# 12: Real Lessons from Building a Data Literacy Program with Neil Richards (The Data Literacy Show)

## Метаданные

- **Канал:** Data Literacy
- **YouTube:** https://www.youtube.com/watch?v=L73IK6CQ3to
- **Дата:** 10.12.2025
- **Длительность:** 42:31
- **Просмотры:** 81

## Описание

In this episode of The Data Literacy Show, Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) talk with Neil Richards, an award-winning data visualization expert and former Global Data Fluency Lead at JLL, about what it really takes to build and sustain a data literacy program inside an organization. 

He’ll share tips on…
- Securing early wins and leadership buy-in
- Little “data moments” to integrate data into the organization culture
- Recognizing the shifts that show your program is working
And more!

Full show notes: https://dataliteracy.com/episode-12

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🌀 Co-founders Ben and Becky Jones, started Data Literacy, LLC in 2018 with a mission to help people learn the language of data. To help our customers become more data literate, we design, implement and continuously improve cost-effective training and certification programs that we deliver online, on-site and on-demand. We aim to demystify data, and to make the learning experience fun and enjoyable. A main tenet of our offerings is that data simply provides a lens into our world and our humanity.

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#DataLiteracy #Data #DataVisualization #Education

## Содержание

### [0:00](https://www.youtube.com/watch?v=L73IK6CQ3to) Segment 1 (00:00 - 05:00)

Welcome to the Data Literacy Show, the podcast that helps organizations build, measure, and level up their data and AI literacy. I'm Ally Torbin, the senior data literacy advocate here at Data Literacy. And I'm Ben Jones, co-founder and CEO of Data Literacy, where we are all about helping people learn the language of data and AI through tailored training and assessments. — Yes. And on today's show, very, very exciting. We have our very first guest. I'm so excited. And today we're going to explore what it takes to build and sustain a successful data literacy program inside an organization because you know Ben and I we have our experiences but it's so great to bring in someone else. Um there's a lot of variables and nuances to consider in every single case. So it's nice to have another example, another great person with experience to bring on the show. — Yeah. And drum roll please, that person is none other than Neil Richards. He's been doing this work on the inside for years. So Neil, if you don't know about him, you should. He's an award-winning data visualization uh expert. He's a data fluency professional. He's got more than three decades of experience in the field. He's the author of a book called Questions in Data Viz. Actually, you did the cover for that, right, Ally? — I did. It's a wonderful book about creativity and data viz. And you know, I like questions and it's an integral part in data viz design. So, I love Neil's book. — Exactly. Yeah. So do I. and he was most recently the global data fluency lead at JLLL. So, welcome to the show, Neil. — Thank you. Thank you, Ben. Thank you, Alli. Um, it's a great introduction. The three decades bit is scary. I don't even work out myself. Um, that was a bit of a um reality check, but — he's fresh out of college. — That's it. Yes. Late developer. — Three decades goes by fast, doesn't it? — Yeah, it does. — Oh, boy. It really does. — It really does. It does. Well, Neil, it's great to have you on, you know. Um, and yeah, love your book. I mean, it's really cool that, of course, Ally uh designed the cover for your book, for my book, even for her own book. So, Ally is that amazing talent in the data world. And so, um, you know, and of course an amazing podcaster. I was able to ride her coattails for the past year, uh, creating this show and 11 episodes down and this is our 12th, the last one of the year, and we get to have a guest on it. So, it's a great way to wrap up the year. We got some other great stats, didn't we, Ally, to uh to kind of just celebrate real quickly and get that out of the way and then we'll uh we'll move on. — Yeah. So, Spotify does that wrapped Spotify wrapped for people who listen to music and podcast, but they also do it for creators. So, people posting uh their podcasts on Spotify. So, we found out that our debut season, our first season, was the most popular. It was more popular than 85% of other new shows. So, that's a nice one. — Hey. All right. Thank you to the 85% that didn't do as well as us. We appreciate that. — They didn't try as hard. — That's right. — And then we also had um more shares than 76% of other shows. So, — people are listening. People are enjoying. So, we're looking to probably do more guests like Neil on our season two. So, be listening for that. — Yeah. We'll watch our numbers just go through the roof here with Neil. Um so, that's why we're excited. So, — no pressure kicking off season two then. You better say something viral, Neil. That's what we need. — Oh, wow. Okay. Cracky. — All right. So, first question, Neil. Um, the global data fluency lead. That sounds like pretty impressive. That's that sounds like a lot of responsibility. Can you give us an idea of like what that actually means day-to-day? — Sure. Um, it's certainly a uh it's an impressive title. I'll give you that. I mean I was uh responsible for data uh literacy data fluency programs within um a particular client at my last um company and it's a global company so it made uh it made it clear that it was for um uh all the people who worked for that particular client um across the globe was three or 400 people I think um and my particular role sort of originally branched off from um the BI role which was where my um sort of previous roles had been. So it kind of meant that I was responsible for bridging the gap between the BI team and everybody else. So all the consumers of data, the dashboards, you know, how could we make that uh bridge that gap? So it meant it started with um sort of uh content development. There's a lot of that for training things that I wanted to deliver. But it also meant things like um building documentation. uh what could we do like a data catalog things like that might be able to um assist people to understand more what was done in the um on the BI side of things. Um and then the more it developed the more it sort of moved slightly away from that and I focused a bit more on the training the uh the methods of delivery and um also sort of developing uh bits and pieces of content as well. — Yeah. So a very multifaceted role right

