This is a profile from the 2020 version of the DataIQ 100.
The 2021 list is available here
I studied finance at Loughborough University. After finishing my degree, I joined PwC’s Transaction Services’ graduate scheme in London in 2010, which was a great opportunity to work with private equity houses and management teams at a variety of UK businesses.
In 2014, I joined Close Brothers as a strategic analyst, an internal consultancy role at the FTSE250 specialist financial lender. In 2016, I started to focus on data initiatives. It was an exciting and fast-developing area in the financial industry and we wanted to test whether it could generate commercial benefits for the bank. We ran several successful proof of concept initiatives with external data science teams in 2016 and 2017. At the end of 2017, we established a brilliant internal data science team, which I have led since.
Building a new data science team in an established business has been a very rewarding experience. From running proof of concept projects with external data scientists, putting an in-house team business case together to successfully delivering our first project has been an exciting journey. I had a lot of support from the Close Brothers leadership team along the way. And, as a result, we have a brilliant data science team that is delivering commercial value across the bank.
I’m a big fan of Simon Sinek and his people first business and leadership philosophy. I highly recommend his books, "Leaders Eat Last", "Start with Why" and "Infinite Game".
It was great to see the positive impact GDPR has played on how companies deal with personal data. The approach to dealing with personal data is shifting from, "how can we benefit from the data we have access to?", to, "how can we best look after customers’ data that they’ve entrusted us with?". Personal data is an asset that has been collected by the company, but ultimately belongs to the customer.
I expect ethics and bias will be a big focus in 2020. Machine learning is a powerful tool in generating automated decisions. But you need to be careful when creating the models to make sure you eliminate or limit the bias. I hope there is an increased focus on how the algorithms are built and used, not just on building the most complex models.
Better understanding of the world around us is the biggest opportunity. Whether that’s optimising business processes or diagnosing diseases earlier. Data and technology allow government, organisations and businesses to serve citizens and customers better. This means better customer service, more tailored products and services at lower prices.
Key to successful digital customer journeys is customer data. However, often customer data is stored in multiple siloed systems. It can be a challenge to bring all the data together, when systems capture data in different formats and using different definitions.
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