We'll start with introductions. My name is Garrett Sauls. I'm content manager at InterWorks. My job is just essentially working with our team and working with external people like Annabelle and Nisa on just having fun conversations and sharing knowledge out into the world. But I will pitch you over to Annabelle and Nisa who are much more interested and much more, into the enable the enablement and data field than than than I am as a humble marketer. But, Annabelle, you wanna give a quick introduction about yourself? Thank you. So a very warm welcome also from my side. My name is Annabelle Rincon. I'm living in Switzerland. It's foggy today. I have fifteen years experience in data analytic, ten years leading teams of data analyst from different size and location. In my last role, I was leading a center of enablement, and I'm truly convinced that enablement is a key to success of the data analytic platform implementation or any digital transformation as a company. That is why we came up with Garrett about this, enablement, webinar series. And in order to learn from the best in the market and create also some awareness on this important topic. So today, it's a great honor that I'm receiving Nisa Marcus. Thank you, Nisa, for being our speaker. I will do a very short introduction. I will let you, deep dive into your experience. And Nissa Marcus is currently the analytics insights and your lead at Mars. And she has a wide range of experience spanning consulting, analytics, strategy, and enablement. Nissa, the floor is yours. Thank you so much. Thank you so much for having me here, Garrett and Annabelle, and for your kind words. I'm really happy to talk about enablement with all of you. So as Annabelle mentioned, I'm Nisa Marks. I'm part of Mars as an analytics insights in elite. So I have around a decade of experience in data analytics on the business side, but also on consulting. And in terms of what is my role and what I do, it's quite wide the scope. Probably teams in, project based projects, creating and establishing some problems of, digital upskilling. We also work with other teams, such platform teams, or platform health, or even governance, or even emerging trends. And on the part of the emerging trends is seeing how we can address new business use cases with those emerging trends, but also how can we incorporate that in data literacy, which I'm also very passionate about. In terms of background, I have a background in health where I was a clinical practitioner for several years, and then it's when I did that move into data analytics. So that's me in a very short nutshell. But and thank you so much for joining us today, and I welcome any questions you may have. Absolutely. What a great introduction. Yeah. I love it. I love having, two very experienced people with data enablement because I know you all have just seen so much in terms of how data and enablement have evolved as kind of tools and platforms as you have evolved, and users have evolved in their relationship with data. And so I I it's just a general note to say I love this this series and especially having you on here, Nisa. Thanks for joining. And, yes, of course, thanks to everyone joining us to to listen to this conversation. I'm curious. This is kind of like a big question. It's a broad question. There's no right or wrong answer. But it's one that that I like asking of people. But how, Lisa, how would you define enablement? When we when we had asked, like, what is enablement, what comes to mind for you, or what are those, like, core pieces? So for me, in terms of the enablement is giving the empowering people to understand and talk about a topic, a digital topic that encompasses that in analytics. For me, that is the the core part of enablement and empowering people to feel comfortable to follow-up their journey, to talk with others, and not be just in a situation where they hear new jargon and they feel afraid and they just nod along and say yes because you don't understand quite well. Yeah. And, you know, what do you think? And you just talked about that, but do you have strategy for employing people to feel confident Mhmm. In, in the data use? Yeah. Of course. So the the first strategy for me, and that is my opinion. I I see the upscaling programs as user centric approach. But if it is a user centric approach that is aligned with the goals or a business goal or strategy, and it's starting with concepts where people will come in just to see the the art of possible and how that fits in the environment that they are in. Because once they understand that portion, it's easier then for them to engage on anything new that you may bring. Because if you just do something that is new and you tell someone, and I told by experience that if someone comes to me and say, you have to learn this because I'm telling you it's important for you, may not go very well. Right? I may do it, but it doesn't you don't create that empathy with the subject. That's true. And if you learn something because you are forced, it's much more difficult. You learn better when you have fun. For instance, I try to learn German. It's very complicated. But, you know. But that's the thing. It can be complex concepts. Yes. But if you if you can understand where that concept fits in the big scheme of things, it's easier for you to memorize. Mhmm. I'm curious. How how do those strategies differ when you're talking about different types of users? You know, when we look at enablement, generally, what types of users do we have? I know sometimes we define them as technical users, nontechnical, or business users. So how do you, kinda support the different journeys for each of those people? What are the strategies like that you've seen work for those different types of users? I think even having different types of users, we have to establish versus your baseline. Right? Because you may have more established users, but still there are some gaps on the fundamental knowledge. And this is where the data literacy comes in. So starting with concepts that will be your baseline. Let's bring everyone to the same level. Even for some users, it is a repetition. Let's start there because then you're ensure that people will, be on the stage that they are ready to for the next