How is your organisation using data and analytics to support the corporate vision and purpose?
This is our mission - to make great leaps in data science and artificial intelligence research in order to change the world for the better. Research excellence is the foundation of the Institute: the sharpest minds from the data science community investigating the hardest questions. We work with integrity and dedication.Our researchers collaborate across disciplines to generate impact, both through theoretical development and application to real-world problems. We are fuelled by the desire to innovate and add value.
2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?
Like many others in the scientific community, the Turing found itself playing an important role in the UK response to Covid-19. Our work on the NHS Covid-19 app improved algorithms for distance estimation between smartphones, leading to more accurate contact tracing. Other Covid-19-response work has included Project Odysseus, Decovid, our role in the Royal Society’s Ramp initiative, and research into health misinformation.
And, of course, like so many organisations, we adapted our events, training, offsites, recruiting, and so many other activities to be done remotely rather than in-person.
Looking forward to 2021, what are your expectations for data and analytics within your organisation?
The Turing will continue to advance data science and AI, building resilience, innovating, measuring and promoting inclusivity, experimenting with novel ways to convene the research community.
Is data for good part of your personal or business agenda for 2021? If so, what form will it take?
This is our mission - we seek out partnerships with businesses who also make it part of their agenda. We are looking for those who want to tackle the thorniest scientific challenges, drive societal and economic impact, uphold the highest ethical standards, embed data science into their everyday work, work with real-world data, make more progress together and activate the enthusiasm, curiosity and commitment of their people and ours.
What has been your path to power?
I’ve been fortunate that following my interests and my curiosity has helped my career. My first job was at Bain & Company, where I learned a tremendous amount about business strategy and change management.
After INSEAD, I moved to New York to work on turning round the music industry. I didn’t manage to fix it all, but we built a data mash-up and dashboard when Hadoop was new, MySpace was where the fans were, and media mix models weren’t a standard ad agency offer. It wasn’t called data science back then, but it was the beginning of data science.
I went on to learn from the excellent decision scientists at American Express and built a diverse team of statisticians, business analysts, risk modellers, and technologists. After that, I A/B-tested my way up the growth curve at a fintech start-up, taught myself a little coding with a lot of copy-pasting from more experienced coders, and moved to London.
Here, I’ve followed my curiosity once again, assembling an extraordinary team at the Turing who are creating the future of data science and artificial intelligence. I’m enjoying the value of seeing the many possible futures, given my intense implementation background.
What is the proudest achievement of your career to date?
I love it when existing ideas come together in novel ways. My habit of reading and learning broadly will usually, eventually, bring me a useful concept or expert.
I had a major achievement at AmEx, using ideas I had learned outside of data and analytics. My team booked a billion-dollar-a-year win when we reframed a supposedly intractable problem by changing the question. Without divulging any confidential information, that one suite of algorithms was good for consumers, delivered double-digit improvements to the P&L year-in and year-out, and pieced together existing technology in a novel way.
Tell us about a career goal or a purpose for your organisation that you are pursuing?
Reproducible research is work that can be independently verified. In practice, it means sharing the data and code that were used to generate published results - yet this is often easier said than done.
"The Turing Way" is a guide to reproducible data science that will support students, their supervisors, funders and journal editors in ensuring that reproducible data science is "too easy not to do". It will include training material on version control, analysis testing, and open and transparent communication with future users, and build on Turing Institute case studies and workshops.
How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?
This is our mission - what helps to bring the two closer together are the flexible models of engagement, for larger corporates to small enterprises, ranging from one-week data study groups and three- to six-month internships, through to multi-year research programmes and targeted collaborative research projects.
Guided by our challenge areas and collaboration principles, our engagement with partners and collaborators focuses on an exchange of talent, skills, and knowledge, with industry and academics learning from each other and sharing best practice.
We are interested in challenges that allow data science and AI to be applied to real-world problems, as well as difficult cross-disciplinary problems that have the potential to yield positive transformation across sectors. Our researchers, data scientists, and software engineers can provide novel solutions to industry challenges, drawing upon the latest developments in mathematical, statistical, computational, engineering, and social sciences.
The Turing also has interest groups which bring together researchers and practitioners so they can share knowledge, spark new ideas for collaborations, and communicate emerging concepts to the Turing community and beyond.
What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?
I have found that a culture of high performance and continuous improvement is the best one in which to develop a data culture. Businesses that focus on what data and analytics can do to, say, improve their customer experience, shorten production cycles, or reduce maintenance costs are the ones that successfully weave data into areas of impact. A strong data culture supports the strategy, the vision, the purpose.