### [5:00](https://www.youtube.com/watch?v=L73IK6CQ3to&t=300s) Segment 2 (05:00 - 10:00)

all to boost this amorphous thing called data literacy data fluency of this large community of business users. So you know it sounds like it involves some data definitions and understanding to your point about the data catalog. It also involves creating other content to really help people learn. So talk to us a little bit more about you on a really practical level when you say hey you know inside this organization we're talking we're focusing on data literacy tell us a little bit more about what that means to you on a day-to-day basis. Yeah. So we really um what I wanted to do was focus on uh focus on the end users. So the users of the data products. Um we we've got some pretty amazing people within JLL who um uh responsible for the skills of the data folk the analysts in particular. So we have some pretty um worldass stuff out there I think or you know that we use internally at JLL. But um again it was really just sort of focusing and so it really meant sort of going back to basics. We I called it well we decided to call it data fluency. There's a lot of discussion what's the difference between data literacy and data fluency and that's you know that could be a whole different discussion really. Um it felt a little bit um less binary and more kind of um friendly. Um, I think just having the distinction of that term sort of allowed me to um to feel like the focus was on those end users rather than on the producers of the data product themselves. Um, and so it meant it meant sort of really going back to basics. Um, how do we read charts? How do we understand data? How do we make um people use the products more and make them more confident? And um I suppose I keep coming back to this, but one of the first sort of bits of feedback I got was someone thanking me for making data less scary. And I kind of took that and I took it as a sort of punch line and almost like put it um you know almost literally by my laptop to remind me that's what I'm trying to do. I'm trying to make it less scary for people to actually approach the data products that are out there whether it's and you know in real life or whether it's um on the dashboards at BI deliver and all the other technology products that we have internally as well um and yeah just sort of gain that bit of confidence and you know again perhaps sort of move themselves up that sort of that fluency scale. So, we're not saying, you know, we're going to turn you from a novice to an expert, but it I felt it was all about what sort of small things could I do incrementally or what could we do to take people from, you know, from this level of scaredness up to this level of less scaredness, if that makes sense. — Yeah, I really like that idea of the scale, you know, just bringing someone up one tiny step of the scale. you don't have to go from zero to 100 immediately which I think people might think that you do or maybe that's the goal but really it's not it's you know move up the scale until you are able to do your job better and then maybe you focus on something else but I was wondering um do you remember what that person who said oh you help me make see data as something less scary do you remember what you did or was there something that they consumed or read of yours Um, I don't know if it was anything specific, but I think it was an attendee of one of the first um sort of fluency programs that uh that I ran. Um, and I think that, you know, it was just somebody who wanted to give feedback at the end of the series of training. Um perhaps we you know we put a a questionnaire out there and you you look don't you for the best of the um the open-end replies because it's always maybe it's something we that we inevitably talk about. It's very difficult to kind of um quantify improvement or even sort of uh put your just put your finger on exactly how has data literacy improved you know for for a person or for a team or for a cohort of people. So, um I think that was more just a matter of sort of finding just these individual um nuggets to try and um uh yeah, you use that as a measure of um of small success. — So, there was something about the training program that led this person in the feedback to say you made data less scary for me. Um what would you say when you look at your — ability and your approach to training? What do you think it was? it maybe or what do you guess it was perhaps if they weren't more explicit about how you went achieving that comfort level? How did you actually get them to h have that feeling? Do you know what I mean? What was really the approach there? — I think really the acknowledgement