step. But, of course, as I said, it's a it's a user centric approach where you have to see really what are you trying to achieve. Because when we talk about enablement and upskill programs, they can be very different for different people and different businesses. Mhmm. So goal is just technical training. That doesn't take any value of being an enablement program, but your goal is just training technically. But if it is to upskill people and making them more digital savvy and create that equity, then you have to see what are the needs of the the audience that you have. It may cater different small problems depending on the the audience that you have. Mhmm. I have seen a lot of benefits on that. And, I remember that, I used to run, like, a little bit of data literacy, data, sorry, data visualization and storytelling, introduction. And a lot of people got curious and even people who, doesn't use data on the daily life. But because they were, like, talk a little bit, this introduction about data storytelling, they could really use it on their PowerPoint. So they were able to make, like, much more impactful PowerPoint after that. So, yeah, you can see the benefit in every level of the organization. It's quite cool. You did catch a point there. Like, storytelling is a is a great example. It's very important. Right? Because you can have a solution that is technically amazing, visually very appealing. But if you cannot tell the story, present the insights you have for the people that will use that solution for decision making, then it kind of fell through the floor what you did because it can be all amazing, but it's not having the soul but as we know. But on the storytelling, what I normally like to do is even start before the the part of digital and digital analytics. As you said, all of us, we have business knowledge. We have information that we need to share with different audiences. And starting with that data literacy before data and analytics and digital, just showing people how they will tell the story to a different audience because senior reader will receive and consume the information very differently from an hour analyst. Right? And that is is a perfect example. For me, it's one of the things that I encourage people to to do is learning them to present it can be a power power PowerPoint. Right? How would you present this to a senior level, level? And how would you present this to your colleague that is on the same business understanding and technical understanding than you are? Mhmm. And that then you can translate very easily for data analytics. I love that concept. This is this is something I feel the more we we talk with other data and enablement professionals, this idea of what enablement is inclusive of. So, of course, there there we talk about the upskilling and the training. Ensure. You know, that that's part of an enablement program and building pathways for people to do that and and and developing data literacy. But that communication aspect of it is really big as well. As you said, the ability to not only be able to interpret and access data, but for those different users to be able to communicate those insights, for for them to read them reliably and then communicate them to their groups. Like, if you're a c level person, being able to communicate that at a board meeting or whatever it may be. Right? It's just as an important part of enablement as as the actual usage of the tool or the dashboards or whatever it may be themselves. Because if you're not communicating an idea, it's not gonna it's not gonna take hold with people. That's that's story component. Right? If you're not telling a story, it's not gonna be as sticky as I'm looking at a bunch of numbers, and I don't know how to contextualize this this in in the broader, you know, sense of the business. And I I I put my hand in the air because when I was starting, I was like that too. So when you are doing your analysis and you are bringing more data in and you find things that are interesting, And you're like, oh my god, this is so interesting. There's an insight. So I'll put this here and I will explain you, walk you through this. But then for a reader, what they want is, okay, you you present all this story to me. You gave me all these insights. What do you want me to do when we finish this meeting? But what is interesting for us as an insights, many times, is not actionable, and this is the part that is also important to understand, even in a PowerPoint. If you are presenting a one pager to a leader, when you have that half an hour meeting with them, what is the key thing that you want them to do as soon as that meeting finishes? Mhmm. Mhmm. And sometimes our passion kind of drive us away from that. Yeah. And usually, I had the slide. What I am expecting from you with three bullet points. That's yes. That that's it. And when you give best practice in, when you explain best practice on the dashboard or whatever, I also explain the best, practice, in term of visualization on this intro introduction course. And a lot of people use it on the PowerPoint because the same is what if you have a white background on your PowerPoint and you put, like, big number in yellow, it will not work very well, you know, it's exactly the same. So And this is normally one of the advice when someone's starting their career that you tell them it's good to have technical skills, very good indeed in data and analytics, but also don't forget the soft skills, that communication piece, because that will be your meaning or your channel of communicating all the technical that you apply in the back and how you got there. Mhmm. It's quite useful in several areas. Right? Absolutely. I think on that subject, I'm curious, you know, when you talk about building trust. Right? You talk about, getting people in the same room or speaking the same language or on the same page as analytics. What are some things that you've seen work? And that could be for a specific group. You know? We talk about, like, executive advocacy. You know? How do you get them plugged in and engaged and built trust in the analytics or just in the culture of what you're trying to do? Or similarly, just any group that you're working with, business users, technical users. What are some of