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Lester Berry

Lester Berry, Director of Analytics and Data Science, John Lewis Partnership

Describe your career to date

 

I have been lucky so far, working for several great companies, across various sectors and with some brilliant people. Getting out of bed in the morning has never been a problem. 

 

My first role, all those years ago, was as an Operational Research Analyst for British Airways. I knew very quickly that I was in the right career, because solving analytical problems did not feel like work. For example, British Airways had about 250 aeroplanes, the question was, “Where should they fly?” 

 

From British Airways I moved to Lloyds Banking Group, then onto British Gas and now the John Lewis Partnership, with shorter spells at Marks and Spencer and Telewest in between. Although I have worked in many sectors, the common thread is my focus on closing the gap between analysts and business decision makers, which is often too wide. Doing analysis and data science is one thing, but advanced analytics teams add most value by improving decision making and that is what motivates me. 

 

My current role at the John Lewis Partnership is perfect. I lead a team of over 100 Analysts and Data Scientists who work on projects across the whole Partnership. My role is to ensure we are maximising value today, while also maturing and developing the Partnership’s data capability to create even more value in the future. We have made good progress and I look forward to that continuing. The recent recognition at the DataIQ Awards was evidence that we are on the right track. 

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

 

There are six pillars in our data strategy, two of which are tackling data literacy. 

 

Firstly, we have a pillar around Self-Serve. We will only make the necessary culture change and realise the value from data that we should, by putting the right data into the hands of those who need it. This will not be tackled by writing code or growing my team, instead we need to make it simple and accessible. So, we are rolling out Tableau across the Partnership and already have 5,000 active users. The vision is to scale the use of data in the Partnership while at the same time enabling the centralised analysts and data scientists to work on more advanced and value adding use cases. 

 

This will only work if we have the right talent to consume data, so a second pillar of the data strategy is Education and Engagement. There are several streams within this. For most of the part-time Analysts across the organisation, we have a Data Fluency Academy which is a set of online courses jointly created with Tableau. We also have a thriving Tableau Community with hackathons and knowledge sharing which engages hundreds of Partners each year. Further, we have launched data apprenticeships with Multiverse and in less than six months already have over 400 Partners on a formal course. 

 

Our final area to address is the Partnership’s senior leaders. Beyond Tableau, awareness of advanced analytics and data science is patchy and therefore, if we are to maximise value, we need to be clearer about the potential of the profession and where it should be used. This is work in progress, due to be launched in 2024. 

How are you preparing your organisation for AI adoption and change management?   

 

Retail is full of artificial intelligence (AI) opportunities, ranging from more traditional machine learning (ML) use cases such as promotions and forecasting, through natural language processing and image processing to generative AI (genAI). Most of the recent buzz about AI has been specifically about generative AI (genAI), but internally, this has resulted in increased awareness and appetite for all forms of AI which is enabling us to accelerate our plans. 

 

Many AI use cases have already been implemented, such as online recommendations, personalisation, and transport optimisation, but there are many more to tackle. We have recently undertaken a piece of work to firstly identify the main AI use cases in retail, to then assess the Partnership’s capability in those areas, and finally to identify gaps and therefore areas of potential opportunity. From that piece of work, we will develop a roadmap. 

 

In parallel, it is important to build the right delivery infrastructure and there isn’t one solution to support all use cases. For our internally built tools we launched an MLOps capability (Dataiku) earlier this year and are already generating significant value. For GenAI, we are working with Google and they are helping us to make a quick start with some initial use cases. In other areas, we can make use of third-party tools like Salesforce and Tableau and many more; the right decision is to buy specific tools such as forecasting and pricing. Even here though, it is worth noting that you cannot just buy tools, install them, and expect them to be optimal. There is still a critical role for an analyst to make sure the tools are tuned appropriately. That final 5% in an optimisation opportunity will give you the competitive edge. 

 

Last but not least is governance. With new capability comes new risk and a need to ensure automated decisions are being made correctly, ethically, and with customer interests at their heart. We have recently set up an internal governance forum specifically with these AI issues in mind and will build in the right way. 

Lester Berry
Lester Berry
has been included in:
  • 100 Brands 2019 (EMEA)
  • 100 Brands 2020 (EMEA)
  • 100 Brands 2021 (EMEA)
  • 100 Brands 2022 (EMEA)
  • 100 Brands 2023 (EMEA)
  • 100 Brands 2024 (EMEA)

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