So I will do a quick introduction. Hopefully, everyone are are seeing our lovely faces now instead of the PowerPoint slides. But, I'll introduce myself real quick. My name is Garrett Sauls. I'm with Innerworks. I'm a content manager. I've been with Innerworks for almost thirteen years. Just talking with data people, getting them to talk about their stuff, making them talk, prodding them to share their smarts with the world. And so that's that's why we're here today. But, I'll pitch it over to Annabelle, my illustrious, experienced enablement colleague to introduce herself and our guest, Ellie. Hello. A very nice welcome also from my side. My name is Annabelle Rincon. I'm living in Switzerland. I'm passionate about data visualization and, enablement. On my previous role, I was leading the Tableau Center of Enablement for, like, a Swiss bank. So that's why I can say with, like, without a doubt that enablement is a key for the success of any data analytic implementation or transformation. And that's why with Garrett, we have decided six, seven months ago, I don't remember, that it will be a good idea to do this, webinar series and learn from the best on the market. And, yeah. And I'm also leading the, Tableau Analytics user group and Tata Plus Women Zurich chapter. And that's why today is a great honor for me to receive Ellie Slater. Ellie is currently the enablement manager at John Lewis Partnership in London, where she use her skill to enable her organization to adopt data driven practice through effective strategy, training, and community engagement. And she will tell us everything about this today. She's also a two time Tableau ambassador and colleague a data plus woman launder. And she's passionate about fostering inclusive community and sharing her knowledge. Thank you very much, Haley, for joining us today. And maybe a small question to get you started. Can you explain what enablement manager does? What are your daily activities? Yeah. Of course. And hi, everyone. Thank you so much for having me on. I have a and Garrett. It's really fun. I've been looking forward to this, but it's nice to actually be here. So as a network manager, it's an interesting role and one that I've kind of developed over the last few years. So at the partnership, we've been using Tableau for about three years now. And day to day, I've been used to be a Tableau enablement manager, and then this year, I've switched and pivoted to kind of just general data enablement. So as part of that, we set up our brand new team in beginning of Feb. So my day to day job at the moment is working through the strategy of enablement, what we wanna achieve across many different work streams, and how that ladders up to our data and insight strategy, and then how that matters up to our organization strategy as well, and all the various metrics and scorecards that underpin all of that. But we basically have five work streams within my enablement team. We have learning, discovery, tooling, communications, and community. So I head up the learning and community, as well as kind of steering the direction for the others as well. So within learning, our biggest thing is to provide a minimum standard of data fluency across every partner, our employees, at our organization. So that involves looking at our data fluency programs, helping them with technical skills, so developing the proficiency programs for our platforms like Tableau, Snowflake, and various other tech skills, but also those softer skills. And I don't love that term, but the softer skills around storytelling, presenting, just making sure that, everyone in the partnership, at John Lewis, feels capable and confident in using data and insight to drive their decisions. So part of that is organizing what training we're delivering to who. It's developing those materials. And, also, I think I often say that the enablement team or the new team is is kind of the marketing arm of data and insight at the partnership, and we are working through various different playbooks to help our different departments better advertise their products and their insight to the rest of the business, both in terms of getting our stuff actually used and valued, but also in helping other people use data for their own jobs. And we're working with them, to help them with their journeys. That's awesome. I'm curious. You know, having been at John Lewis partnership for a little bit, when you when you started, were some of these structures in place or how many of these structures did you kind of, like as you progress through your roles and as you kinda got acclimated, did you kinda help lead? Like, essentially, how did you go from starting at John Lewis partnership to where you are now where you have, like, these designated subgroups and teams that are focused on different things? So what was that like? Yeah. It's been quite a journey and quite a little bit has been, like, kind of side of desks. So I joined as actually the reporting manager, and we had, at that time, had, like, one team that was a reporting function in Tableau. So I came in as kind of the the more Tableau expert having had some experience in it, and in community building. So I was there to manage two business areas reporting needs, so turning some reports from Looker into Tableau and providing the inherently alongside that, providing the templates and authoring guides and best practices as part of our kind of community. The community did not really exist. I helped put set that up. So we have a Slack community. We're now expanding this beyond Tableau, but at the time, it was to try and identify Tableau champions, basically provide that channel on Slack that people can ask questions for. But, also, we have developed a Google site, which is not being developed even further, but, basically, it had all the answers on it. It has all the answers on it. All the information they need to get a license or even just a login, what user types will mean, what are the glossary of terms, what our best practices and documents are. So through my development of the reports with actual users, I then understood what they needed to help that build that into our best practices. And then through that, just really kind of preferred that training program. I built a training program for financial services, Well, business areas I was working with. I just really enjoyed actually getting hands on with teaching. So through that, I've been my community work. I then kind of convinced my management that there needed to be this Tableau enablement role because it was bigger than what I was doing and the impact was bigger. And then through that, it just grew and grew. We launched a data fluency program a couple years ago and then launched our specific Tableau training a year ago, which is very specific. It's all in house. Pick and mix, color, style. And, yeah, now I get to do that for all of our other platforms and software skills. It's been a journey. Yeah. For sure. For sure. That's awesome. It sounds like I mean and and you make it sound so simple too. And so, like, you know, boom boom boom. But I I can imagine as as anyone who has been in a role like this or does enablement things, whether it's kind of like a side desk or a part time thing, you know, unofficial sort of thing or in an official capacity, just growing that organically, that kind of journey. But I I think it's really cool. I'm I always love when people kinda start at that level too and kind of build that path and help build those structures and partner with other people in the organization. And I also think that it's awesome that you work in an organization that does allow for