Jonas Christensen 2:37
Genevieve Elliot, welcome to Leaders of Analytics. It is so good to have you on the show today. And I have been waiting for this opportunity for a while because I've been following Bunnings for a few years, both as a customer but also as a analytics strategy because I know you guys have been brewing on some really cool projects. So we're going to hear all about that today. But before we get into that, let's learn a bit about you. So could you tell us a bit about yourself, your career background and what you do?
Genevieve Elliott 3:08
Sure. Firstly, thanks for having me. Also, thanks for being a customer. That's pretty exciting, because you're actually then know, I guess, the product that Bunnings offers. So a bit about myself. I started my career after doing a double degree at uni. I couldn't actually ever work out what I wanted to be. But I started as an auditor at KPMG and I became a chartered accountant. It guess whilst I didn't appreciate it at the time, the few years that I spent as an auditor have been exceptionally valuable to me for my career post auditing, I think. As an auditor, you need to understand business process, workflows, controls. This is what you assess. And actually understanding how the connective tissue of an organisation comes together has really stood me in good stead. I spent some time in audit. I move to advisory and specialised in manage funds in superannuation. This took me to Sydney and then to London. And you know, I guess in roles I started working in project management when I was in London. Actually then jumped from being a consultant into financial services at the bank and sort of took on strategy and planning roles. Created a function I called ''Sales Infrastructure''. So that was sort of supporting everything a sales person needed to do their job really well, but they didn't really want to do themselves. So CRMs, training, etc. After about 10 years in London, we moved back to Melbourne spent some more time in financial services and then I moved to the property sector. And that was probably the one role I've ever actually planned. I decided I wanted to work in a listed organisation that was headquartered in Melbourne. And within that business was given the opportunity to work in data and analytics.
Jonas Christensen 4:47
Yeah, great.
Genevieve Elliott 4:49
In terms of what I do now, I guess the way I'd capture it would be: I'm responsible for delivering, finding data strategy. So essentially how we unlock value from the application of data and analytics across the business.
Jonas Christensen 5:03
So that is a very, very career journey indeed. And you said that your one planed role was actually the one in Melbourne, in property and in analytics. How did that come about? How did you plan to get to that and why?
Genevieve Elliott 5:18
I think if I reflect on my career, for many years, I was actually quite concerned that I didn't have depth in any field or industry, because I've sort of moved in different roles and different sectors. And the moving around was primarily driven by opportunity. If someone offers me a role, that sounds interesting, I'll probably take it. And I didn't know if that was really actually standing me in good stead. So when I sat down and said ''Okay, well, what are the things I'm interested in?'' Partly it was that I have young family and working sort of New York, London, and Luxembourg was pretty challenging, from Australia. So I wanted something that was headquartered in Melbourne. So I took a role in a property company. it was actually a financial planning or planning analysis role. And sort of within about 12 months of taking that role, I was offered the GM of marketing role. And then the business went through a merger and at the time, the CEO was convinced that data was going to be a thing, and was sort of really keen for me to take on this role of data and analytics, which essentially it was a greenfield opportunity. There was there was no capability in the business or very limited. And it was an opportunity to sort of build something from scratch within, you know, a very sort of mature business. Property has been around a long time and operates very thoughtfully, strategically but based on previous methodologies, I'd say,
Jonas Christensen 6:38
Yeah, so that is a very, very experienced, like we've already talked about, but when you put more detail into it, you can really hear that you've actually gone from being a chartered accountant to being an investment manager or funds manager to also being a marketing leader, which is sort of almost the other side of the spectrum. You feel like you're using different parts of your brain for those different things at least. And then analytics. How do you think this experience has helped you in your current role?
