I was finishing my postgraduate degree at the Harvard Kennedy School, and was intrigued by a chance to work in London, for the then newly created Transport for London (TfL). I joined as a senior business planner, helping to put together TfL’s first-ever business plan. I have since worked in a range of roles, including acting head of finance for London’s Transport Museum, chief of staff to the managing director of finance and planning, the head of Oyster development, and head of analytics. These roles have allowed me to work on an amazing variety of projects, such as the launch of the contactless payment system across the capital’s transport network.
Since working at TfL, I’ve seen how the use of data can improve the way we live our lives, and it has been exciting to be at the forefront of this with the transport network in London. From the early days of our Oyster card, I looked at how we could use data to answer questions about travel patterns that were previously unknown. Over the years, I’ve worked to grow a data team that can use analytical and software skills to take a range of data sources from our transport network to provide insight and services back to our customers and our operational teams. It’s been a phenomenal journey.
At the start of 2012, I became the head of analytics at TfL, when we were preparing for the 2012 Olympic and Paralympic Games in London. Heading up our analysis of travel patterns during the excitement of the Games was fantastic. We were able to identify patterns of travel during the Games, which guided our advice to customers so we could provide the best experience for all users.
Despite what many naysayers predicted, we provided stellar transport services during the Games that were widely complimented and were integral to the smooth running of the events. It was amazing to think we’d played our part in that.
I have many but one historical figure I particularly admire was a pioneer of data visualisation and the first woman elected to the Royal Statistical Society. She’s most famous for being the inventor of modern nursing – Florence Nightingale – but her passion for analysing problems and then for taking action is inspirational.
When I thought about what 2019 would bring, I anticipated data would be even more embedded into technology solutions, and this indeed happened over the course of the year. And was further development of artificial intelligence tools. I’m proud of the work that my brilliant team has done in embracing these new analytic opportunities.
For example, we published London’s Cycling Infrastructure Database, which is the world’s largest and most comprehensive database of cycling infrastructure. There are over 480,000 photographs of cycling infrastructure in this open database. These photographs were taken, of course, in the live environment where lots of people and cars were around. Now here’s where my team came in to help. We needed to blur faces and car number plates on these images. My data scientists created a machine learning algorithm to do this, a prime example of how technology tools develop and how we can use them to solve new challenges.
I expect that new tech capability will continue to evolve at a rapid pace. Many thorny problems that were once too difficult to tackle because of lack of data or lack of analytic tools will now be solvable. But there will still be questions that we cannot definitively answer with data. Hannah Fry has recently written eloquently on this, on the need to understand people not only algorithms. And I also think that the question of data ethics and data responsibility will, importantly, continue to be at the forefront.
The opportunity for us is to apply these emerging tools and capabilities in areas to tangibly improve society. We have to be thoughtful in making sure that we focus on the right questions.
It’s becoming very clear that the biggest opportunity for us as citizens is how we can focus on our analytic skills and data capabilities on addressing the challenges facing the environment. From my perspective as CDO at TfL, my focus is on using data to support delivery of the Mayor’s Transport Strategy target of increasing public transport, walking, and cycling journeys. I challenge my data community industry colleagues to focus data efforts on this fundamental task.
The biggest tech challenge that I face is that there is an ever-growing appetite to consume data at TfL. This is a wonderful problem to have but it’s a challenge to keep up with demand. To tackle this, we’ve been working on ways that our colleagues can self-serve their data needs. We’ve laid the technical groundwork for our teams but have given them the tooling to visualise data themselves.
To do this, we’ve set out protocols for working with data and have run training sessions and weekly drop-ins to help analysts work with data. It’s an evolution from when data was locked into cumbersome legacy reporting tools and when any change required significant recoding. This has also allowed us to liberate data for our TfL teams. Our peer community of data users are also working to share knowledge of data science tools and techniques and launched a data development portal this year so we can train our staff and build data capability.