that freedom, that does allow that expansion because that's not that's not always a given. It can be a battle to to get to get that in place and to make people see the value of that. So Yeah. For sure. I think I've been very lucky in that I I have been able to focus on things I've enjoyed doing, and I do think that's important. So I've been able to run community events where we look at our different, quantified self data, so Netflix data or Spotify data, and really bring that to life for people, and also then be able to develop some more of the the training elements. But even last year, getting back to some of the marketing stuff, we did launches in person. So I've been able to develop skills that I wouldn't have had otherwise just as a normal analyst, learning how to launch a product and design marketing materials and branding, and it has been really good fun. Mhmm. Yeah. On that, I think that you present some of this material at the Data Farm Europe. I had the chance, like, to attend your presentation, and, yeah, I really love it. And it's true that you integrate a lot of the branding, with all this imagery and everything. And everything. And I think it's fantastic. But I think that you mentioned during your training that that's not something that someone on his first year can do. Maybe maybe you do, like, the training and then you refine until adding the braining. I don't know if you can really comment on that. Yeah. Yeah. I can. So I think I'm lucky in that I have an art background. I think before going to uni, I was fifty fifty split between going down art or geography. So I've not always come from a more technical STEM backgrounds, but, do geography. I did physical geography and learned statistics and just loved it. So that's why I went into data. But that design element and the artistic side is also partly what drew me to Tableau in the first place because of the crazy stuff you can do. I know, Annabelle, you've been doing a lot of the art stuff recently with Tableau, which is really awesome. And I think that has helped me, develop that element of branding. So if I can share my screen, I can use the tech to do so. Here we go. This is some of the stuff that we've done. So, hopefully, you can see just that screen. Yeah. Uh-huh. Yeah. So this is our Tableau mission. So we decided, with our data fluency, we wanted to have a sub brand, and we decided on a space team. So we have really bought into this, and I think it's really helpful with our story that we tell because it means that we can link things up along that journey. So we have various headers and various different thresholds that we use and have developed. So we've only developed this kind of, like, informally. So I love doing this via, like, PowerPoint or Google Slides, or I discovered last year Google Drawings, which is pretty cool because you can export it with a transparent background. So none of it is that high-tech. Like, I think we're slowly this year gonna start moving into Adobe and various other kind of paid for products, but we use Google at Domino's. So I just kind of leverage what we have, and the various icons you can get from templates. So that's kind of how we went together with this. So we've got that across our communications. It's across our branding. Our training program talks about space, and it's got a few different, style of elements in there, which is good fun. It's good or bad. It's a mixed, reception for that one. But, in general, it's quite fun. So, along with that, we have the design that I want to do for data fam. So for me, I was really nervous about delivering this talk, and I just want my main biggest thing, is that I want people to listen and pay attention to the whole thing. So I thought, okay. For enablement to properly work, including enablement of a talk, you've gotta kind of have a story that keeps people engaged throughout. So I thought, why don't I turn my talk into a a game walk through to really kind of highlight and exemplify the gamification element that we often pull on as enable workers. So I turned it into a game. So I had it that you could select your different players, which are our different user groups, our user personas. So we have inside consumer, data consumer, and then data analyst. These have developed as well since I've done this, but, it's kind of, like, played through, and you could select on the carousel what one you wanted. I then create the map. So, again, all of this is just using icons and slides, and, like, we have them so that moves along in animations. It's all in slides. And it was really good fun. I think it tied the talk together. We also I went kind of all in on it as well and pretended that my colleague and I, Sophie, we were crew members on the starship. So we were very cheesy with it, which hopefully will go down well when I repeat it in a couple of weeks at tablet conference. But we were we did the whole, like, signing of the exits and and made a few jokes along the way. And we had it so that you could go through the different levels, almost like a game walk through literally, and kind of shows through different, elements of our proficiency and how that all went together. So, yeah, that's kind of how that developed. So It was fun. It was also helpful as it relaxed me into the talk as well, and it was really good fun, actually. Mhmm. Mhmm. That's so I feel like that's so crucial. You know, I feel like in in the data visualization and the visual analytics sphere, we talk a lot about, like, visual best practices. And sometimes we think that applies only to the dashboard. Right? But in reality and, again, as as you know, as someone who's who's doing this marketing stuff, just all the things that surround it and the frameworks that you do, the frameworks that you design, even something as simple as, you know, I know Annabelle and I have talked about this, like a branded landing page where resources live or, again, a PowerPoint to help visualize what that journey looks for. People are inherently visual and if you include that all as just text, it's not gonna resonate as well. And if you certainly commit to the bit and go like full, you know, Oscar performance, I think I mean, I love that. People remember that. Like, at the end of the day, people remember that and can it be cheesy and whatever? Yeah. But, I mean, it should be fun. But so so kudos to you. I was really impressed. My one my one follow-up question with that is how many requests for stickers have you gotten since creating those resources? Yeah. Quite a few. So we've got, we had quite a lot of stickers. So we have Tableau mission stickers, and we got stickers that you could add depending on what journey you went through and what you've completed training wise. And at the conference, I did hand out a load of the Tableau Mission stickers. I haven't actually had any follow ups from the conference, but people at work absolutely love the stickers. We stick them over the laptops and Uh-huh. On various bits and pieces. And it's been people do request them, and people talk about it, which is good fun, and they recognize it. And, again, that's all down to the brand, and they recognize that it's our internal Tableau training, and they can get involved with it, which is good fun. Mhmm. Mhmm. I love that. Session was fantastic, Kelly. If you have any doubt about that, the session was fantastic. I still remember. I do do the data on tablet. I really love that. So we do have a I well, there's a question in the chat that I'll I'll kinda run through real quick. We'll see if we tie it to to what you're talking about here. Someone had asked, could you go deeper into the data