those things that you've seen in your career that that have really been like, it it really clicked when I did this? It's senior leadership. It it does depend if you have a technical leadership or not. Sometimes you don't have so much a technical leadership. So it's good to start. At least what I have seen working is showing them the art of possible. Mhmm. Because if they don't know how that fits and what to ask from their teams, it's very hard for them for you to have that buy in. In terms of the the people that work with data and normally, we have people in enablement that people that's already very versatile in Excel. So they're already working with data, not necessarily in the digital tool, but they're already working on the data. So it's showing them that by using their data on a tool, like, say, a Tableau or visualization layer, the numbers that they are seeing correspond of what they are expecting. Let them play with their own data because then they can they don't come to you and say, oh, that was the tool. My my data should show these numbers, and now it's completely wrong. The tool is not reliable. But those two things, for me, so far, it's what I saw that works best to create that trust is understanding where they are, how it fits in the bigger picture, what they can ask from their teams, and then also realizing that their data is correct by using the tool. Mhmm. I like it. And, what works also very well on my side was, like, when we, not publicly, but in Internet, where we will communicate the, our success story. This, data product works very well. It generate this amount of money or it save this amount of money, this amount of time. And we document, like, we're we're already this success story, and that works well with, the manager, of course. And, I wanted on that, Lisa, I wanted to ask you an important, question because often you have to, like you said, sell it to the high higher person in the room that as an environment program is very, important. How do you measure the success and have the buying? I know it's a very complex question, but Yeah. It can be very complex and it can be quite simple. It depends on again, it always depends. It depends on the environment where you are. If you have a culture that encourages new knowledge and development, that may be a little bit easier. But, measuring success, you have the easy KPIs, if you can call that, which are the if people come to do your workshops or classes, so engagement there, attendance, you can track that. That is an easy KPI to track. In my opinion, doesn't translate to success of your program. It translates to bits of the engagement itself. The other way of doing it, and that is where you start, is a little bit more qualitative, but you start getting all of that impact because it's all about the impact that business having, these programs, the the goals, is asking people, what were the new skills since you start this program that you attained? Which new skills have you gained from this? That, as you said, is just talking with people, surveys, having that focus group where you can go back and ask them, have one to ones with them. That is one way of doing. The other way, depending on how you are doing your program, So I do have programs that are solution based, which is more on use case based. Not so much, let's create a solution. It's more, we have a business challenge. Let's start your design thinking. Let's see how we approach this. Develop. As we are developing, we are learning new skills. And then what is the perceived impact of that? And it may not be dollar sign to it. It can be, as you said, efficiencies, automation. It can be new insights because before that, you didn't have that data. Now you have access to that data. But those in the level of leadership and, as you said, buy in is easier for them to say, yes. This is a great program. But it's not just that level of the leadership that is sponsoring a program. It's for that layer of sponsorship that will report to their managers. Easier to justify something when you say, this team has progressed, and this is the type of impact that we already seen in business level. That's great. Mhmm. That's great. It sounds like a a lot of it too just with setting up an enablement program, like you say. You know, surveys, don't you can make surveys as complicated as you want them, but simple surveys are are an easy thing to do. So it sounds like when you're measuring it, it's it's more about intention, and it's more about setting up those checkpoints. Just, again, being intentional about them after you do that to report back on and to continually ask people and just to keep that dialogue open versus, oh, I'm just gonna run this training, this training, this training, this training, and then hope at the end that rolls up into some benefit that everyone will obviously obviously see. No. You you I mean, you'd have to you'd have to document. So I I really do like I'm a big stickler for documentation. So I really like that idea of, like, well, if you wanna measure success, it's just take regular checkpoints and whatever metrics you wanna set up. However you want to define that, there are better ways, I'm sure. But pick pick something and then kinda stick to that and hope that or or try and connect that with what what, you know, the higher ups are are thinking. What what are they looking at? What what do they care about? And try to send you your metrics along that. Yeah. And, that is where the user centric centric kind of approach comes in. Because if you do that, you're already having that, user centric approach. Because when you have those touch points asking people, what were the skills that you gained in terms of the technical skill or even the impact, the value of personal level you get you gain from this enablement, you can also ask, what are the things that are working and things that aren't working? Because people learn differently, and we have to cater for different ways of getting that information and learning from that information. So it's having that, almost agile approach with user centric altogether, where that is much easier for you to redefine a little bit the problem and adjust than to reach the end of the problem and think this didn't work. Mhmm. How to rethink