### [10:00](https://www.youtube.com/watch?v=L73IK6CQ3to&t=600s) Segment 3 (10:00 - 15:00)

that I mean you try and consider what your personas might be, you know, what are the levels, who are the different kind of people that we need to train. And I think it was important to start sort of right from uh right from first principles right from the start to acknowledge that there are people who um have a little bit of fear at looking at the dashboards you produce — phobia we call it. Yeah. — Well yes or just sort of change phobia and again you know it took me a while to realize that data fluency data literacy is really um a change management exercise. And I remember um one time going to um another one of the offices I think perhaps just sort of slightly before I I came into this role and learning that you know some of the most um competent people there were using um things perhaps in Excel that they have always used for 20 years. and they vaguely might have been aware that somebody said there was a BI team that put out dashboards, but they were not interested in them because they were um happy and competent within what they had and would rather not change, would rather not learn something new. — Um so it's — yeah I think it's all a matter of understanding that you you're sort of taking people right from the very first principles and that some of it can be um a change management exercise. So something that I think when people who have focused on change management a lot of the times they'll talk about the importance of executive buyin you know there's always this debate is it top down is it bottom up and of course it's some blend of the two but with your specific experience what do you think was helpful or was there kind of consensus within the executive team that this was needed for this particular client this program this data fluency program and if so what were the reasons why they felt that way or if not how did you get them to sponsor a program like that at the onset. — Yeah. Well, um it's I I'm not exactly clear on some of that, but I think first of all, I was sort of very um fortunate. I decided sort of two or three years ago that I was quite passionate about moving away from being a day-to-day analyst and sort of seeing the need for kind of data um literacy or fluency roles. And whereas a number of us work in different um areas, different clients, etc., I was fortunate enough to push and think look I think we need this role and for um the uh the senior uh members of the um team to be able to give me that role. Mhm. — And I think one thing that we uh that where I was able to position it is we have a kind of existing um uh university if you like which is um the not only the client but they we had the we called it the client university — where there were a number of different programs that people could join. So people new to the account would learn sort of um a module on basics. people more experienced would take a leadership module. Um, and there was room if you like for a data module, a data um data savvy module we called at one point and then I moved it to the data fluency. And I think being able to pitch it there so that there was already the um the leadership team were already committed to um continuous learning improvement if you like. that position was there for me to say, well, look, all right, if I take this role, I would like there to be a um an entire uh section around data literacy and I would like to be the one who delivers it. Um so that was kind of my in if you like. Um — so fitting it into a framework that already existed instead of saying that's right this whole thing needs to start brand new from the ground up with no infrastructure, no other tools or components to it that are already there. So you really found that it was good to position it that way. — That's right. Yes. And it gives you a kind of a brand if you like because it then takes the same amount of um seriousness as um leadership training and of you know um intro training onto the account as well. to all the other kind of modules. It can be pitched at the same level of seriousness and at the same um you know it gives people's managers the option to think you know I think this is uh this is where we'd like you to take your next training for example. So that was kind of a wein if you like um and enable me to sort of develop um some of the content for people. Mhm. — How did you know what people needed? Like were there some sort of learning path or assessment that people took so they knew, okay, hey, I might need this particular module and this go to this particular brown bag or whatever or was it just everything was open and they could choose what they wanted? — Um, it probably maybe it wasn't as sophisticated as that really. It was just a matter of of I um developed what I thought was um a sort of series of um uh a series of modules that would