fluency component of that? So I suppose when you're looking at, like, those different user personas, what does that mean whenever you're you're we're talking about how do we get different people, especially, like, nontechnical users, fluent in data, adapted, plugged in, speaking their language? How does and they're just kinda asking how you brought that shift into the organization. Yeah. So I think I think that shift is really important, obviously, and it's really hard, I think, and it takes so much longer than you might think. But I think for us, it's about our users' needs and keeping that first and foremost at the front of our minds. So we have many different user types. And I think there I showed three, but actually we've developed this now into, I think, about eight. But we have included this out to our people in the retail stores. So often, we get stuck in head office thinking about what our head office users need because they're the ones that are more technologically skilled in using data and accessing it, and they typically use a laptop. Whereas, actually, the predominant workforce that we have are in store. So we have, in total, I think there's about seventy five thousand of people at John Lewis Partnership, of which I think fifteen thousand are in head office, or on a laptop based role. So it's really interesting actually that the majority of our users are on the shop floor. So this means we've got provisioning just for them. So they need to get data fluent in terms of understanding what the report says and how that metric or performance object actually impacts their day to day life. So it's, again, it's going in with that fluency level of translating the story of of what this number actually means to them and how they can actually impact the data as well in various different ways in the store, like availability of stock. So that's really interesting on that level. But then through that, we've built stories and built user journeys on different user types. So, for example, on that screen I showed before, we had insight consumer, data consumer, and data analyst. So these are mean different things. And they at the time, they tied back to Tableau, but we've now expanded them to our full data stack. So an insight consumer is basically anyone that consumes the insight that we provide to them, and we can get on to self-service in in a second. But, basically, we provide reports, insight, data products to our users, and they are able, through being trained up, to consume that information and then apply it to their role. The next level up is then how they can take data products, like data sources that or even workbooks that we provide to them, consume that, and then actually offer some stuff off the back of it. So that would be example would be Explorer and Tableau, would then be able to go on and edit a workbook to ask that secondary question that the report might not answer. We then have that third layer, which is that data analyst, which I think is now called data author. But, basically, they are able to connect some source tables, connect to new Google Sheets, and create those data products themselves as a data source, which then to be provisioned would go through our governance system. But we then have actually decided to build way more layers onto that. So I think our next layer up is more technical with Snowflake, and looks at where how they can consume and write code themselves. So so far, it's been non coded. We then have all of our kind of developer user personas, which are to do more technical stuff like coding or using, more developed model ML ops, like, machine learning language and that kind of more technical stuff that we wouldn't expect the general data user to have even all the way through to, like, more of the AI developer products because AI is such a hot topic. So that's kind of how we approach, provisioning different things for different users. Okay. That's fascinating. I mean, I love that. It's very well thought out too just to think through holistically what each of those users functions are and to separate it. Because I know there can be a lot of crossover sometimes, especially when that structure isn't defined and you have a lot of people doing a lot of different things. So I think it's awesome that you all kinda have those delineations for those different people and set them up accordingly. Yeah. It's been a massive undertaking as a whole operational model, and we use Miro at the partnership as well, and that's such a cool tool. But you I don't know if you ever used it, but you can visualize big giant, webs of how it all interconnects. So it's been a real mammoth effort from the team to look at the operating model, to look at, like, how license supplies across all of these, to how the governance works, to what access needs to be done, to what processes need to be in place to get the access and the different requests to get the access and and all of those different layers. And then we're now coming in as enablement to actually go, okay. Great. Let's package this up as a toolkit and provide I because we're a John knows our waitress. I equate this to kind of like a shopping basket, and you take all the things that you need that's applicable to your role, and you can always pick up, like, one of those ingredient ready meal quick bags that has, like, all the different things in it. And you can then go take that and do what you need to. That's great. We love a good metaphor. We do have a follow-up question. And I think this kinda pertains to a little bit of what you were talking about, especially when we're talking the difference between people who are building reports and then the more technical Snowflake people who might be working with with more, like, data engineering or whatever it may be. But, Sharmila in the chat was asking, how did you set up data sources for users to access when building their dashboards? She heard you mention Snowflake, and she was wondering, do users connect to Snowflake views slash tables, or do you have published data sources in Tableau? I suppose, how do you set up those data sources for those people for, I guess, which would enable self-service to a degree. Right? Yeah. So we have two kind of methods for doing this with a partnership. So we very much advocate self-service. So we have two ways of doing this. We have stuff that's provisioned to them and stuff that we they can self serve from more technically directly. So it's we've branded this kind of like the Netflix of insight and the TikTok of insight. So we have certified tablet data sources and data products like workbooks that we have gone through the the big certification process, and these are trusted, documented, traceable, secure, defined, really easy to use, and it has all the collateral to use it. Like, it's in the template, and it's, got all of the videos on how to use it. They are there for people to use them if they need to. So this is either picking up that published data source and running with it and using WebEdit in Tableau cloud, and running new workbooks off that or even editing the workbooks themselves if they know how. They can't save over stuff that's all sorted in our live environment, but they can have a pay in their own kind of personal area. On the other side with the stuff that do more traditional self serve, our kind of TikTok stuff, we then, provision them to connect to, create their own targeted sources so they can connect to provisioned ones. I think it's coming this year in a release note that you can finally do relationships with vision data sources, but, that will be so good when that comes in. But, they can create