everything. This is a question for both of you, and it's kind of on the on this topic of of being the person that does facilitate these things, the person that does check-in on these things and plan these things. But what do you think the value of of mentorship and coaching? Essentially, having someone who is a dedicated enablement professional, right, as as you both are and have been. What is the benefit of that for people who are who are on this journey? What value does that what difference does that make? You want to start, Annabelle? No. Start. I will agree with everything you say. We chat about, so stop me when I no. But for me, I love mentorship. So with, I love mentorship as part of the enablement. I love the mentorship outside of that. I I do like the part where you have not only the the the person that is responsible or the people that are involved in the enablement through that mentorship, but people within, let's say, the data analytics fields that have more experience and have those touch points with people that potentially are starting. And it is very valuable for both. So for the person that is starting, it gives you guidance. It can give them confidence of what to do next. Sometimes in data and analytics, we have so many options, so many pathways that can be overwhelming. So having that touch point is very important. On the other hand, people that are starting the journey can be from a different pathway or even coming out of the university. They bring so much energy, so many different ways of looking at things that you learn with them too. So I do think that it is crucial to have those mentorships, even if you don't have as part of your enablement program, to have a program outside of that that will complement what you you have. And about I'm a big fan of mentorship. So, I, I have been a mentor for, Tableau, but also for other topics, for leadership. And you learn so much when you have a mentee. It's really both ways, I would say. And, yes, it's fantastic. And I will say that it's often, good when, for instance, your mentor, if you are speaking, mentoring in data analytic, for instance, is not from the same departments than you. For obvious reason, because you don't have your article, but also because the data that they use are completely different. So you can, like, have this learning across, businesses that is very, very, insightful. So yeah. Big fan. I think the challenge sometimes, and I don't know if the audience, kind of relates to that, is sometimes we have that willingness to start a mentorship, but we don't know where to start. So my recommendation will be just reach out to someone that you think has the job role that you want or did something or or is on a position that you think maybe I could do this. I think I like this. Just reach out to to that person and have an informal conversation. Mentorship doesn't need to be structured, doesn't need to be part of an enablement. And I promise you, you will have things to gain from that relation. And the person that is a mentor, also. Normally, it's very hard by experience to find someone that you reach out for. Oh, do you want to be my mentor? Can talk over a coffee to say, no, I'm too busy for you. Mhmm. Just start. If you are thinking and you you are feeling that I don't know how to do this, just start with a connection for coffee and talk about the company itself and what you are thinking and see how people react about it. Yeah. That's great advice. I think about that too and the differences, when something has stuck. So I I've only I've only taken, obviously, being a being a marketing person for a data company. I don't I'm not in the data tools often. Right? I'm adjacent to them, but I've I've taken tablet training a few times, and I can distinctly remember the times where it stuck the most was when I had a really good mentor, someone who is personally invested, someone I felt comfortable asking questions of, and someone who cared to your point, Lisa, after the fact who checked in and said, hey. How are you? Like, how are you doing? Have you built anything? Like, what's going on? Like, you know, like, I'd love to see what you're what you're working on with this with the skills that that you've learned, and and those people come to mind as as making a a very big difference. And the thing they all had in common was, I was not afraid to ask questions of them, and no question was too dumb. And believe me, I had some dumb questions. So it was it's it's very heartening to have someone in in your corner like that. And the the the power of mentorship, I think, what you had said, Lisa, as well with reaching out to people who, are in totally different departments. I think early on in my days at InterWorks and not knowing anything about tech, I remember we were learning about an old column and database named Vertica, HP Vertica. And I had no idea how it worked. And that's the I was just barely getting to know what visual analytics stuff was, and I was just like, this is this is like a foreign language to me. And I talked to a guy named Josh Varner who is very intelligent and very patient with me. It was the same thing. I'm like, please fill me in. I have all the time in the world. I have a notebook. Let's do it. And I had the great the great benefit of running into him the other day at a at a at a restaurant here in town. And I got to say thanks to him, you know, like, thanks for those early days for for being patient and, you know, pouring translating all that high technical information into to marketing brain speak for me to understand. And so, from personal experience, I can really confirm that that mentorship, even as much as having a great training program, even as much as having clear, easy to understand videos and and how tos and stuff like that, it's it's the people that make a difference. And, it's a great source of knowledge, business knowledge. So let's say you already have that technical, but you need to understand the business. And sometimes you have people that are working on the business for years, like decades. And the amount of knowledge that you can have from the connection. And people like to share experience. That is what I find. People like to talk about their experience and what they achieved and how