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The John Lewis Partnership is building the foundation for AI success by creating a centralized, self-service data hub powered by Collibra. Through a collaborative governance framework, John Lewis Partnership is delivering trusted data products at scale, enabling faster, more confident decisions and strengthening oversight of AI initiatives.

In this session, you’ll learn:

• How JLP is overcoming fragmented, unreliable data with a single source of truth

• What drove adoption and business alignment for Collibra

• How trusted data products are accelerating AI readiness and governance

In this episode, host Jason Foster sits down with Barry Panayi, Chief Data and Insight Officer at John Lewis Partnership to discuss the evolving role of the Chief Data Officer (CDO). Barry shares his journey from coding and analytics to leading data and insights at iconic brands like John Lewis and Waitrose. He offers a unique perspective on how CDOs can transition from technical experts to strategic business leaders. Barry's candid reflections and actionable advice make this episode essential listening for data professionals, aspiring CDOs, and anyone interested in the intersection of data, technology, and business leadership. Don't miss this engaging and insightful conversation!    *****      Cynozure is a leading data, analytics and AI company that helps organisations to reach their data potential. It works with clients on data and AI strategy, data management, data architecture and engineering, analytics and AI, data culture and literacy, and data leadership. The company was named one of The Sunday Times' fastest-growing private companies in both 2022 and 2023, and recognised as The Best Place to Work in Data by DataIQ in 2023 and 2024.  

PyDataMCR October

THE TALKS

The Data Architecture behind John Lewis Partnership's Return to Profit - Anna Aleshko (She/Her) and Jacopo Coluccino (He/Him)

In this presentation, Jacopo and Anna will take you through the data journey of the John Lewis Partnership, exploring the evolution that led to the creation of the Partnership Data Platform (PDP). We'll delve into the history, the challenges we faced, and the strategic decisions that brought us to where we are today. Additionally, we'll share a real-world example of how PDP teams are able to deploy production ready data pipelines with external third party integrations in a matter of days.

Anna Aleshko is a Data Engineer at John Lewis Partnership where she is part of the Waitrose Online team. With an educational background in biochemistry & experience spanning several industries, she brings a unique perspective to her work. As Founder & Co-Organizer of Data Engineers London, she is passionate about knowledge sharing & community engagement among data professionals.

Jacopo is a Data Engineer at the John Lewis Partnership and a Software Engineering Master's student at the University of Oxford. In his current role, he leads the Partnership's Customer Data team, where he is instrumental in establishing a robust customer data infrastructure on the new Snowflake platform. With a passion for crafting Python solutions grounded in solid object-oriented design, Jacopo excels at integrating business insights with data-driven decision-making.

Revamping our A/B testing methodology - everything is a histogram if you squint! - Dustin Hayden (He/Him), Tom Armitage (He/Him)

Auto Trader is always striving to improve the user experience on the website, constantly making changes. Understanding the impact of these changes is vital to know if our changes are having the intended effect. A/B tests are the go-to quantitative gold standard, but there are many different methodologies to choose from when it comes to analysing the data. In this talk, Dustin and Tom will discuss Auto Trader’s shift to a Bayesian framework, the benefits of this, the challenges, and how they developed approaches to overcome them.

Tom is a data scientist at Auto Trader with a background in computational astrophysics, where he first developed an interest in ML. He has worked on various products involving imagery, forecasting, and optimisation, with his current projects focusing on search and recommendation systems.

Dustin is a data scientist at Auto Trader with a background in neuroscience. He is currently developing models for search at Auto Trader and models for polyphonic note detection at home.

Location We'll be at AutoTrader, who are kindly supplying the venue and catering. The capacity is limited to 80.

EVENT GUIDELINES

PyDataMCR is a strictly professional event, as such professional behaviour is expected.

PyDataMCR is a chapter of PyData, an educational program of NumFOCUS and thus abides by the NumFOCUS Code of Conduct

https://pydata.org/code-of-conduct.html

Please take a moment to familiarise yourself with its contents.