new ones using, either Tableau prep to connect to that and add more data sources in, or they can connect to Google Sheets and build new data sources from that, or even connect through to Snowflake and connect to our consumer layer of Snowflake and pick tables out of that using either just the Tableau interface, ideally not custom SQL, or even that extra layer further and going and writing some code and stuff like in kind of setting up tables that way. So it's a variety of different ways depending on what user can do, what they need to do, and how many like, what the value of that is. So if it's a big case that someone's writing tables or writing code and it's gonna be useful for many people, we would talk to our kind of, teams that have sit on the engineering side and get them to productionize something. Awesome. That's really great. Again, another well thought out thing contingent upon, yeah, your different users and allowing for multiple streams of access. So, I think that's great. That's fascinating. It's not just one way. So Yeah. And I think we've built it quite transparently so that it's an inspirational journey as well. So as a user, you can just go in and do what you need to do your job. But if you wanted to be interested in learning more or wanted to move up and up that kind of scale of skills, you can do that as well. Awesome. I'm curious too. So in addition to the John Lewis stuff, I know that you and Annabelle in particularly have a lot of overlap with data and women, but you also do data fam stuff, which is, again, the topic of of, what what you had presented on as well. I would I would love to hear just, a, how you two met. I think that's, like, very interesting and how you came into interaction then, essentially, kinda what you're doing in each of those communities, whether it's Data Fam like you had just presented, and then, of course, Data Plus Woman, which is another large and fantastic organization. But, yeah, let's start with let's start with the the Meet story, the Meet Cute story. What about Ellie? So I've been running Data Plus Swim in London for about five years now, and that was just before COVID hit. I've been to a few events, loved it, wanted to get more involved in London once, so then signed up. And when COVID hit, we moved to virtual. So I didn't actually run an in person one until a couple of years ago. We did loads of stuff virtually, and then through and through the network of Facebook and different ones, we then decided to collaborate and do a joint virtual Christmas one, in the December. So through the planning of that, it's how I met Annabelle, which was really good fun. And then I think it was that year or or the following year or two, you did a talk at, I think, conference and recorded maybe it was, yeah, it was twenty twenty one maybe when it was all virtual. You got different people to do different bits from day to day programming, and record videos for that, which was fun. So, yeah, few years now. Exactly. On this data with women Christmas special event, we used to have games and even, like, make recipe of chocolate, hot chocolate live. That was very fun. Very darn fun. Computer, but very fun. Yeah. Almost good. And, any yet, I mean, I have my own thinking about that, but, I think that this, data plus woman, even are quite beneficial, you know, for you in your role or how to, enable more people. Yeah. Definitely. So I really love it, and I think that it's personally really helpful for me to understand how people actually use data in different organizations because you can get very stuck into not quite an echo chamber, but you can get limited with just how it's applicable to your company or your particular use of data. And actually bringing people together in a space where they can talk about different things, talk about different approaches, it just highlights the fact that everyone has a unique use of data and a unique journey through how they then go and apply it. And I think for me, that that's really great to highlight and understand, yeah, different people's views so that you can then go and apply that back to what you do and tailor what you need to do and your enablement efforts, which is really awesome. And I think, for me, it's about creating that inclusive space and in different accessible ways as well. And we're just getting people talking about data and sharing because, like, in that case, I think it's the top the one of the tag ones of Tableau, isn't it? The data is only useful if it's being used or shared or or something like that. And I think sharing what you're doing and and and helping others learn and be inspired is part of it and part of it. And then actually, I learn loads. I try to go to as much conferences as I can to learn from other people. So to be able to create a space where people can do that as well is really important. I I like when people say, oh, thank you, Annabelle, for harassing me to become a speaker. Yeah. Crawling and poking. Well, it's good for you. You should try it. Exactly. I will say, yeah. It's good. You will see. You will be fantastic. They always are. So yeah. Yeah. So on the data piece woman, Garrett, the men are welcome. They cannot sleep. That's it. That's a rule. Yes. They are not welcome to join. Yeah. For for everyone TC, we will have a data placement meetup so you can also, come and join, and see what is it. Yeah. And I remember that from from last year of of being a thing because I know that is that is something we've we've worked on together. I ran I think if I remember, I I ran the registration for that, and then, well, I would kinda go and make sure some stuff. But I really I really enjoyed seeing that, and I I loved seeing that platform, for women to connect and and to present, you know, about things that affect them and how to kind of again, it's kinda stuff like this as well of, like, how to how to refine your voice and how to share it out there and how to connect with other people and learn from people in the community to not only just share your ideas, but to learn from others and to get connected because, again, those connections are force multipliers, both in building your own enablement, you know, courses and enablement plans, but as well as you just never know down the line, you know, four or five years from now if you're going to be in another role and what those connections might mean for you if you are searching for another thing or you are looking for a shift or whatever it is. So I I really enjoyed kind of seeing it, play out because that was my first, in person data plus, woman event that that I had seen. But like you said, it's happening in again at TC, and I know it's a very popular event. It's very fun. So anyone who is going, you're in for a treat. And then I think if I if I remember right, people can sign up and well, it's on the it's on the wait list now because, again, it's it's in such such demand. We have two events. We have a data placement pregame, which is an event before the conference. And we have a data placement meetup, which is, like, at just after the keynote, if I remember correctly, on the Tuesday Okay. Which is, like, a meetup, an informal meetup where people can just chat. Mhmm. So we have two events. I'm I'm curious too because, you know, obviously, there's, like, in person events and there's virtual events. But can both of you, like, speak to in terms of Data Plus Women and even Data Fam, you know, the these events and and community, how do people get plugged in? Let's say there's, you know, not an in person event or a thing for a month or whatever. What's what's kind of the asynchronous? How do you get