Genevieve Elliott 7:11
So I think maybe the focus - I guess where I concluded in terms of informatics is that I'm happy to have created something within sort of a mature business, which does actually require similar change management disciplines as you try and work through to convince people that you've got something valuable to offer. But I think if you take all that experience, what you see in different organisations and in different roles is actually similar patterns of behaviours. You know, they might do different things but revenue generators typically behave in a certain way. You know, operational teams usually behave in certain ways and you get to understand the different drivers or levers that they're trying to pull. You understand the results that they're trying to generate. So it's actually quite useful to actually have spent time in different organisations, so that you actually recognise those patterns. So when you walk in, even if you don't know the industry, you actually see the patterns fairly quickly. And I think just generally, data and analytics is probably a sweet spot for me, because it pulls sort of the analysis that I loved in my finance roles with the science of the marketing piece, and actually brings it together to actually then drive business outcomes, I think. So it's been a long route to get here but I think that all of that experience does actually add value to my current role.
Jonas Christensen 8:26
I'm happy for you that you finally found your true passion and the right place. The best thing to work on in life is data and analytics, of course.
Genevieve Elliott 8:35
Absolutely. Yep.
Jonas Christensen 8:37
Now, Gen, let's get into how you use data to drive Bunnings the business. So you've been with Bunnings, if my memory serves me right, since August 2020. And you look after the data and analytics function there, which encompasses data engineering, BI and also advanced analytics. And that's not a long time, because Bunnings is actually much older than that. I counted about 130 years old or close to that. It's been some very different businesses throughout those 130 years. And before we get into the specifics of data analytics, could you tell us a little bit about what Bunnings is, who Bunnings is and where you are today, also for our international listeners who might not have the same exposure as Australians to the brand?
Genevieve Elliott 9:25
Yeah, no problem. So you're right. It does have a long and storied history. Bunnings started in 1886, when Arthur and Robert Bunning arrived in WA and set up a sawmill. And over time, they took the business public and it became a leading supplier of hardwood but in 1994, it was acquired by Wesfarmers and today I'd characterise Bunnings as Australian New Zealand's leading retailer of Home Improvement and Lifestyle products, as well as being a major supplier to project builders and commercial tradespeople, the housing industry. And it looks like a Lowe's or Home Depot. For North American listeners, or like a B&Q or Homebase for UK listeners, if there's any. So, we have about 375 locations, stores. We've got large warehouses, small format stores, trade centres, some frame and truss sites. We've acquired businesses in that sort of trade tools perspective, perspective Adelaide tools, Beaumont Tiles acquired. So, it's a business that started off as a sawmill and is sort of really grown to service that customer need for Home Improvement Lifestyle and we've got about 55,000 team members.
Jonas Christensen 10:44
So it's a very big business. And for those of you who have been inside a Bunnings, it's not a place where you let your kids go, because you might not see them for a day or two. They're very big operations with everything you could ever want to buy for home improvement.
Genevieve Elliott 10:57
Yeah.
Jonas Christensen 10:58
So, Gen, Bunnings has a well known strategy based on the three pillars of low price, widest range and best customer experience. As I read your strategy online increasingly these three pillars are going to be optimised using data and analytics. Could you tell us about how you see that happening in practice?