### [15:00](https://www.youtube.com/watch?v=L73IK6CQ3to&t=900s) Segment 4 (15:00 - 20:00)

be useful for everyone. And I did sort of try and pitch it um so that people who were um new to data and new to the account would be able to sort of start right from the first principles and we would then move on. So certainly what I'm talking about to start with it wasn't a matter of um you know perhaps small boat says modules that people could take. It would be um sort of set um hybrid delivered um sessions lessons that people would be um people would take and would be encouraged to um attend. Um, and then just sort of very brief and basic knowledge checks as a result just sort of to make sure that people were uh were happy with this at um and you know not overwhelmed by the content. — Got it. Yeah. And then so you know you're building out all this content putting it into this u this university uh system right and then going about kind of deploying that and being involved in all of that. So when you're looking back on there on that, what would you say are the handful of maybe guiding principles that you really feel you had in your mind as you developed all of this uh program? Really, what were the principles associated with it? — I think from my sort of principles from the point of view of how I wanted to set it up was just to make it um to make it accessible to everyone and to make it kind of light touch. you know, I didn't really feel like I could go barging in there and take a lot of people's time. You know, this is something that we were u making accessible to people from time to time. I wanted it to be sort of not too overwhelming for people. I didn't want people to feel like um they have um learning fatigue, for example. We're a big sort of remote um company and every so often there's more learning that you're required to do and you might think you know really why do I need that training or why have I been picked for that training and you know can I do this um and um uh you know how much attention do I really need to be paying to it while I do it. So there's a sort of acknowledgement that there is a potentially learning fatigue unless people sort of understand how important it is for them or people aren't sort of too overwhelmed by it. So, um, yeah, again, it's making it sort of, uh, making it accessible, make it kind of light touch and that was sort of true with some of the other initiatives that I wanted to do out outside of, you know, that that's kind of where it began, the university position, but um, brought in other things such as um, awareness weeks and more sort of um, uh, sort of communication in uh, in newsletters and in meetings that sort of everybody got to um, attend just to try and build up that awareness and that brand, but again, keep it light touch, keep it um enjoyable, and keep it so that um people feel like it's only going to take 5 to 10 minutes of their day if that's what they've been asked to do. — Was there anything that you felt like particularly stood out like, oh, that was a this is a really good activity or this particular format worked really well for the people who took the trainings? Um yeah, I've got one really specific thing um that I did um was I set up a um a Tableau escape room um and I sort of I changed it and branded it to uh our own kind of um dashboard look so it looked like something that we'd done. But really this was something that's open to lots of people out there. developed by Mark Bradbornne at Tableau uh many years ago. Um and you know a few people have done similar things and I thought well this would be this would be quite cool if I made this the activity for one of my things um to sort of have this escape room thing that I can sort of disguise as one of our dashboards. And people, you know, right up to the kind of head of um department really um enjoyed that. And uh in a way that sort of set the ball set the uh the bar quite high. You know, when I was asked to do next, I said, "Hey, have you got anything else as good as that escape? " I thought that was — I'm tapped out. That's my best idea. — Yeah. I borrowed heavily on you know, other guys work from that. And uh but it it did sort of remind me that just something that's a little bit quirky or unique or that felt fun or that almost didn't feel like work if you like was kind of the way to go. So like another example of that would be um when I'm sort of introducing uh some of the first AI um literacy training. I would do things like uh a quiz like a cahoot quiz where you know people can just of how fast can they tell what's AI and what's not AI and can they get kind of you know competitive with the other uh with the other people in their teams and can they spot all 20 of the things that are AI not AI. just things that just feel like a little bit um more uh well I'll put air quotes you can't see this unless you could look back more fun uh because you know it's not everyone's