involved? How do you get plugged in? Who do you connect with? Those sorts of things, part of that community that people can can start tapping into. I think for me, I think the the connection through as part of the Tableau user group's sphere and the data found, I think that's really powerful because there's loads of stuff going on. So you you can kind of cross promote each other stuff like the the London talk that happens kind of on a on a frequency as well. And there's tons of stuff virtually like an the analytics user group, and loads and loads of stuff to get involved with. So I feel like there's well, it was almost too much to a certain extent because there's so much you could get involved with. So I think LinkedIn is, probably, like, the most used valuable thing in the user group network and connecting into that data fan because there's so much promotion on there of of great content, of great user groups. And then there's also then the write ups. So to make it more accessible as well, I try and write up the Data plus London events, which are hybrid anyway, but I try and but I can record it and share it. And then I've also got a blog where I kind of do a more in-depth, stuff on that as well. Yeah. That's awesome. I love that. And about any other tips from you for kinda plugging into the community? No. I mean, I started on a TikTok and the the Turkish woman's Zurich, virtual because we were nothing around where I live, you know, because I was just jealous from the data women London group. That's a true story. But, yes, I think it's important. If you, can meet in person, it's fantastic. If not, just create your, own group or join like a virtual group. It's also it's also good. It's also a fun learning, I think, on how to run the different styles of events. Mhmm. So I'm running it from virtual is a very, very different game to running it in person. I think even hybrid is another skill to have. But, like, you just gotta think about your audience, and that's the really fun challenge I found being a colleague. It's thinking of different games to play online or different games to play in person and just you've really gotta think about your audience, which which is a good skill to have in any day to day event. Mhmm. I'm curious, Ellie, as well. I and, you know, tying it back to John John Lewis, running events, getting engaged, understanding users, How have you taken maybe some of those lessons that probably you've you've learned largely from the the Tableau community and kind of applied them at John Lewis in in creating enablement pathways and understanding users there? I mean, what are some things that you have learned and that you've applied? Oh, there's loads so much. So in part of the data community, there's loads of different initiatives using Tableau. And part of that, you can apply to just general storytelling. But, for example, I need to run them again this year, but run an initiative which I called mini vis a thons. So it's kinda why you go in and you either you provide them with a dataset, and it's a non business specifically non business dataset, which is kind of what the community is anyway because then it's fun to share on Tableau Public. But it's a way of approaching data storytelling, of approaching datasets, and working on how to break it down. So I'm a big, big fan of quantified self, and I look at my Strava data, my Netflix data, Spotify data, any kind of data about myself I can get my hands on. I then love finding the stories because it's so personal to you that actually only you can see the story. So for example, these things up here in the background, they are actually That's funny. Much rather data from a couple of years. I entered it to the Volley Academy summer exhibition. It's a piece of art. But it's good fun, and you can therefore I know exactly what those colors mean and where the, like, highlights are on my year, but no one else does, which is really cool. Mhmm. So I then took that kind of initiative of understanding storytelling and kind of make of a Monday style, which is one of the data initiatives into what so I would host these events at work that had a pre session to be more technical. So here's how you extract your data from Netflix, for example. Here's how you'd join it on for more context data, like ratings of your TV shows, more colorful context. And then we would spend, like, an hour and a half together as an event and, again, hybrid event, where I would go through a specific theme about best practice. So that's stuff like what colors to use when, and when to limit them, what charts to use, but even just how to approach it, a dataset analysis like five w's and why, where, what, etcetera. And then we'd spend an hour independently visiting together, and then and then come back and share what you've learned or what you've got up to. And and it's very much a work in progress to hopefully promote later. Yeah. So that's one of the initiatives I've learned from the data family brought into work. But there's also I've taken a visualization, and we've dissected it, and then rebuilt it in a more traditional kind of make it a Monday style and ways of working style, which is good fun. But I think as well, we ran, a year and a half ago now, we had a big event called the inspiration day, which Tableau kind of helped us with. But as part of that, I ran a championship, which was a data competition. So we had, three different datasets, which were, around kind of pollution and the carbon cycle and kind of making it kinda tie back to our ethics and sustainability. So we had a dataset on, emissions across Europe, and a dataset that was internal on our weight risk emissions, which was a very kind of, the first look at that. Or they could go in, if they were technical, go and find their own dataset using, GitHub or get various different data source areas. And that was really cool because the winners of that, the three winners, presented at the day. So it was I learned again using some of my skills from outside of the data file. I hosted that and presented that and kind of helped them with that kind of storytelling on stage. So taking it from the technical analysis through to that storytelling and presenting and ran three sessions with them, which is awesome. And I think the one that actually was a tool that's really annoying. It can't be on Tableau Public, but it was a tool in Tableau that looked at emissions data. And you could put in your partner your membership number, and it would tell you kind of where you should maybe buy different products because the process of getting it in was too common loading. So that was a really cool but, actually, one of the finalists, and this is pretty cool, only picked up Tabloid three weeks before entering. So it's fun to see the progression within that as well. So, yeah, many different ways to apply the data from stuff to work. That's cool. That's a very cool, yeah, success story, I would say. Have you encountered people reluctant, internally to, play by, usually a data farm activity that would be fun outside, but internally you say, no. Because that happened to me. That's why. Yeah. It's a really hard one because often community is side of desk and these initiatives actually take quite a lot of work. And to have people turn up is great. To have them turn up and be interactive and actually, like, participating in another level as well. So, yeah, it's it's a real mix, and there's definitely reluctance there. I think as long as there's a willingness to learn and talk to them and get feedback on how they would want to learn or how they'd want to interact. And, again, it's using different types of data. And