Genevieve Elliott 11:20
Yeah, sure. So I think if I start with best experience, one of the biggest changes that we've seen in the last three to four years has been our acceleration of our digital capability. So it's feels very recent. Introduced an Ecommerce capability through our website. Launch PowerPass app for our trade customers. Launched a ''Product Find'' app to help people navigate our stores, because we know that 70 - 80% of the questions our team members get is ''Where can I find?''. And we've actually launched a partnership with Flybuys. A membership or loyalty programme and it lasted a while too. We've had a real acceleration of our digital channels. I think that's important to acknowledge, because that then obviously means that they're much more data rich channels than the sort of traditional visit to a physical Bunnings Warehouse. So until recently, the best experience pillar was best service. But that best experience definition now, it really reflects that growing number of channels that we offer and our desire to provide that sort of consistent, great experience to customers across all the channels that we offer. So in terms of customer experience, we think about customers in two main segments. We've got our retail consumer customers and then our commercial or trade customers. And from a data perspective, the opportunity here is really to demonstrate that we know and understand our customers and to do that we actually need to join up all of the different experiences that they have with us to form that picture of the whole customer. Yes, so our focus has been in the last 12 to 18 months to really understand those touch points and to then map those into what we've imaginatively called a single view of customer, where we resolve the identity of the customer, regardless of the touch points or the experiences they're having with us. We can know them, if you like. So that we're making sure that we understand the preferences to purchase etc. I guess the other thing on our consumers is that typically they're project focused in their mission, their shopping mission with us. And what I mean by that is customers that go to a grocery store, typically will buy the same brand of milk. They'll buy the same brand of beans. They'll be regular shoppers. We will see them at a certain level of frequency. We don't really see those patterns occurring in consumers at Bunnings. If you're a Bunnings shopper yourself, you often coming for a project. So it'd be ''I'm building a deck'', ''I'm painting my kid's room'', ''I'm creating a veggie garden''. So that creates another nice challenge within the data space of ''What are the signals that we need to look for that then help us indicate what the project is that a customer is trying to accomplish?''. From a commercial perspective, we do see more regular purchasing patterns. And it's actually just about, you know, enhancing the experience throughout our past programme, making sure that we're providing the products they need and in the way that they want it delivered. If I move to lowest price for second pillar and that is a really critical pillar for us, so we've got sort of comprehensive business processes and procedures that allow us to do daily checking on prices and verify that we are providing the best value to customers. But I think one of the key use cases that we've got playing in this space at the moment is with our commercial quotes process. So, commercial customers can come to us and ask for a quote, for a job or project and you know, when we provide a quote back to them and typically that's been priced by the specialists in our stores based on the knowledge of the customer and their knowledge of our product range. But what that does mean is we've got years of that sort of data and it's a really nice and broad data set. So we've started to use that to create a predictive pricing mechanism or recommended price for all of the items that a customer is looking to buy. And the trade specialists can choose to use that price or not. But what we're finding is that if the first filter is the lowest price, the next filter is ''What is the price that we need to offer that will drive stronger conversion?''. And we're seeing really great take up of this across the business. And I think it's a really nice example of how we're supporting decisioning within business functions, that had embedded sort of analytics, which is really the end goal of any analytics team. I guess, widest range, we also play a role from an analytics perspective. It's really important to have the right amount of stock in store. Not too much, not too little. So that you can really optimise the sales that are driven from that. And we do sort of an optimised Min/Max forecasting. We have a forecasting product that we provide to the replenishment team. And, you know, it's about 90% of items or skews in store that are now going through that process, where we input what the sales have been in previous periods, adjusting for COVID, adjusting for weather, adjusting for stock availability, etc. So it's just a nice way of, I guess, another nice example of analytics at work if you like, within a business.
Jonas Christensen 16:26
Yeah, great. There are a lot of use cases in that. And here's some of the things that I picked out was the difference between what I would probably technically look at as basket analysis in a supermarket versus a Home Improvement shop like yours, store. The whole project based thing. So instead of going ''If they buy milk, they also buy bread'', it's actually very different, except for when they are tradesmen. It could be more like the basket analysis of the supermarket, the equivalent. So that's really interesting. The other thing that I picked up on the agenda was the way you designed the enablement of frontline staff to price quotes. And because this is one of these traditional challenges that analytics teams have that they can either completely trump and automate a person's role by saying ''This is now the price'' or you can aid. And there's a lot of process design in that and this is where analytics teams often need to really grow into the space of actually understanding that they're not just algorithm developers. They're also process designers and UX designers really. You are designing a process for that frontline staff member. Could you talk us through how you sort of engage with the business to get that right? Because it's really all about the uptake with the individual frontline staff in the stores. That's the beyond that, though.