ACCESSIBILITY

Under 16s welcome with a responsible guardian. There is a quiet room available if needed. Toilets and venue are accessible.

SPONSORS

Thank you to NUMFocus for sponsoring Meetup and further support.

Thank you to AutoTrader for their sponsorship and for the awesome venue and catering!

Thank you to Krakenflex for sponsoring PyDataMCR.

Thank you to John Lewis for sponsoring this PyDataMCR event.

Beyond Spreadsheets with R shows you how to take raw data and transform it for use in computations, tables, graphs, and more. You’ll build on simple programming techniques like loops and conditionals to create your own custom functions. You’ll come away with a toolkit of strategies for analyzing and visualizing data of all sorts using R and RStudio. About the Technology Spreadsheets are powerful tools for many tasks, but if you need to interpret, interrogate, and present data, they can feel like the wrong tools for the task. That’s when R programming is the way to go. The R programming language provides a comfortable environment to properly handle all types of data. And within the open source RStudio development suite, you have at your fingertips easy-to-use ways to simplify complex manipulations and create reproducible processes for analysis and reporting. About the Book With Beyond Spreadsheets with R you’ll learn how to go from raw data to meaningful insights using R and RStudio. Each carefully crafted chapter covers a unique way to wrangle data, from understanding individual values to interacting with complex collections of data, including data you scrape from the web. You’ll build on simple programming techniques like loops and conditionals to create your own custom functions. You’ll come away with a toolkit of strategies for analyzing and visualizing data of all sorts. What's Inside How to start programming with R and RStudio Understanding and implementing important R structures and operators Installing and working with R packages Tidying, refining, and plotting your data About the Reader If you’re comfortable writing formulas in Excel, you’re ready for this book. About the Author Dr Jonathan Carroll is a data science consultant providing R programming services. He holds a PhD in theoretical physics. We interviewed Jonathan as a part of our Six Questions series. Check it out here. Quotes A useful guide to facilitate graduating from spreadsheets to more serious data wrangling with R. - John D. Lewis, DDN An excellent book to help you understand how stored data can be used. - Hilde Van Gysel, Trebol Engineering A great introduction to a data science programming language. Makes you want to learn more! - Jenice Tom, CVS Health Handy to have when your data spreads beyond a spreadsheet. - Danil Mironov, Luxoft Poland

Visualizing Graph Data teaches you not only how to build graph data structures, but also how to create your own dynamic and interactive visualizations using a variety of tools. This book is loaded with fascinating examples and case studies to show you the real-world value of graph visualizations. About the Technology Assume you are doing a great job collecting data about your customers and products. Are you able to turn your rich data into important insight? Complex relationships in large data sets can be difficult to recognize. Visualizing these connections as graphs makes it possible to see the patterns, so you can find meaning in an otherwise over-whelming sea of facts. About the Book Visualizing Graph Data teaches you how to understand graph data, build graph data structures, and create meaningful visualizations. This engaging book gently introduces graph data visualization through fascinating examples and compelling case studies. You'll discover simple, but effective, techniques to model your data, handle big data, and depict temporal and spatial data. By the end, you'll have a conceptual foundation as well as the practical skills to explore your own data with confidence. What's Inside Techniques for creating effective visualizations Examples using the Gephi and KeyLines visualization packages Real-world case studies About the Reader No prior experience with graph data is required. About the Author Corey Lanum has decades of experience building visualization and analysis applications for companies and government agencies around the globe. Quotes Shows you how to solve visualization problems and explore complex data sets. A pragmatic introduction. - John D. Lewis, DDN Excellent! Hands-on! Shows you how to kick-start your graph data visualization. - Rocio Chongtay, University of Southern Denmark A clear and concise guide to both graph theory and visualization. - Jonathan Suever, PhD, Georgia Institute of Technology Great coverage, with real-life business use cases. - Sumit Pal, Big Data consultant

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