so maybe the Netflix didn't really appeal to them because they weren't, didn't watch it, didn't have an account. We did provide, like, a sample dataset. I made my team anonymize their data and sacrifice their data for it. But it's about finding that hook for them and finding that relevancy, I think, is the big thing, and and that even applies to kind of more standard learning. We've got our tablet programs, but often which is we've made on a job in a superstore kind of style dataset. But, actually, what's really helped those reluctant people is going in as a more kind of targeted enablement effort and actually taking a subset of their actual business data, using it for their role, and how they can actively use it rather than they kind of making it more generic or more fun, in the business sense because not everyone not everyone sees the fun of of Tableau and using data analytics. And that's okay. For most for most, it's just a tool to do their job. But, yeah, that balance is a fun one to navigate. Well, I just someone had had actually just asked essentially the question that Annabelle had asked. You know, do you find it easy? And this might be a maybe this is a slightly different shade too because the question was, do you find it easy to get people to attend data literacy programs, which, you know, I imagine that there are a lot of people who I I don't I'd what did I say? This is gonna sound worse than it is. Is. They think they know how data works, and then you kinda get to the education process and maybe there's a fundamental misunderstanding or something to learn or maybe, as you had said, maybe they don't have that willingness to learn, so they're not open to you know, how do you how do you meet that resistance and and make inroads to getting those people involved? Yeah. Of course. And I think this tie into that whole data culture piece as well. So, first up with the programs. So we constantly have feedback loops, and we have to put it on our programs. So the one that was released a couple of years ago, it is too long and doesn't it it's almost too comprehensive with what we wanted it to do. So we're actually in the next kind of quarter reassessing that, and we've had feedback that it's too long, and that those that want to learn are put off by the time that it takes. Mhmm. I think it's about eight hours long, and it needs to be, like, an hour, if not less. So we are listening and feeding back on that. But I think also a couple of things on getting them engaged in the first place. We have gamified everything. So you can earn badges. We have a dashboard where you can track your progress. We've now started automating our journeys to get people to use it using Salesforce Marketing Cloud, where we've got that on the marketing angle of having automatic things of you've not looked at your training in thirty days. Do you wanna have a go at this again? And that kind of thing or celebrating their success is a big one as well. So celebrating when they've completed a program or when they've met, like, the different levels that we've got with our astronaut theme. You've gotta be a data cadet, then you're a one star mission crew, then you're kind of on various different kind of astronaut levels. And kind of gamifying it and tying that back into the story is helpful. We're gonna really develop this that this year through our new kind of portal site and have it that there's gonna be different missions across different platforms. So we're gonna have some snowflake mission stuff. There's gonna be different badges that you can earn by doing various bits like navigating a different website or learning what this definition is over here and and stuff like that. So that's really cool. We're hoping that will get people engaged. But it's also that leadership buy in piece. So, it's getting leadership to understand that their team need to prioritize and put time aside for this for the long term health of their team. Mhmm. And that is really tricky and ongoing. But I think for data fluency, it's it's giving up that time to understand it as a team and understand how it's relevant to you rather than thinking you might just know it because you did start at university or that you've done some maths courses. It's actually then, okay. How can we take that and adapt it within a team setting to stuff that actually matters to them? So for example, with our finance team, we went in and did a whole kind of we call them a roadshow, but we took people from our data platform team, my analytics team, and our Tableau team, and kind of had a big roadshow where we, got different use cases and basically kind of inspired them that it was relevant to the training. It was relevant to them. It was relevant to their journey. They could save loads of time. They could automate stuff if they understood it better, and it would have this knock on impact of if you understood what this number over here meant, you could then translate it through to business value. Mhmm. And often often putting that business value is is a helpful one to get people engaged. Very cool. These are some quick questions that kinda came in a little earlier, and I think it pertains to self-service and and resource creation and a lot of what you're talking about. But these two questions are they're kinda similar, so we can probably answer them at the same time. But one had asked, how do you monitor and catalog your various assets created by consumers of self-service? Do you have a process or a check post that goes on? Which see sounds like it kinda comes back into the to the to the maybe even the governance side of the of self-service, like the flip side. And then someone also had asked, how do you keep training and enablement documents up to date with Tableau upgrade? So essentially, asset creation, how do you do you centralize it? How do you stay current? You know, how does that work? Yeah. So on your first one about monitoring, we I think it was about monitoring our progress. So we have various dashboards and assets management programs. So we have all of our in works. So all of our on on demand proficiency programs are on a program called Workday. We then extract the data. We can look at exactly who's doing what and also who's uploading what. And we use a lot of the Tableau. Because on Tableau cloud, we get a lot of the, Tableau management data sources. Mhmm. And as an admin, you can see a lot of the metadata from Tableau. So there is the, admin insights data source, which shows you events data and user data. We've also then linked this to our workday data so we can see which business areas are doing what. So that's why I'm managing kind of who are our star uploaders, who's actually using the platform, who's viewing, what content is being viewed, and by who, and how effective that is. But in terms of managing the stuff and the content on there, we have different project setups. So we have, in each, our tablet cloud is set up by business area. So there's a finance, there's financial services, there's Domino's, Waitrose, trading, etcetera. And in each of those folders, projects, we have a sandbox, a dev, and a live or tested on live. So everything in the sandbox area, anyone can once they have a license to, publish, can publish into that particular sandbox. That is not managed by us centrally. That is managed by the business area, and we try and we have identified a project leader per business area, and it's taken a lot of work to get here, but they basically are responsible for that sandbox. If they want subfolders, they can have subfolders, but, basically, it's just content in there. Anything that getting gets put through