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Maria Vounou, Director of Data Science, Burberry

Maria Vounou, Director of Data Science, Burberry

Describe your career to date

I joined Burberry as a Data Scientist in April 2013, after completing my PhD in Statistics at Imperial College where I applied machine learning techniques to large scale genetic and brain imaging data. I was one of the first members of the newly established Data Function in Burberry, and during the early stages of my career I played a key role in setting up the foundations and in embedding data-driven decision making in a design-led and product-led company. Since then, in my ten-year journey at Burberry, I grew my career by taking on additional responsibilities in relation to our data and analytics strategy, achieved multiple promotions to more senior roles, and I have been promoted to Director of Data Science. During these years, I have built from scratch a team of 18 highly talented Data Scientists that I largely recruited straight out of university, and I supported in mentoring, developing, and progressing their own careers, with several of them eventually taking on managerial and leadership roles within my team. Over the last five years, I have focused on expanding the footprint and impact of data science across Burberry and my team now supports several functions including digital, customer, marketing, content design, product, merchandising, supply chain, finance, and commercial. Through a close partnership with our technology team, I have set up an effective working model across Data Science, Data Engineering, and ML Engineering, where my relatively small team delivers cutting-edge DS products at scale and contributes tens of millions of pounds in yearly benefits for Burberry.

Data literacy is a key enabler of the value and impact from data. How are you approaching this within your organisation?

Data literacy has been a continuous journey at Burberry, and we have been employing several strategies towards creating a more data literate organisation.

1) Collaboration: Creating close partnerships with business stakeholders has been one of our most successful ways to improve data literacy and increase the effective adoption of data in business decisions. We found a co-creation, co-development approach (where the business user is driving the requirements and acting as a product owner) gives us the opportunity to educate and create trust along the journey, while empowering users to leverage data in their everyday tasks.

2) Knowledge sharing: We are also running several initiatives to share knowledge, educate, and inspire our colleagues. One of our biggest successes this year was a business-wide offsite that I chaired, centred around generative AI, capitalising on the recent excitement with AI. We presented introductory material and showcased our current innovations to educate, excite, and generate opportunities. We also run several other initiatives, like data reading clubs, discussing articles with stakeholders to educate, and upskilling in specific areas.

3) Training: On the technical side, over the last four years, we have been running data apprenticeships which are open to interested employees across Burberry, and designed to equip them with data skills that can be directly applied to their day jobs. Finally, and importantly, we always invest in the development of our core data team, providing the necessary development time, material, and opportunities to upskill and stay on top of the latest trends in their field.

What are the key challenges to your data function that you are facing as its leader?

Although we have a relatively mature function with strong data foundations and advanced capabilities that already deliver a lot of value to Burberry, there is still massive unrealised potential. Securing the right investment and business partnerships to continue our data management and data exploitation journey will be key. Some of the challenges we face relating to scaling the adoption and value from data include:

1) Strategic alignment: We have been most successful in areas where we were able to align our data strategy to the business strategy and build a roadmap that delivers products directly relating to our stakeholders’ priorities and needs. However, the clarity and alignment we can achieve varies substantially by area, largely depending on its data maturity. Therefore, finding the right balance between building understanding of the business area and challenges as well as proactively building products for inspiration is key.

2) Business processes: Particularly in large organisations, business processes and decision making can be quite convoluted, making the corresponding data management as well as the creation and adoption of data products challenging. Raising awareness and appetite at the Executive Commitee level has proven crucial to unlock these situations thus far.

3) Resistance to change: Given the pace of change in data and technology, resistance and fear of change is another challenge we see in data and AI adoption. We continuously approach this by investing in data literacy and ensuring we build products that both enhance our business as well as empower our stakeholders to perform their jobs better.

Maria Vounou
has been included in:

100 Brands 2024 (EMEA)

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Maria Vounou, Director of Data Science, Burberry

Join our membership network of over 250 global senior data and AI leaders.

The trusted global collaboration and intelligence platform for data leaders.

Join our membership network of over 250
global senior data and AI leaders.