Genevieve Elliott 17:57
We do it in a few different ways. But typically, what we'll look for is a business sponsor. And this isn't easy. Sometimes finding the right sponsor, finding someone who really understands what is possible can take a couple of guys. I guess just the other point to call out is referred to process design and still keeping decisioning largely in the hands of people who are customer-facing. You know, I think one of the things that always amazes me about Bunnings is its culture and it's got a really strong culture of empowered leadership. And so that opportunity to put decisioning in the hands of people, you know, our team members, is actually really key. So it's not just about being sensitive to a process. It's also being sensitive to culturally how does a business want to work and making sure that the data analytics products that we're delivering are sensitive and take into consideration those things. So, it's finding the right business sponsorship and really partnering. I was actually talking to one of our key business sponsors yesterday and the way he described one of the pieces of work that we've delivered recently together was beautiful in its simplicity. It didn't go into ''We've created a time series forecasting model that does X Y, Z''. It was ''We've created this thing that allows our frontline team members to make better decisions''. So actually allowing our stakeholders to advocate for the work has also been very critical. Maybe just the last thing to say, it's often the analytics is actually pretty easy. And I'm sure my team won't thank me for for saying that. The challenge really is to drive engagement, to drive endorsement, advocacy. And that just takes a lot of communication and a lot of time with different team members. Having them understand what we do, but also making sure we understand what they do and what the drivers of their success look like.
Jonas Christensen 19:51
It is so hard to get that right, Gen, and the analytics is the easy part for us as analytics professionals in a relative sense. We really need to reflect on our own ability or sometimes inability to just get out of our chair and go and do that hard work of actually convincing people. It's quite cumbersome and quite tiring, especially if you like to do numbers stuff, typically. So, yeah.
Genevieve Elliott 20:15
It is a real skill and I think it's actually why - you referred to it earlier - sort of the three different parts of my team. I've actually expanded the team to include data governance, which is actually essential and critical. But we've introduced a function or part of the team called Data Enablement. Their role is really to take our products and to advise our analytics teams on what is the best way to try and deliver this. What change management do we need to actually consider for the team that we are in landed in? Do we have user guides? Have we had training? And then what does that ongoing support look like once it's in production and operating? And it's been really hard to actually describe that data enablement function in terms of - because they do so many different things. But it's actually been a really critical component for us to drive the business engagement that we need. And, ultimately, at the end of the day, if no one's actually using our data products, we're actually not generating any value.
Jonas Christensen 21:10
Yeah, exactly, right. So Gen, this is a really interesting point, because this is probably the secret sauce, the glue, the sand between the rocks that a lot of analytics teams and businesses are missing. Can you tell us a bit about this data enablement team? What kinds of skillsets do you have in it and what is their remit, their sort of function day to day?
Genevieve Elliott 21:30
So, they've served a couple of different purposes. So, it's really small team. It's two people at the moment, but they leverage our change management capability in the business as well. But their remit is both internal into the team and external out of the team. So internal into the team: How do we best organise ourselves to deliver a product efficiently? So, you talked about having a data engineering team, a business intelligence team and analytics team. You've also said people who like numbers often don't communicate, in a way between each other that enables some of this work to be done efficiently. So really, we've used that opportunity to understand what the pipeline for work looks like and how do we feed it through our system within our team to then produce a product? And then it's really been around: Externally, what are the products that we're going to be delivering? And working with the teams that we're going to deliver it to. So not just the stakeholder if you like, but the teams that will be using this products in action, if you like. And really supporting that team and supporting our team to make sure that our communications are spot on. You know, why are we doing this? How will it help? What's the value? What do you need to do? What will change for you? So fairly, hopefully, basic in its desire. But often, when you've got specialists creating predictive models, they're not thinking about what it's going to look like in the hands of people who don't think like them. So it's really that - you've described perfectly - it's that glue. It's that it's the connector that we need between our team and the teams who are going to use our products.
Jonas Christensen 23:08
Yeah, it is so essential yet so overlooked, unfortunately. Not overlooked. I shouldn't say that actually. I think it's one of the harder challenges that haven't been tackled yet. Let me put it like that. Because it is not technical and it's not 2 + 2 = 4. It's not a simple equation. It's a lot of review, negotiation, using different parts of the brain, currently.