to the kind of test release environment and then through to our live folder, that's all gone through our governance and our central governance system. So once it's in live, you know, it's trusted. It's got that green tick on the certification level of the data source. We have watermarks for our various templates so you know that it's been kind of certified and trusted. So we centrally and the engineering team look after the maintenance and support if it breaks for anything in the live environment. But we have been very clear through our Google sites and through any documentation we have around this that anything in the sandbox is the owner's responsibility. And that's an interesting journey and interesting question in itself. So that's kind of how we manage kind of content creation. We also have various utilization dashboards to help with that management for that project leader when we support them very much. In terms of our programs and staying current, we just constantly feedback and iterate. So we did a whole launch last year before cloud AI really got into all of our stuff. So we're now having to go back and and redo them. But the way that we set them up is that we have various programs and courses underneath them. And that means we can just add a course, or we can add various programs. And the way that we've done it is to be able to have those toolkits and have used be able to pick and mix what they need anyway that's applicable to their role. Mhmm. So say that they need that particular thing on AI. So for example, we need to go back and add a little tablet agent stuff because that's what we have, a journalist partnership. We've just had a pilot over Christmas, and we're now kind of rolling that out. But we will go back and add in those programs. And, also, because we're trying to now be more than just Tableau in our proficiency, we are gonna have to go back and update references to different programs. So we'll just keep updating in the way that we we have set it up so that it's a really easy implementation as much as it will be a little bit painful to your life and update things consistently. But, yeah, as as the as the data world consistently changes and especially with AI coming in, yeah, we'll need to consistently be up to date with stuff. Yeah. And it sounds like, again, having these delineated roles and people dedicated to enablement allows you to be able to go and update those resources and stay up to date and and stay in tune with Tableau updates and and, of course, other platform updates as well. So it it hope we hopefully, you have more more bandwidth to be able to pay attention because, again, that's more of a central part of your job versus I'm doing this in the one like, one hour a week, you know, sort of capacity. Yeah. For sure. We are still a small team, and, hopefully, hopefully, we're gonna grow this year, but we're still only a team of four or five of us. So lots to do. Yeah. And, Eddie, at the beginning, of your introduction, you mentioned that at, leadership, you start as a type of expert. And then your role evolved because you are capable of convincing your boss of having, like, an enablement practice. Can you maybe share some tips how you did this by him? And, also, that's going along with Harry's long question, how the enablement initiative are then evaluated, and then you confirm that is, you know, needed. Yeah. So how do I prove that my role was needed? It's an interesting question. So, actually, as I mentioned, I've been there for three years, and I've kind of moved through different roles. And, And, actually, what I didn't mention was that I, up until beginning of February, was always on a full time contract. So I was a I was, a full time employee of the partnership, but always on contracts, the twelve month contracts kind of thing as as someone that needed, like, stepping it up. So I had the year contract, and I was always under the guise that it would kind of get extended. But, actually, as of February, I've been now a permanent employee with theoretically no end date to my contract, which is amazing as part of this whole new team. So I think the way that I personally done it is to really work closely with our Tableau, central team, and really work well with the team to highlight those use cases of value. And it's really hard actually because a lot of what I've done to kind of prove enablement is on the side of desk. So all of the work I've done with the community has never been part of my role until maybe eighteen months ago. Mhmm. And so I pushed to do that. I pushed to have that by highlighting the number of people that came to my things, by highlighting the stuff that they were then creating off the back of it. I would consistently, advertise to my leadership team that this was happening. I would send them reports, and there's a weekly note that goes out by one of our leadership team, and I do everything I can to push my team's efforts in there as little paragraphs and little ways of winning. I would also for those people that attended the events I was running, I would make sure that their managers knew anything cool that they were creating to show that there was value. But I think to prove that kind of data and enablement specifically is worth investing in. It's a really tough one because value is actually indirect from an element. So you can't say, oh, I was responsible for this particular monetary value that someone else has found using data. And that's, you can indirectly. And so that's that's where this is kind of more gone to is assessing those use cases where through hackathons, through finding data sources, through providing training for that data, you have allowed the person to either using data and uncover monetary value. So, for example, in some of our stock returns, they found that if they had some certain stock actually available to buy rather than letting it run out, you obviously use you you don't want money that way. But, like, some stories like that, finding and validating and signing off and getting exec sign off for those monetary values is helpful. But a lot of the cases, it's to do with more hours saved or kind of skill unlocking. So within the finance team, I think there was one use case where they were spending forty hours a month on running a ton of tabs in Excel and running loads of macros and getting stuff ready for this report that they can now do in two days using Tableau. So it's it's it's highlighting that, and then you can actually convert that into an average salary per hour kind of monetary value. So I think it's about highlighting those values. But those use cases are quite hard to find. So the way that we try and do it is, as I mentioned, we've had a Netflix and a TikTok kind of way of looking at insight. We run two surveys that go out. One for any kind of value that someone's who's using our product might have, and We go in and ask them questions if they answer that. But also then with the tick up on it, it's generally more like hours saved and how we can kind of attribute that. And through all of our tracking of our success measures, we know where in the business people are using our products, where they're using our platforms, and we can just go and talk to them and find out kind of what they're doing, and how that kind of works that way. So, yeah, a lot of our adoption and measuring success dashboards and metrics, which we're now kind of expanding out to other platforms is is really key because, also, it's not just about increasing active number users. It's also about increasing the stickiness score. So the number of people that are coming back to stuff again and again