Mike Hyde, chief data officer, Trainline
Describe your career to date
I’ve spent the past decade founding and scaling data organisations in big tech to drive growth and innovation in some of the world’s biggest products. My last five years were at Meta (Facebook), where I was the London site lead for data science and data engineering, steering the organisation through five years of exponential growth, and being a founding member of the Workplace product leadership team.
Prior to that I was the data director for Skype, where I built and scaled a product data organisation for Skype consumer, steering Skype through the mobile revolution and the Microsoft acquisition, after which I led data for Skype for Business, which ultimately became MS Teams. Prior to this I spent a decade in strategy consulting and had an early career founding a few start-ups during the first Internet bubble and had a business building recording studios.
I joined Trainline in 2021 as I wanted to bring all of these experiences to a European tech business which had a killer strategy, was ready to scale and had a strong environmental mission.
What stage has your organisation reached on its data maturity journey?
I’ve spent the first year putting the ingredients in place for long-term growth by hiring an exceptional leadership team from the likes of Meta/Microsoft/Uber, rebooting our hiring, completing the cloud transition and kicking off a long-term cultural change plan. We are beginning to see the benefits of increased self-serve and scaled-out data being used to drive the business, and we are scoring some machine learning customer wins, but we have several years of the journey to go to achieve full maturity.
Tell us about the data and analytics resources you are responsible for
I run an organisation of around 80 to 90 data professionals (approaching 10% of the total organisation) which cover the full spectrum of data skills. This includes data and platform engineering, machine learning and real-time advanced analytics, BI and dashboarding, experimentation, elements of our martech implementation, governance, and embedded data science.
I run a combination model where some teams operate as a centralised resource, and some are deeply embedded in the vertical parts of the business, where they play active leadership and partnership roles in cross-functional product teams, marketing and commercial teams, in the same way as most modern Silicon Valley companies.
This model allows us to centralise the assets which we need to build once and well, and decentralise other parts of the operation to move fast, to empower them to innovate and allow the model to scale while avoiding bottlenecks.
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