Does data now have a seat at the table during strategic discussions? If not, what will it take to get it there?
As a product- and mission-driven tech company, Bumble naturally puts data at the heart of its decision-making and strategy-shaping processes. For instance, we use data extensively in our annual planning process to formulate the strategic discussion points and drive product development. This data-driven approach necessitates sustained efforts in maintaining and evolving our strong data infrastructure and capabilities, as well as continuously driving a culture where every decision taken is based on tangible data and insights. This approach is happening at all levels of the organisation and decisions are made faster as data is widely distributed across the company.
What are your key areas of focus for data and analytics in 2022?
In 2022, we will keep accelerating our data transformation, both from a technological and a cultural standpoint. Concretely, this means continuing the work on self-serve analytics coupled with a clear strategy and programme of work around data literacy to reach all audiences. We will also continue our shift towards our target cloud infrastructure to enable more flexibility for our data operations. Finally, we will be expanding our machine learning teams and capabilities with ambitious hiring plans and strong investment in core machine learning engineering toolsets and experimentation frameworks.
Tell us what leadership means to you in the context of your role as a senior data leader.
It might sound like a cliché, but it all starts with a clear vision. This includes understanding the strengths and weaknesses of the current data set-up, weighing the opportunities at hand to fulfil the business mission and objectives, and having the communication skills to listen to our people and convey a compelling data story both internally and externally. I tend to focus my efforts around these four pillars when managing my data organisation: talent, culture, mission and operating model.
What key skills or attributes do you consider have contributed to your success in this role?
Beyond any technical knowledge I may have, the key to my successes has clearly been my people-first approach to problem-solving and team management. Delivering machine learning systems at scale is an extremely complex endeavour and I strongly believe that we can only succeed if we have a high-trust and collaborative environment that fosters continuous learning and innovation.
How did you develop – and continue to develop – these skills or attributes?
Mainly by being curious and having a positive attitude towards new challenges! Data science is at the crossroads of many disciplines, so there’s always something new to learn there. It is also important to remain humble, as well as honing an ability to sense what will be the data trends in the near future. The technology and capabilities are moving fast and the data landscape can certainly feel very crowded at times, so being able to select the right information becomes critical. Today, I’m prioritising my people skills through coaching and mentoring as these are proving essential in handling complexity and uncertainty.