Genevieve Elliott 23:34
But it's interesting, if you ask the team, who the most valuable person on the team is, if you like, typically they'll say the Data Enablement resources, because they're helping turn - I characterise it as a dream. You know, I've got this dream, i'm going to make this product. It's going to help all these decisions and it's sort of turning it into reality in a way that's quite comfortable for all parties.
Jonas Christensen 23:56
Yeah, I'll tell you that I'm working on setting up something similar in my organisation and it is really hard to define exactly what it is, what the remit is, how it's going to link in with the business and how it's going to link it with the team. So, I really commend you actually for getting that far. I think it's very hard. It's sort of pseudo-experimental until you've set it up, really.
Genevieve Elliott 24:20
Yeah.
Jonas Christensen 24:21
Figuring that out.
Genevieve Elliott 24:23
Yeah,=. I guess the other thing that they're really focused on and they've done it organically: If we talk about data literacy or fluency, I want people to be fluent in the language of data and analytics. So if we talk about their fluency, it's been organic through the different teams that we've engaged with but a more formal fluency programme is also within their remit. So based on a team members role, what level of education or support or fluency do they need to have in data and how are we going to deliver that and that's a key element of their next 12 months.
Jonas Christensen 24:56
Yeah, really interesting. So you've got this group forging the path, for the technical work to then follow, basically. And there, there's a lot for us to learn in that. All of us listeners and not least me, Gen. So thank you for sharing that.
Genevieve Elliott 25:12
No trouble.
Jonas Christensen 25:13
I'm interested in how you select and prioritise the right projects to work on in the organisation, because I'm sure you're in hot demand for lots of stuff.
Genevieve Elliott 25:25
Yes, You notice the pipeline of requests coming in often exceed the resources at hand to deliver. We start with the top down approach. So what is the business trying to achieve across our strategic pillars and what role can data and analytics play? We're part of the corporate planning processes. It happens annually and so we use that as the opportunity to really prioritise our focus. On top of that, we then have analytics business partners that are dedicated to key areas of the business and they help us develop a joint roadmap for those key areas. So if you think retailer, the key areas would be merchandise and marketing and commercial customer. So we actually have analytics business partners that are embedded in those teams and they again connect our specialists into those key business stakeholders and together they'll create that joint roadmap for the year that is based on their strategic objectives. And I guess the other thing that we use is like - sort of, the next filter, if you like, will be value and return on investment. There are lots of problems that we can help solve. Too many most of the time. And, you know, hiring the right people into data analytics means that you've got really curious people that want to solve any challenge that's thrown their way. But I think it's really important that within a business, it's expected to drive commercial outcomes. That we also hold ourselves to account from a value perspective and we're really cognizant of the size of the commercial outcome. If it's not worth the effort, then these precious resources that we hire into these spaces really need to be focused on where they can make the biggest impact. And I guess that finally, from a mechanical perspective, we do have a quarterly planning cycle that, again, sort of adjust priorities for the team. And what we're finding is that, you know, our data platforms and engineering team are in massive demand, not just from our analytics team, but you know, for the different projects that across the business, because as data becomes more and more important, that connectivity between different systems means that we'll have a role to play in a lots of the tech projects or transformation projects were involved in. So, understand business strategy, work collaboratively with the business to map that out, make sure we're focusing on the things that are going to drive the most value or impact and then just make sure that we're linked in to all of the different strategies that are at play across the enterprise.
Jonas Christensen 27:47
So tell us about this corporate quarterly planning for the organisation. Is that you and all other functions in the business doing that together? Or how does it play out?
Genevieve Elliott 27:58
It's reasonably new. We've done a few quarters of it. Now, it's really started off as a combination of the transformation projects that we've been working on and the technology teams. And making sure that we were aligned around the key projects, but also thinking about the work that has to be done over the next quarter, that needs to be done by multiple, multiple teams. And so, we've got tier one, two and three pieces of work. Tier one of the really strategic priorities. Tier two is when more than one team is required to deliver a solution. And tier three are those that can actually be done within team. So we find we'll have a range of different projects that we're working on and we're asking the team to work on at any one time. But it is a really nice way of understanding what's important to the business and making sure we're putting resources on those right things.