they did it. So it's, it's a winning situation, I will say. You will then lose to have those connections. And when we talk about networking, that is the beginning of networking. It's reaching out, putting our self out there, and learning from the others, and also sharing what what you know. Mhmm. Mhmm. I like that. It's a it's a same it's a little bit the same way when you have a voice, and you're little stuck with it, and you have someone from feedback. And it it's exactly the same. It's an external eyes that, pick up directly what's wrong with it or at least can be improved, and is very, very valuable. And, on this aspect, I also, like, was so proud when some people come time, reach out to me several months. Oh, yes. After, I gave a training, say, oh, Annabelle, look look what I have done now in Tableau. It's very rewarding for me. And on this rewarding aspect, Nisa, what do you believe is the most rewarding aspect of building data driven culture within organization? Exactly. Okay. Good one for you. It's seeing people embracing. So people that weren't very, kind of, willing or were a little bit shy on getting into a programme or embracing a new skill. And suddenly you see them coming in and talking and feeling confident about talking a topic. They say like AI these days, that's they they went to a workshop, and now they understand what is AI and what are the risks. And they can talk around that, and they want to learn more. That is the part when they come back and say, this was amazing, but I move on. I already I'm there. Tell me what's next. And that is it's amazing to see. Quite a challenge for you, though, because you have to prepare the next step. No? Yeah. But then we'll have different channels, right, to to to send people or to recommend. Because even ourselves, we are always evolving. We don't do an enablement program, and that's it. Okay. I created an enablement program in two thousand and eight and now I'm using that until my last day when I retired. You guys know that isn't like that. You have to evolve. The landscape in data analytics is always evolving. The software is improving. We have new software software coming. All of that, you have also people with with the time, and you will gain those channels where you can also recommend to others. Mhmm. Mhmm. I'm I'm curious. You know, this this concept of advocacy and mentorship, a lot of times, these these individuals are people who run enablement programs. But I know as you as both of you have ran enablement programs and organizations that you have data champions, these these types of people that emerge as people who are really kind of power users or passionate about sharing. What role do they play in kind of of of building up an enablement culture and enablement program? And are they, like, your best friends and you're, like, thank you for helping me out because you're the lone voice in the wilderness, and all of a sudden you have an ally to help you here. But, you know, how how can they take, a a culture and really kinda amplify it? So I like to call them as well, like, extension of our teams because they are. They are just the extension of the team that you have. Because normally, we don't have a big team to run this type of of projects. And depending on the size of the business where you are, you will need people that understand the different parts of the business. And if you are a central IT team, you wouldn't have that business knowledge. So you are making really you are pairing the best of the business logic with the technical skill that's you imparted on those users, your super users, and you are saying go to your areas and share all that goodness with the areas that you are in and showcasing how you can use the new skills, how that applies to you. Because they will be the best people to do that. It won't be my team doing it. It will be them because they know the business challenges. They have business knowledge. They can even recommend, oh, you could use this tool or you could do this technique for this challenge that you have. So I I see them really as an extension of the team that is trying to to get the the upscaling protocol. That's great. I love that. I I it's always fascinating to to me to see those types of individuals emerge. Sometimes they're the people you would expect, but sometimes they come out of out of the blue. And they're the the person it's, you know, it's Fred from accounting, and you're like, wow. I I had no idea. He was so passionate about it. Data visualization and that but those are always the the most fun ones to me when you see when you see it click with someone who it might not be, they use data, but they're not They don't have an analyst in their title. Right? Or they don't have, anything like that to see them kinda take over as a power user. What what do they have is they have the pains of the things they are doing at that moment. And if they see the value of becoming digital, they will be your biggest advocate because it's like, oh, I managed to save I don't know how many hours, or now my life is much easier because I have insight into this. And that that is the that buy in that we're seeing into the business itself. Because once you have someone that understands that and sees how they can use the new skills to address things that are real for them, not with dummy data, not with training data, but with their own use cases. You create that empathy. It's like your product. It's my business use case. I will do it, and this will be amazing then I can take to others. And usually these people are really want, like, to give back. So when you have someone that really want to share his knowledge, your part as an environment leader, it's easy. You just have to train him technically on the thing that he doesn't know about the tool or don't know yet. And, he's already willing to. So it's a easy part for us to identify this kind of, characters. Mhmm. And, normally, I already people that are quite passionate about what they do. Mhmm. You will normally find that people that are very passionate about what they do and how they do it, and you even notice how they talk about it. Mhmm. Those are the your super users. And sometimes the super users can have the technical part, but maybe you can you have that