### [20:00](https://www.youtube.com/watch?v=L73IK6CQ3to&t=1200s) Segment 5 (20:00 - 25:00)

idea of fun but I think once people get involved they like to do just something that felt a little bit more um light-hearted. — Yeah. So, it sounds like your focus was really on just making it enjoyable, making it fun and engaging, finding interesting ways to bring people in, disarming them, making them feel more relaxed, and then even enjoying themselves a bit in the learning journey, which I think is great. I think that's amazing. — Yes. Thank you. That was certainly the intention. Yeah. — So, talk to me about the results. So, you say, "Hey, when you look back, you go, oh, this was a moment where I realized things were shifting, things were changing because of what I was doing with my data fluency program. " So, what was that? What did that look like? How did you start to see evidence of something different than what you saw before? — I've always found that one of the hardest questions to answer um because as we know it, it could be hard to uh to quantify. um it's possible to do um surveys and like for example um in some of the AI literacy it's sort of quite easy to say how much do you use it and then sort of take them through perhaps the week of um of awareness week and training and then say how much more do you think you will use it now um and it's you know it's quite easy to see maybe things that have improved or you can sort of see um user usership of dashboards maybe sort of goes up after trainings um I it kind of again I can think of one example where there was uh the the most sort of um senior leader on the account would decided to get people together and um look at some of the dashboards and really try and um identify um what cost savings can we make I want you know I want all to sort of be able to look into this and really curate a data story let's brainstorm on it and let's find something that we can put in front of the client And I I claimed that as a win. I don't know if not, but I just thought that feels really close to what I wanted to talk about, what I tried to talk about in my data fluency and getting people to understand, be more confident in uh in what they read and in how can they convert that into a story and confidently present it back to a client. So um most of and I suppose that was your question in as much as did you notice things. So you try and sort of notice anecdotal things. Um it's it can be difficult because um I'm here you know working remotely in my back garden and we're a diverse team um where you can be talking to people from any one of u any one of the continents uh and in particular the people that I train will be then off working in different teams um speaking to the client. So you kind of have to hear back from these things um uh almost kind of anecdotally. — I do always try and sort of get some feedback and you know see if people can um give examples of data stories that they've been able to curate that perhaps they haven't been able to um prior to training. But um they can be they I don't know they can be quite hard to identify. So, um sometimes you just have to keep trying and just sort of acknowledge that the um the difference that you're making is kind of incremental and slowly but surely. — Mhm. Yeah. — Yeah. Go ahead, Ally. — Yeah. The quantitative and qualitative I feel like that is something that we've always thought about and talked about too. Like there are some things you can measure, but there are going to be a lot of things that it is more of like hearing comments or stories and you're going to have to piece it together like, "Oh, I don't think that person would have been able to do that before this training program. " — Yeah. And it it's hard to, you know, it's hard to justify and it's hard to kind of um to demonstrate, if you like, because I think ideally um anyone in my position that what they would want would be for it to be a continuous process. It's not really a dip in train people on data literacy and dip out again and say my job is done. You know, you you would presumably want to keep working with those same cohort. You would keep wanting to uh work with new people who come in. You would want to um you know uh you would never just sort of want to come in and say, "Right, I've delivered that. That's all that's needed. " Um but that can be a hard thing to demonstrate. — Well, the good thing is technology never changes, right? So we once we learn it all, you just sit there and you're done and you're like, I checked that box. — All the data skills too, like — yeah, the skills are the same. — There we go. Box ticked all down. — So let's talk about going back to the beginning then. So there's some folks out there listening that maybe are thinking to themselves, you know, I've been trying to get a program going at my organization and I want to know what Neil's recommendations are for what I can do today, tomorrow, this week to try to get things moving in that direction. you know, what would you say? — Yeah. Um, well, a lot of what I do and sort of what we started with at JLL was was bite-sized. You can do you

