CDO interview: Barry Panayi, chief data and insight officer, John Lewis Partnership
Barry Panayi, chief data and insight officer at John Lewis Partnership (JLP), has a giant ardour for info. So large, in reality, that he admits to being a little bit of an obsessive.
“I’m either one of the most boring or interesting people you’ll ever meet, depending on what you’re into,” he says. “I’m incredibly narrow – so there’s not much chat outside of data, analytics and insight. That is what I’ve done for well over 20 years now.”
Panayi, who has been with the partnership for simply over 18 months, leads the data administration, governance, analytics, analysis and data science groups for the group. After a profession spent honing his data management expertise at a number of the UK’s largest organisations, Panayi is placing his obsession with data to make use of for the good thing about John Lewis.
“I feel my job is to open the doors for my team to do their work,” he says. “The team is just over 200 people, and they need to be able to operate. I try to create the environment so that they can work effectively, whether that’s focusing on structural elements, the technology or something else.”
Building data and management experience
Panayi’s first job after commencement was with a advertising company that was utilizing data for junk mail, which he says is how he began to be taught his commerce. After that, he began shifting by means of industries, corporations and management roles. He spent a substantial time with EY, serving to to determine the advisor’s first data insights follow.
“That was brilliant timing,” he says. “The phrase ‘big data’ had just taken off, so I could ride that wave and it was accelerating massively at that time in the mid-2000s.”
Panayi then turned head of data and analytics for the Virgin Group, head of data science for Bupa and, previous to becoming a member of JLP, group chief data and analytics officer at Lloyds Banking Group. He recognises that this plethora of roles has helped sharpen his consciousness.
“I’m either one of the most boring or interesting people you’ll ever meet, depending on what you’re into. I’m incredibly narrow – so there’s not much chat outside of data, analytics and insight”
Barry Panayi, John Lewis Partnership
“That’s the gamble I took,” he says. “I don’t see many data professionals bouncing around the banks or the retailers or healthcare, which is fine. But I really do enjoy seeing stuff that I learned in one place, picking the good bits and learning from all the things I do.”
Panayi says his position at EY helped to construct confidence as a result of it was a cross-industry place. “One day I’d be working on a pricing algorithm for maxi dresses, and the next day we were trying to reconcile energy trades. You can apply the same techniques, tools and learnings from one place to another,” he says.
“There is some industry knowledge, of course. But I’ve tended to find that whenever I go somewhere, there’s a tonne of people who know more about what the business does than me and I can learn it. So, am I a retail expert? Absolutely not. But there are 80,000 other people at the partnership who get it. I deliberately aim to be the voice from outside.”
Taking on a contemporary problem
Panayi says he was attracted by the immense cache of the JLP manufacturers – John Lewis, Waitrose and John Lewis Financial Services – which he says provide a singular mix of non-food retail, grocery retail and monetary companies.
“It fills that gap where I like to apply my knowledge across different industries,” he says, earlier than saying he was additionally attracted by the goals of the enterprise. “I met the new chairman, Dame Sharon White, and I was absolutely sold on her vision – brand new chairman, brand new board, and trying to use data and put it at the heart of everything we do.”
Panayi says John Lewis will not be massively data- and technology-driven historically. He says the corporate is well-known for its nice customer support however now should take into consideration learn how to keep it up delighting its shoppers in a digital age. Luckily, he believes all these challenges might be probably solved by means of his staff’s canny exploitation of data.
On a day-to-day foundation, Panayi says the work of his staff is cut up into three important elements. First, data administration, which covers governance and privateness, but additionally utilizing and presenting info in a approach that is smart for purchasers. In easy phrases, that work is about guaranteeing merchandise are accurately tagged in order that they are often discovered and purchased.
The second key space is data science and enterprise intelligence. Organisational challenges can vary from setting pricing and promotions to designing employees rotas and onto discovering probably the most environment friendly routes for lorry supply drivers. He says data science will help John Lewis take care of these considerations.