Jonas Christensen 28:50
Yeah, and that really is a gift to you, I think. As an outsider, that's easy for me to say, but some of these planning, iterations can seem cumbersome and kind of ''Oh, let's just get to the work''. But they're actually a gift because they give you the opportunity to focus and to say no to all the stuff that's not on that list, which is really the challenge for a lot of analytics leaders. A lot of our day is about saying no to lots of good ideas that just aren't the very best. And that's actually quite hard, both timewise, emotionally and we don't like to let people down.
Genevieve Elliott 29:23
No, and you want to solve the problem. And you often can see the answer as well. It is really difficult to hold yourself back. But I guess experience would say, where you have embarked on something and you haven't had the right resources or enough of the right resources or you haven't spent enough time looking at the quality of the data that you need to use, typically the timelines really blow out. There's a whole lot of complexity that you haven't taken into consideration. You often come up with a prototype type thing or your minimum viable product that doesn't scale easily. So, then you've got a whole lot of other work that you have to do but you've done enough for it to be used by parts of the business, but then it requires a whole lot of manual support as well. So I think I've definitely learned the hard way of what happens when you embark on something because you think it's going to be easy and then it turns out to be much more complicated than you thought. And then you kind of live with it trailing along and never quite delivering on what you'd hoped for it to do. So I agree, I'm really grateful for that planning cycle that we have, because it does allow us to be really focused in what we do.
Jonas Christensen 30:29
Yeah. So to anyone listening out there, if you don't have a planning cycle, make note and have a think about how you could create one for your organisation. Either with everyone or even within your own team, just to help you do that stuff. It's so critical to stay on path.
Genevieve Elliott 30:45
Yeah. Maybe just the other thing to point out there is: Before going into that we actually spend a lot of time thinking about - we think with our tech team and with our strategy and architecture team, defining what our Enterprise Architecture strategy looks like. And where does data play a role in that? So in terms of our overall tech design, where does the data platform sit and what is its role? And so doing that work upfront has been really useful also because it also then prescribes when we do need to get involved in a project and when we don't, and actually having that sort of embedded in as far Enterprise Architecture principles, then means you get included at the right point for the right things.
Jonas Christensen 31:27
Yeah. So it's about really being visible in that process and having a strong link. So what I'm hearing is that in your organisation, you are a strategically important pillar, if I may say that. And that is really important that if you're not in that situation, you have to be able to create that. So one of the things that come to mind there, Gen, is you have projects, requests, or initiatives that you are called upon to do work on or deliver.
Genevieve Elliott 31:59
Yes, that's right.
Jonas Christensen 32:00
But you will also have your own internal list of things you think you should be doing with that same time that are competing, because otherwise we won't get to X,Y,Z amazing capability in two or three years. How do you prioritise within that realm? How do you sort of help plan to seed of some of the things that you need to do to advance your capability in the organisation?
Genevieve Elliott 32:23
That's a really good question. And there's a lot of tension in those dynamics, if you like. It actually comes down to how you describe the value of the work. So if you can say ''By doing this work, I think we can save X'' or ''By doing this work, I believe, we can drive, let's say, incremental revenue by Y'', then that actually then drives the prioritisation. So it comes back to how are you thinking about the value of the work and the sort of matrix, if you like , value versus effort. You know, how quickly do we think we can make this happen? Really starts to then drive that prioritisation.
Jonas Christensen 33:02
Yeah, and this is this is also again, critical for everyone to make sure that you are selling your own wares. I've managed teams that were so good at delivering on requests, but never took the time to actually care for themselves and build data infrastructure and so on. You're robbing from yourself in the future or stealing from yourself in the future, If you don't take that time to invest in your own capability.
Genevieve Elliott 33:25
Yeah and it becomes unsustainable. Yeah. and high risk too, because it's really hard to manage what will typically be a house built on, you say, house built on sand, I feel like.