user that has all that business knowledge and then can use others with technical skills to help the area where they are. Mhmm. I feel like that's when you see a lot of really cool stuff when you start marrying the technical users with the really passionate business users. You get them in the same room, and they're working towards a common goal. Right? Because I I feel like this is this is a thing in organizations. We talk about all the time in organizations, the siloing of organizations in different groups. Sometimes having different tools and very different goals and sometimes at odds with each other. But when you start getting them in the same room, talking the same language, and being like, oh, actually, we're all trying to do the same thing, which is provide value for the business. That's when some real magic happens. Exactly. And when they come back to you and talk about because even for the people that are driving the part tech the technical upscale, when they come to you and they talk, oh, because we can do this or we can do that for you this use case. And, normally, we will also recommend, so if you do that, why don't you add more data and have a few? And they will be the first one. Oh, that wouldn't work for us because of reason one, two, and three. And sometimes the conversation ends up with, maybe this isn't the tool that you need. You need to add another tool on top of this to be able to do that. So it becomes quite agnostic. You come out of just that technical know how to, k. Let's address that challenge that you have, and let's try to find what is best for the challenge and not the other way around. So you don't force feeds almost Yeah. Into the tool, but it's more you address the challenge with the tools that you have. Those are the the most advisable ones. Mhmm. I'm curious, Nisa, on another topic because I see that the time is running. We could spend, like, another hour, additional, but I don't know. You spoke several time about, what the future holds. Do you know already, like, the future trends that will reshape to data enablement over the next years? No. No. We're talking about AI this day. Not to say that we are all ready for AI, but it's something that has already happened. It's not nothing new. There are new capabilities, for sure. AI has been for decades already. Machine learnings is just a portion of the generative AI. But that will change. It will change the way that we consume information and insights. Now we are talking about natural language processing. We have the models already with reasoning. In the future, we may receive an email instead of being, a dashboard, a click, and we go to the server, you'll receive already the like, a report saying, this is what we see. This is why it's happening. But the way of consuming information will be different. Mhmm. And I'm I'm not saying that the technical skill won't be needed. We will still need because you still need that data literacy to to know what will be truth and if it makes sense, not accept it just because it's driven by AI that everything is correct. So you you still have to be careful with the hallucinations and things like that of AI. But the way that we will consume information will change. And with that, of course, they are scaling programs also. Mhmm. That's a really great point. I I think about this in terms of, again, from, like, the marketing front. Right? We we we think about information or news and how social media accelerated access to that. But the problem is is that access just because you have increased access doesn't mean that there's necessarily not, you know, garbage in, garbage out. We talk about that a lot with with with data engineering and with with data with, data science and AI as well. Right? It's great if you if you have that, you know, report, that KPI coming to you directly through email. But at the end of the day, if you don't have the data literacy to understand it and you don't have the right data foundation practices in place to begin with for it to be accurate, it what what good is it? It's just delivering inaccurate information faster now, and it it can it can accelerate problems. It can accelerate benefits, but it can accelerate problems. So that'll be a very interesting challenge, I think, for enablement and data professionals to deal with. There's that that topic of governance and really amping up data literacy. Right? Exactly. It's that part of the responsibility and how to deal with that. Because as you said, you will have much more information. And because it's we receive it so quickly that people may think, oh, this is a hundred percent correct. It was done by a machine with all this data. And then you have hallucinations that they already have and many things that Gen AI creates is one of the risks, and we know it has a lot of benefits. But just having that knowledge and literacy to know this doesn't make sense, we need to investigate further instead of just picking it up and say, okay. Let's act on this without having that reasoning behind. Mhmm. Mhmm. I'm still not convinced about AI, especially on the enablement topic because I think that we still are human and we still need this human perspective on it. And I'm not sure that I will be very happy receiving a long email instead of a picture, but that is my how my brains works. But then the development, it may be a picture with the inside by the side. Right? You you may have two of them. But I think it's exciting for sure. We can see a lot of benefits. We can see a lot of things that we have to be careful and we have to have governance around it. As anything in life, right? There's always this excitement, but that excitement has to come with caution. The same way as when we use data. We have to have governance around it, or even when we do this upscaling the problem. You have to have certain levels of governance because the last thing you want to do also is, people just creating reports just for creating because I learned how to do charts. But there's always that level of governance around it and how to do things and why are we doing this and how to approach it. Mhmm. Mhmm. I think that's that that's all the more reason, and I'll I'll I'll put a plug for you guys as well, to have dedicated enablement professionals. So