Finding solutions to essential questions
The closing factor of Panayi’s remit covers analysis and insight, which incorporates quantitative and qualitative evaluation. He says taking duty for this type of analysis is fairly uncommon for a data chief. The excellent news, nevertheless, is that this side of the position actually appealed to him.
“It gave me the chance to work with my director of research and insight again, who I worked with at Virgin. He’s come over now to work with me on this programme. We take what the customers say, which is the qualitative data, and then we compare that to what they have actually done and we look for patterns,” he says.
Panayi says the analysis usually covers particular business-led subjects. One instance is the launch of a brand new services or products, such because the not too long ago launched Anyday vary, which goals to offer inexpensive merchandise at a top quality in a sustainable method.
“So, we were asking, ‘What sort of products should be in there, what sort of prices do people want and do people associate our brands with them?’ Any new proposition would go through there. But it’s also about understanding how people are feeling about key issues, such as affordability, ethics and sustainability – how important are those things?” he says.
“Half the work comes from a particular place in the organisation, which has a question that they want to understand and get close to customers. Sometimes we bring customers into board meetings to talk to members of the executive committee. We invite them to have tea and cake. But sometimes, there are just themes that we spot that are coming and that we want to ask customers about.”
Creating capabilities and platforms
Panayi is simply over 18 months into the position and says one of many issues he’s most happy with is growing the aptitude internally to serve the enterprise’s long-term data goals.
“The team didn’t exist as it does now when I joined,” he says. “The first thing was to create a team that would be a one-stop shop for all data insight and analytics requirements. That was hard because we had some leadership roles to fill.”
With functionality honed, Panayi turned to programs and companies. One of the issues he seen was that there have been components of the expertise panorama that weren’t serving John Lewis successfully. Panayi says he’s “very interested” within the instruments the corporate makes use of.
“As soon as I joined, I had a very healthy feedback culture where I got my team to tell me exactly what was wrong with every platform. I used that feedback as my to-do list and prioritised their requirements. There was some software we were using that we weren’t getting the most out of, so I killed some platforms and software and brought in some other technology,” he says.
“I like our stack now. I didn’t like it a year and a half ago, but what we have now has enabled our data scientists to do amazing things and to manage and govern our data much more effectively. One of the key things we did was drop one database technology and bring Snowflake in.”
Snowflake now sits on the coronary heart of a tightly managed data ecosystem. As properly as Google Cloud Platform, the retailer runs Tableau enterprise intelligence on prime of Snowflake. Panayi’s staff additionally makes use of specialist data instruments, akin to dbt and Collibra, whereas most coding takes place utilizing Python. His staff can be starting to discover machine studying instruments.
“These changes made a big difference for my team. They all play nicely together. And there’s loads of features in the Snowflake tool that we’re not using yet, but that I’d like to in the future.”
Focusing on the correct priorities
Panayi says his staff’s precedence tasks for the subsequent 12 months cowl two key areas. The first contains what he refers to as “the more traditional customer marketing-type projects”, akin to segmenting buyers and concentrating on them with personalised gives and working with suppliers to provide them insights to get the correct merchandise in the correct ranges.
The second precedence space covers behind-the-scenes operational functions. Panayi is worked up by developments right here and believes tactical use of data might have a huge impact. He says examples embrace desirous about the place the partnership’s vans must be driving, how many individuals should be working at sure occasions, and how a lot Waitrose ought to cut back the value of strawberries on the finish of the day to verify waste is lowered.
“All of those big optimisation problems are really operational issues. That’s a core challenge. And we couldn’t do any of that if the data wasn’t in the right place. The temptation a year and a half ago was to go after a lot of those. And although we did do a few, it was pretty manual and pretty painful,” he says.
“We’ve had to be strict on getting everything set up correctly, but now we’re in a position where we can start doing that really cool stuff and we’re seeing it work.”
