#187 Devising a Winning Data Strategy with Andy Sutton, General Manager Data Driven Transformation at Endeavour Group

We are joined by Andy Sutton, General Manager Data Driven Transformation from Endeavour Drinks Group. Endeavour Group is the master brand that holds several well-loved Australian brands like Dan Murphy’s and BWS.

One of the benefits of the retail industry is the massive amount of data that is generated, not just from customers but from the operations side as well.

Andy stepped down from his previous role in Endeavour Group to dive straight into devising the organisation's data strategy and now serves as the bridge between the business and the analytics team. One of the key aspects of his current role is to identify priority use cases to work and focus on. When they started, 6 months ago, they had 275 use cases.

A vital component of their effort’s success was going back to the ‘why’. “Why do we need a data transformation team to exist?  How is it going to operate? What is it going to do?” All these questions were the starting point of their journey and helped them identify the path they would follow. Instead of focusing on just building fancy models, they describe their mission as delivering real value through data.

A lot of their initial work was prioritising and identifying the biggest opportunity areas that would bring value to the customers and organisation. Out of their initial list of 275 use cases, they narrowed it down to four, three of which are being worked on currently, and the other one will be tackled in 2023. 

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RESOURCES

Andy Sutton: https://www.linkedin.com/in/andysutton/

EndeavourX: https://www.linkedin.com/company/endeavourgroup-endeavourx/

Wow Careers: https://www.wowcareers.com.au/endeavour/

“There always be resistance to change along the way, whether that's people being deliberately resisting change, or just passively resisting change, because they don't know what that change means for them. Just keep going.”

- Andy Sutton, General Manager Data Driven Transformation at Endeavour Group

WHAT WE DISCUSSED

00:00 Introduction

03:48 How's the process of, if you were in the business, while the planning for this setup was there, and then starting kind of like this journey of the last six months?

07:40  Some tips that you have for people that are wanting to create that desire for change. To bring something new, trying to sell that the why across the organisation. Trying to get alignment.

12:42 How do you know when you've activated somebody's business person with their data literacy?

20:39 How did you go from that point on (prioritisation and distillation of the use cases), and getting yet to that, to the top list?

24:28 Keeping track of the potential of an analysis or a piece of work that the potential could have across the organisation and ways to reuse against the work. How do you work at that dimension?

27:23 When building the products and the use cases, what type of business impact are you focused on? What were some of the things that were more interesting?

30:31 There is the product manager for each use case. What's the mix of skills or the background of those people?

37:48 How do the data products and the use cases get operationalised into the business? How does that happen in your case?

40:00 Who would be responsible for the change management for the rollout? Operationalising? How do you see that working?

46:05 From a data science and analytics perspective, what do the next couple of years look like?

EPISODE HIGHLIGHTS

I think the reason we made it easier for ourselves is we went right back to basics. So number one, I stepped out of my own role. So I got to focus fully on the strategy, which is not really a luxury you normally get. But I also went back to the right back to the why.

May be because Big Data has become so it kind of all consuming, right? Everyone wants to do big data and AI and machine learning. And some of those softer things, focus things like research and asking customers questions, and actually getting why people feel the way they do. It's kind of gone a little bit off the boil.

I think one of the things we're doing is we're creating a role that’s going to focus 100% on data literacy, so bring somebody into the organization who will work with our L&D  team to kind of go, Okay, what is the organization need for that literacy perspective? How do we deliver that? How do we measure it? How do we show we're making progress, which is just a step forward to acknowledge that we've got a gap.

The key thing really was to start. Which sounds almost, naive. But actually, we just started. We just started doing personalization, we asked for some money, and we started delivering benefits, we started showing the progress we've made and the value is delivered and told the story, the narrative.

There always be resistance to change along the way, whether that's people being deliberately resisting change, or just passively resisting change, because they don't know what that change means for them. Just keep going.

You've got the business on one side of the bridge and you've got analytics on the other side of the bridge and the reality is, what they should do is both walk to the middle of the bridge to agree on a way forward, but what tends to happen is analytics walk to one side of the bridge. Or they stand on each side of the bridge and kind of face-off each other but nobody actually crosses.

There aren't many organizations that have what we've called data product managers now. So, you have businesses that have like, you know, data translators or analyst analytics translators, you have businesses that have product managers. Trying to find the right blend - we've done it pretty quickly. It's a fairly new I think role that we're creating or we're creating in our business at least. So, it's a blend I think of product management experience.

You've wanted to be a high performance and analytics teams, you've always needed to communicate, collaborate, be commercial, and make priority calls, right? Because you've got constrained resources, and lots to do all the time. So, I think that's probably something that goes under appreciate is a lot of analytics teams have always been working in this way. Just not necessarily, with the language and the spotlight around it.

It's how you get that balance right between, you want to deliver short term wins, because you want to show the value in terms of what machine learning advanced analytics does. But also having one end on the end game, which is, you've got to build out an ecosystem, you've got to build a team, you've got to build the foundations.

You get into world where you build something and you kind of hand it over the at the end, and either it becomes Frankenstein, because you're just trying to jam it into a process that doesn't really fit into, or what actually happens is you go back to the old way of doing it because you can't see the merit in it.

 We are a complex, big business. In some respects, we're also small enough to have the conversations with the people that matter to stay aligned. And I think that's the good, that's the really good thing about working for working for Endeavor is that it's, it's big enough to have pretty deep pockets that, you know, we, we've got the investment and support to deliver on these things.