to build up those relationships. Right? I think I think that sometimes the idea when technology accelerates is that that will replace relationships. Oh, I can I can use, generative and or machine learning to essentially not have to talk to this analyst or that person because it cuts out the middleman? Right? I get I get the analysis, and don't have to have someone build me a report. But if anything, I actually think it doubles the need for having an enablement professional to have that relationship to help contextualize things. Right? So when you do have questions, you're like, I'm seeing this. Is this accurate? Or help me understand this. Right? Because even if something even if generative generative AI feeds you something, there's still a whole art and science into asking the right questions and asking the right stuff of it. And then when you're a data enablement professional and you've been doing that for ten to fifteen years, you tend to hear a lot of questions, and you tend to hear a lot of, things around what does this mean and diving into the context in the weeds of the data. So I actually do think as technology accelerates the importance of relationships, particularly with people who are enabling it in data focus is gonna be just as important. And data literacy. So just this conversation we are having here, it's important for people to understand even if they aren't using machine learnings and AIs, it's important for them to understand what is AI and what are the risks that we are discussing here. Because if we have that knowledge, then people will also think I have to address this with a little bit of care instead of being, okay, I will just accept this and I will start using. And the governance goes from using data where you shouldn't be using it, sharing things that potentially you shouldn't be sharing. Like with data, you have governance on how you share your dashboards. You need that also in using AI and other tools. It's the data literacy that will be, I think, the foundation of any enabling, like, as it is already in the program. It's just giving confidence and knowledge to people to know in the big scheme of things what I do, what is the impact, and the same way how this can help me and where do I have to be critical about. Mhmm. Mhmm. Yeah. That's the best one though. Yes. I have another question that I really want to ask. And I think that we ask almost all our, guests, previously. What kind of which which piece of advice you will give to people who start their journey in data and analytics or maybe to to the advice that you wish you have heard yourself, like, several years ago? Well, I have two things that I hold earlier. One is be open to learn new things. Just carry on evolving and learning. That is your biggest gift that you can give yourself. And the second one, don't pull yourself to specific technologies. Just have that agnostic view of the world. Mhmm. Because that question that normally you well, in the past, you will go for a meeting view, and people will say, where do you see yourself in five years? Honestly, these days, for me, it's very hard to say that. Mhmm. Who knows what will happen in five years in the landscape of data and analytics? So, yes, definitely embrace knowledge. Knowledge nobody can take knowledge away from you. And then the second one, having an agnostic view of the the data and analytics world and how everything fits together. Mhmm. What will be the task will give? Well, this question is for both of you. This is interesting. I think, I love that. I really do. I again, the on the agnostic field on focusing on foundational knowledge, that can be transferable from one thing to another. And I think the one thing that I that I will say, and then I'll I'll have I'll have Annabelle talk because she'll have more specific specific details, right, is, I really do like the emphasis on on the soft skills. I know at the end of the day when you're looking for roles in data and analytics, a lot of times you're gonna see, you know, you need to know, you know, Power BI or or Tableau or whatever it may be. And, of course, there's there's gonna be those things that you need to know. But, that those soft skills as technology continues to accelerate and becomes easier for the business user, how you communicate about it, how you work with others, how you tell that story, those are gonna be the things that matter more and more and more and more. And so that's it in my mind in the next five to ten years. If I were someone who were in this field, I would be thinking, yeah. Learn the tools. That's good. But how am I gonna tell a compelling story? How am I gonna connect with other people? How am I gonna again, how am I gonna learn from other people, you know, that that aren't like me continuing to branch out that network and make it bigger and broader, I think is probably gonna be the most important thing that I can think of. And I would I would apply that to anything, not just data and analytics, marketing, you name it. That's just that's kinda the name of the game because technology drives everything. So No. I think that you have both, like, spoke about, with topic very well. Indeed, soft skills, yeah, are very, very important. And I always, make sure to learn a lot of different software. So when people say, hey. But you're specialized in Tableau. Yes. But before I was specialized in SaaS, I know also Luca. I know probably I know the Google Squeeks site. Name a tool. But it's very good. And when you start learning one another tool, you see that I mean, the grammar is maybe different, but it's there. Everything is there on the same component. So you just have to learn where it is and how to place it. What is very important in, this field, is really learn the best practice in, best, data visualization practice. And that is very important. And then you can apply them in every tool, I will say. Definitely. Best practices, it's, if you have that ingrained and it's part of the soft skills itself, you can it's quite easily transferable. As you said, technical. So you you talked about visualization layers such as Tableau, Google, Power BI. So those, even if you have one still in one software, it's easier then to pick up on the other one. What, I was saying, not just