Jonas Christensen 33:37
Yeah, good analogy. So, Gen, what advice would you give to other analytics leaders wanting to drive strategically important results for their organisation? And perhaps, for ones that aren't quite there yet, wanting to sort of position their team in the organisation?
Genevieve Elliott 33:55
Yes, another really good question. And if we think about traditional material businesses versus digital natives. Digital natives don't have this problem. Typically, they understand the role that data plays. In fact, data is probably central to their proposition. But if you're working in a business that has been successful and hasn't needed a significant investment in data analytics, it becomes much more difficult. I think, for me, being able to demonstrate value initially is fairly critical. So you need to, in my experience, you actually need to find pockets of advocates. So people who are really keen to work with the team, keen to see and understand the value and give you a go. And that's a real opportunity to again collaboratively to create something together. I think, once you've done that first thing, you know, it starts to give you a seat at the table. And one of the critical things here is making sure that any measurement that you do, like, it's really important to measure what you're doing, so that disciplined empirical return you've measured that drives real confidence that the result is real is also really critical and you need to think about that, before you embark on whatever predictive model you're going to build or whatever you're going to deliver. So thinking about how am I going to describe my result, with real confidence actually is important. I think once you've got a couple of those wins, like working from the top down is also really important. So really, if you don't already have and I do think we're very lucky at Bunnings, we have massive support from our senior leadership team, executive leadership team. If you don't already have that, like that's your opportunity to go and talk about what is possible. And you can't underestimate how many times you have to go and have those conversations. But it's actually being comfortable with those conversations and being able to have them in a way that taps into what's important to those senior stakeholders. So again, not talking about the level of error or your accuracy of your models. Those sorts of things from the outcome,like ''I think we've got an opportunity to create some efficiency here by doing X,Y,Z'' because it's really tapping into what the motivators of those the same stakeholders. But it is difficult to create the space for your team and for me, it's one of the critical components of my job. My job is actually to go out and create the excitement, create the conversations that talk about these opportunities for the role that analytics can play in our business.
Jonas Christensen 36:17
Yeah, and you're pulling on a point there that I had almost ignored as if it were taken as a given. But it shouldn't be, which is you have very much support from the top of the organisation, right? So the reason I can sit and say I know what Bunnings strategy is at a high level when it comes to data and analytics is because it's made public investor presentations by the CEO, so that that means that there is total focus on it and that's pretty critical. That should take it to play sort of thing.
Genevieve Elliott 36:48
Yeah, at least one other reflection and this is probably where maybe some of my finance experience comes into play is that often if you can talk about what data means in terms of traditional financial measures, you get better cut through than just talking about measuring customer satisfaction in a certain way. So if you can say ''When I look at sales, I can see that'' or ''When I look at customer satisfaction, I can see a direct correlation between that and sales or between an age of a store''. Some of those key metrics that really have been the focuse of a mature business. It also then starts to open up conversations as well. So, anchoring in on what this means to those traditional metrics is a good way to have the right engagement.
Jonas Christensen 37:35
Yeah, so have you seen examples there, perhaps where you've seen it go in one direction and I suppose when I say one direction, in a direction where that was lacking and then as soon as you added that, you could move on to bigger and better things or you get caught better cut through?
Genevieve Elliott 37:52
Yeah. Certainly not so much in my current role, but definitely in previous roles. Linking customer metrics to financial metrics and sort of investment returns was a really critical enabler for some expanding the remit of the data analytics team.
Jonas Christensen 38:09
I couldn't agree more.
Hi, there, dear listener. I just want to quickly let you know that I have recently published a book with six other authors, called ''Demystifying AI For The Enterprise: A Playbook For Digital Transformation. If you'd like to learn more about the book, then head over to www. leadersofanalytics.com/ai. Now back to the show.
Now, Gen, a big question for you, where you have total creativity to answer. So, you look after pretty much the whole data pipeline at Bunnings. If you were to design the perfect data-driven organisation, what would it look like and why?
