• Home
  • >
  • Daragh Kelly, Global VP, Data and Analytics, Burberry

Daragh Kelly, Global VP, Data and Analytics, Burberry

Describe your career to date 

Following an early career as a labour market and education policy economist with the Irish government, I dived deeply into the world of data at Freeserve and it was there that I learnt my trade as an analytics professional. My proving ground was a fantastic five years as a consultant at Experian, working across many sectors and around the world helping companies build and exploit their data capability.

In 2010, I joined Sky, where I worked in pretty much every part of the company, including marketing, product, customer service and content and in all parts of the insight value chain, including analytics, decisioning, business intelligence, research and knowledge management and building some ground-breaking new insight capabilities.


I joined Burberry in 2018 with a mandate to accelerate the exploitation of data across the whole of Burberry, building on a strong foundation in digital analytics and personalisation. It’s been an amazing few years, where we’ve extended the footprint of advanced analytics into new areas like forecasting, content strategy, pricing optimisation, media optimisation, sustainability and customer experience. Burberry is investing in becoming a creatively-driven but data-inspired business and I’m finding the synergy between art and science hugely stimulating and fulfilling.

What stage has your organisation reached on its data maturity journey? 

I think we’ve made huge strides in using data and analytics to operational decision-making – in trading, in media, in personalisation and CRM etc. The next step for us will be for data to have a greater impact in the really existential ‘bet the company’ decisions.


Tell us about the data and analytics resources you are responsible for

I run a UK-based team of around 30 data analysts and data scientists that reports to Burberry’s digital, customer and innovation officer. This centralised globally-focused team provides support across all of Burberry’s divisions and regions. The more distinctive parts of our model include 1) we employ a full-time insight editor that runs our insight newsroom; 2) heavy investment in engineering excellence where we hire 1.5-2 data engineers for every scientist/analyst on the books; 3) a specialist team focused only on building data science apps for business users; and 4) a specialist team focused only on the discovery and onboarding of new data sources.

What challenges do you see for data in the year ahead that will have an impact on your organisation and on the industry as a whole? 

I worry that the [justified] excitement around Generative AI will prove a distraction with lots of gimmicky PR-led initiatives on the client side and lots of hype and vapourware from vendors. The technology isn’t there yet in my opinion [although it will be in a few years I think] but the bigger problem is going to be, as always, organisation readiness and the ability/willingness to change operating models and job designs to leverage these amazing new technologies.

Have you set out a vision for data? If so, what is it aiming for and does it embrace the whole organisation or just the data function? 

We have set a vision and have a well-articulated data capability strategy. But, in terms of the wider vision for the use of data, we tend to focus more on influencing the overall business and divisional strategies than on building up a standalone data strategy – making sure that the strategies themselves are informed by data and in ensuring that the data enablers required to deliver those strategies are in place. 


Have you been able to fix the data foundations of your organisation, particularly with regard to data quality?

In a word, no. Fixing one’s data foundations is a Sisyphean task that is never complete I think. We’re doing all the obvious things around data governance, data observability etc, of course, but one area I’m particularly excited about is the use of machine learning to drive data quality and integrity. For example, we’ve had great success with using NLP and machine visions to generate high-quality and consistent product, customer and campaign meta-data – tasks traditionally performed, usually very poorly, by humans.

Daragh Kelly
has been included in:
  • 100 Brands 2019 (EMEA)
  • 100 Brands 2020 (EMEA)
  • 100 Brands 2022 (EMEA)
  • 100 Brands 2023 (EMEA)