keeping one software as just concentrate on that one and don't look to the others. Because more times than money, you will be on a business and the strategy changes and you need to adapt. Those are the things that you need to account as you are building your career and building your progression in the the data and analytics. Love it. That's all great info. I am going to because we're about five minutes out, we'll open it to questions for people. While we're waiting on those questions, I'm going to ask one, Lisa, that you had included, which I think is really interesting. And it might be telling of, something on your mind. But, what is one data myth that you wish would disappear? Can I say two? Sure. Yeah. As many as you like. So having more data is equal to having better insights. That is a myth that I wish it disappeared. It's not more data. It's more quality of data that we should have. Not necessarily having more data will give you better insights. And the second one is on the back of the conversation we are having here, enablement is just training. So if you do training, you are able to do everything. So those two are the main ones. I could give you a third one if you wanted, but, it's free there. So data visualization is just pretty charts. Oh, yes. I hate this one. Those three are are a killer. Make it pretty. Make it pretty. Yeah. I love that. I really I'm I I I love the one that more data is not always better. That's great. Yeah. And, yeah, data visualization pretty charts completely agree. Those are also good, though. And training and enablement, that's that's a big one. That is a big one from from someone who even working with enablement people, you know, tangentially, that's one that I continue to learn more and more about that it's not just training. And that's a big misconception for people who are in organization, and all of a sudden, they have, like, an enablement culture starting to take hold. And they're like, oh, yeah. I mean, yeah. I've done the training before. I've done this and that. Well, it's way more than that. Right? So But even for the leaders, right, I think the the the part that we start is gonna be a little bit more dangerous is you have a team, and you think that just because you train them during the week, you will they will be able to do a solution next week. So the expectations are completely misaligned. That is, that is the risk of the thinking. Like so Mhmm. Yep. I can tell you. I have take I can't tell you how many, you know, little LinkedIn certification things that I've taken. And, you know, you go through the modules, and you're like, yeah. I'm learning something, you know. And then you get the certification, you put it on your profile, and it's two months later and you're like, I can't even remember what happened at that core. I can't read it. I can't remember a thing. And that's the difference again between having a human, another human at the other end who cares about your your progression and development beyond you ran through three modules. Right? That's the difference in what makes it stick versus I got a digital certificate that says this, but what does it amount to at the end of the day? I know we are getting to the end, but that is the part of, loving those, digital upscaling programs that are based on use cases and business challenges. Because you will be learning the skills that you need to address the the challenge. So that's it's more impactful on personal level because you can see how you can apply what you learned instead of just going through a standard training course. But then it's difficult to make that bridge between this is training and this is how I apply it. The best way to learn is when you learn why not even noticing that you are learning. So that's the best way. Or when you are, like, doing, like, a visualization challenge or whatever. They are, like, learning something, new skills, but they don't even, like, notice or apply it directly. It's the best way. You can see the value of it almost straight away. Mhmm. Yeah. That's such great advice. The best the best way to to to learn something is, a, to do it, or, b, teach it to someone else. I distinctly remember when I when I think back to university and stuff, like, a managerial accounting course, which I haven't thought about that in a while. But I the reason why I succeeded in that course was there was a group of us, and we had to teach it to each other. And I was like, I never thought I would learn this, but here we are. But when you apply it, I think that's kind of the common thread. However you apply it, whether that's teaching to someone else or doing a challenge or creating a tangible output, that is the key to really making it stick to it. Well, Greg, well, we are at one minute on the hour. Lisa, Annabelle, thank you. Lisa, big thanks to you for joining us. This was fantastic. I really appreciate all the knowledge you've shared. I'm gonna be watching this one back and being like, okay. What did she say again? You know, to to really make it sit because there's just a there's just a lot of gems here, I think, for anyone who's building in the Nanaimo practice or anyone who's participating as even just a user in the Nanaimo practice. So thanks for joining. Thank you so much. Thank you so much for inviting me. As you see, we could talk about upscaling, enablement, data analytics for hours, but it was really enjoyable. And in the audience, if you want to reach out, happy to to discuss if you if you want to to ask questions or just learn a little bit more. Please, Annabelle, you can share my LinkedIn. Yeah. We will. And that's that's one thing for for people, who are tuning in. This we'll we'll email the the replay out to everyone, and we'll we'll include all those details. And, of course, it will post it to YouTube. It'll be on InterWorks social media. So this will be posted, if you wanna watch it again or you wanna share it with someone who you might think find this interesting, or if you wanna reach out to Nisa and Annabelle and Annabelle who would love to connect with you after, it'll all be there. K. It's okay. Thank you very much, Nisa. Thank you so much. Thank you so much for joining us too. Yes. Thank you everyone for joining us. Appreciate it. Thank you. Bye. Bye.