether applicants meet that test.
“The onus will be on TfL to either justify why this is required under its existing powers, or seek new powers to obtain this information from operators.”
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