Genevieve Elliott 38:54
So really interesting question. I'm probably a little bit stumped. But I actually think that you need the right balance. You know, a totally quant-driven business isn't going to take into consideration human behaviours and you we know that we don't all behave logically, right. So it's got to be a right balance of quants, I think, supported by qualitative. If we think about ourselves, right, we're actually sitting on a huge amount of data. You're sitting on 50 years of data and lived experience myself. It's just that it's me who's experiencing it. So, I think it's actually that sort of what is happening combined with maybe human interaction through experience. Why this might mean that and then what are we going to do as a result? I think there needs to be that right balance. The other thing I reflected on is: I would really, really like all data at source to be perfect.
Jonas Christensen 39:49
Yeah.
Genevieve Elliott 39:52
Which I know is a pipe dream, but in particularly again, businesses have been run for a long time. When a lot of the processes were being established and systems being set up, they weren't designed to support complex analytical models. And so you end up compensating a lot further down the pipe than you want to. Because I think the an organisation that's invested significantly in data managementand solid processes around measuring the data quality, it's ultimately that really drives the value of what you deliver, at the other end.
Jonas Christensen 40:24
I think for data scientists or analysts that are further up the pipe, - making a broad brush generalised statement here, so people are totally allowed to disagree- but it's generally undervalued and underestimated in terms of the importance and rigour that should go into that. In my opinion, data scientists and analysts need to lean into the creation of that data a lot more. For the sake of their own good, their own productivity.
Genevieve Elliott 40:50
Yeah, it should make their life much easier. You know, they should be able to create products much more simply without the sort of same level of constraints that they have to build into the product. I spend a lot of time watching soccer, because I've got two boys and they play a lot of soccer. I think analytics is a bit like soccer sometimes. So everyone focuses on the the striker, the one that's going to score. The one that sort of makes the dreams come true for getting that goal in the net. But what it often fails to realise is that you've got a goalkeeper in a really solid defence line - because my boys played defence - that have had to keep that ball out. That have had to keep discipline, patent ways of working to enable that ball to get up to the striker. And so you can't get those two things out of whack. You actually have to be focusing on your data management to the same extent that your own data products, because otherwise your data products' performance and quality will be diminished over time.
Jonas Christensen 41:46
I love that analogy as a soccer strategy and a analytics strategy. There's so much truth in that and you can't lose the game, if your goalie keeps a clean sheet basically. So there's something to take away there. Now, Gen, we're almost at the end. I've got two questions left for you. And the first one is: I'm going to ask you to pay it forward by telling us who do you think should be the next guest on Leaders of Analytics and why?
Genevieve Elliott 42:13
So I've been thinking about this. I don't know her particularly well. In fact, I've only met her a couple of conferences, but I always love what she talks about. Her name's Elizabeth Moore. I know her from her Telstra experience, but she's moved recently to the ABC and looks after audience data and insight and I think she'd have some pretty interesting stories to tell about what she does.
Jonas Christensen 42:32
Great recommendation. And Liz, don't hold your breath. I'm going to be chasing you very shortly. And lastly, Gen, where can people find out more about you and connect with you, get a hold of any content you may produce?
Genevieve Elliott 42:49
My profile on LinkedIn. I'm ashamed to admit I don't produce a lot of content. Probably should do some more. But yeah, if anyone wants to reach out, you'll find me on LinkedIn.
Jonas Christensen 42:59
Genevieve Elliot, thank you so much for being on Leaders of Analytics today. It's been really, really interesting to learn more about Bunnings and just about how to establish a very mature data and analytics function, when it comes to the processes in the team, but also how you engage with the business. So I thank you for sharing your knowledge with us today and I wish you all the best. And I look forward to being really personalised and targeted when I go to Bunnings next time and I especially look forward to not having to walk aisles and aisles and aisles to find that little one thing that I'm looking for.
Genevieve Elliott 43:34
Product Find App. That's the answer. Yep.
Jonas Christensen 43:37
Yep, I've used it once. It did actually help me find that screw or whatever it was that I was missing.
Genevieve Elliott 43:43
Yep. Thanks for having me. It's been a real pleasure. Thank you.