and using it on a regular cadence. So not just logging in to view a new report. They're logging in weekly to see their numbers on a Monday morning, that kind of thing and those kind of use cases. That's awesome. I I I think that's a fantastic way to measure that value. Again, tapping into those feedback loops, surveys, making it relevant. And I this is not the this is a thing that we had heard we had heard in this in our in our past episode. The hours saved is a really impactful metric because, again, it it can be even in marketing, we struggle with this on the marketing front. Creating the perfect attribution model is like the golden goose of marketing. Right? It's just like it's almost impossible to do but you're still gonna try doing it. And at the end of the day, it's very difficult to find an exact, like, this specific action equaled five hundred dollars or whatever, you know. So framing it in hours saved is really, really, really a smart, smart strategy. I'm I'm curious too. You know, I what I love about this presentation so far is it really is a balance of self-service and governance. This, you know, enabling people but also making sure that there are standards in place. I'm curious now having done this for for a few years specific specifically at John Lewis, what does I mean, what does self-service mean to you and your users now? I mean, in in its simplest form, how would you define self-service? What does that mean for the average John Lewis user to have self-service analytics at their tips and the impact it might have? So I think for me and for our users, I think the main thing is that they're able to access and consume the information that they need to do their job. So that means that we are providing them with insight, with data products, with reports that allow them to very, very, very easily and quickly consume that information that then goes allow them to do their job more effectively and make better decisions using data that's accurate. So that's why I think the we forget because we're so, like, analytics based that we forget that that's actually the main use and the main purpose. Mhmm. Because there's also that secondary layer with self-service as, okay, we've given them this first thing. But if they're not able to answer the business question, we also need to provide that self-service. They can go and find that information themselves. They have this access to the programs to upscale if they can't, but they can basically go and access. Because we provided the data sources, they can go and find out that answer, that question themselves. So whether that that is by us giving them the information or by giving them the tools to go and able to find that question to their answer. Answer the questions. Yeah. That's a great definition. That's a great reminder. You can you can certainly go down so far and so far down the rabbit hole that you can forget, oh, why what's the basic reason we're doing this again? Yeah. And that's where, like, our vision and our kind of, like, translating our enablement strategy up to the data strategy, up to the partnership strategy is really key, actually, in in keeping that North Star going. Mhmm. Mhmm. Well, that's that's awesome. We're we're at ten fifty eight right now, so we probably have room for, like, one one more little little tidbit. I think we did a good job of answering questions throughout. So, we'll we'll just go ahead and say the q and a portion has completed. But I I'm curious just in terms of of, like, next stuff. What's next for you? What's next for, DataFam or Data Plus Women or even even your role at at John Lewis? I mean, what's what's on the horizon? There's loads of really exciting stuff. So, as I mentioned, I'm speaking at Tableau Conference in a couple of weeks. I'm also gonna be speaking at London Summit, in a few weeks after that about our best practices and our authoring guides and how we've kinda set up our templates to enable people. But, yeah, I think for us, the partnership and for me, we are expanding beyond Tableau as great as Tableau is to all of our other platforms, and that what is what excites me because that will unlock Tableau as well. And it's all about that interlinking data end to end journey. And that's really, really cool, and that'll be really fun. And I'm looking forward to building the marketing around the user stories and user journeys. But But with Data plus Women, we've got three new cohosts this year. We're now theming our events, so I look after the networking events, and try to make it more accessible with tiny talks as well. But my colleague runs the, nonprofit section. We've got few nonprofit events. There's one happening tonight, actually. And then there is also, education, and then it's a massive year for sports women. So we've got we should have in the back end of the year stuff around that Rugby World Cup, and other bits of, I think there's a netball world cup as well. So there's loads of different themes within data classroom, and it's fun to expand. That's awesome. That's that's and that's a lot of ways to interact. Anemo, I pitched the same question to you in terms of I know we talked about it briefly at the beginning, but whether it's data fam or community things at Tableau Conference or Data Plus Women, I mean, what's what's next for you in in terms of what you have going on? And I know one one thing that's next, but what else do you have? So, yes, next for me is, like, going as a Tableau conference and try to steal some sticker from Ellie. Because now that I know that she has stickers, I want one for my computer. Now the, Like, the same, the data plus women. We are a this year as a data plus women's jury chapter. We are focused in data art. So we will have, like, different speaker choose here who are specialized in data art and Do something different than the business dashboard that we also love, but it's cool to sometime have your, mind blown away with a different way of thinking. I think it's quite cool. Mhmm. Those are all great things. It's an exciting time. This is this is the Super Bowl, the World Cup of data, coming up in in two weeks. But, yeah, I'll I'll just put a pitch out there too. I think on Thursday at, nine AM is Annabelle and I's, enablement talk where we'll be talking a lot about the wonderful resources that Annabelle has created, but also insights we pulled from data forms, conversations like this with Ellie. So we definitely encourage people to to attend those and Ellie's talk as well. And, yeah, to anyone attending, have fun. And if you're not attending, tune in virtually because there's still a lot of value to be had from watching those virtual sessions and and learning. Don't miss that opportunity, especially when it's free. So well, cool. Well, any final words, any sign off? I mean, Ellie, I just wanna thank you so much for being here and for talking. All of this was wonderful. And as usual, my only regret is that we don't have more time. Oh, no. Just just thank you so much for having me along. Like, it's it's it's an amazing thing you guys are doing, and it's so interesting hearing from different people about different efforts. Like I've mentioned, like, it's so fun learning from other people, so and everyone does it differently. So it's good fun to learn. And, yeah, just thank you for having me along. I've really enjoyed chatting about this, and I can't wait to see you guys in a couple weeks. Yes. Yep. I'm excited. I really can't wait. So thanks for joining. And, to everyone out there, definitely look us up. Find us at Hubbell conference. But to Annabelle and Ellie, we'll see you soon. Bye. Thank you, Annabelle. Bye bye.