### [25:00](https://www.youtube.com/watch?v=L73IK6CQ3to&t=1500s) Segment 6 (25:00 - 30:00)

was was bite-sized. You can do kind of small things. And I've spoken a lot about the work I did on with my client, but um a number of us sort of across different clients who sort of shared the passion. We started with a uh with a lot of brainstorming you know what can we do in our different roles and we came up with um data moments for example you know what can you do that's just sort of one or two slides at the start of a presentation that you can just sort of sneak in there to get people thinking about data something sort of data literacy themed or something like that. It's how can we get the message, you know, if we can't go out there and present uh and sort of put sort of whole data literacy programs out there, how can we just sort of get the word out? And um we had a bit of success with that and from my point of view, I thought, well, um Hans Rosling, of course, you know, the the legendary um sort of best data storyteller that many of us will have known from his TED talks. um his uh his factfulness books and where he talks about the uh the 10 um uh the 10 data habits that we have. Um uh oh blime me it's it's my age. It's my 30 years in the business. I've completely forgotten what the term is. Um but uh um but anyway, the way that we the dramatic habits where we over dramatize data. So we'll see two data points. Uh so this was the one out of 10. We'll see two data points and all we'll see um is the gap. We'll uh we'll see the drama. We'll think something's gone up, think something's gone down, whereas really all we need to do is get the um get some more points, get some more context, and uh you know, and not jump to so many kind of conclusions. And that's um that's one example. And he had 10 of these dramatic instincts. that's what he called them. Um, and all 10 of them make for just a sort of simple um diagram and picture and um slide at the beginning of let's say a um — you could you know you can put it into the uh the business reports that the leaders will go back and give to the um give to the team members. Um and so I would start to put a data moment at the start of all my trainings. Um, and it might be like that's happening in like minutes minute one and minute two and that's just when everybody is still sort of coming on and getting ready. Um, but it just it it's a nice way of sneaking in kind of data stuff, data awareness. Um, the importance of data literacy being that we're always just thinking about data and how do we use it confidently in the right way. almost like a interstitial message like maybe like I don't know a break slide or a starting slide that's right and for everybody joins — yeah the idea came from the fact that you know we're real estate company and you know there were things in existence already things like a safety moment so it would be quite common to start meetings off with just here's a here's a safety tip here's something that you absolutely need to know we'll just put this in get it top of mind and then we'll go into the rest of the meeting and we thought well why don't we have a data moment Um and you know just so it's again and I suppose it's similar to the uh what I was talking about before about um trying to fit data literacy or fluency training in where the other training exists. It's seeing how important data um uh literacy is as a skill um and putting it on the same level as how important it is um to know the rest of your job and how important it is to know um you know health and safety for example. I'd also give you I'll also give you kudos for a data moment being a better branding than interstitial by the way if you're trying to make things — it yeah exactly it's a little bit catchier I think but — yeah interstitial explains exactly what it is yeah so — Ben you had a data moment um at the beginning when we talked about the Spotify stuff you brought you brought up your own data moment — oh yeah we were having a little aside weren't we about uh something about the what the power law and behavior of human beings and how we focus on — a tiny percentage of right the content creators out there. But it was a fun little aside. — Yeah, we made our own data moment. — That's right. — Yeah, I like those a lot. — Yeah, Neil, I love the idea of just like hey, let's just like insert a little bit of something at the beginning and it's just a tiny little habit and you know you can do a big aggregate number, right? We know bands where you can just like just at the beginning slide 80% of something. That's so easy. It's very easy to do. So anyone can do it. — So you did mention at the beginning or in the middle a little bit about AI um and having the training about AI. — What do you think — has been the main impacts of AI on our data literacy data fluency movement? Do you think AI is going to be really

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impacting it more even further? — Um I mean there's no doubt that it will. Yes. Um, AI is very interesting because um the link between AI and data is so um is so crucial. Those of us in data literacy, data fluency, the it's almost a natural um leap into AI literacy, AI fluency because the the soft elements, the kind of skills are kind of there. From my own point of view, I have a lot less knowledge of AI than I do of data, but I've really enjoyed being able to learn as I developed and as I've been as I delivered, you know, I've been learning as I go. Um, so there's certainly um no doubt that you know AI is going to be vital and I think it's just it's the existence that people will now use AI to do um a lot of their data thinking. you know if people will perhaps go to a dashboard or download something from a table or go you know straight to Excel if that's what they want to do and they will put it into AI you know we talk about um we can talk about whether it's um Tableau or other PI packages going into AI but people can um pretty much bypass that step and do what they want and put it into chat GPT and that's where it becomes so important um obviously I'm preaching to converted here but that's It becomes so important for people to understand just the uh the importance of what they're doing and understanding um you know is what they are doing uh valid um and how can you know how can we help them to get a really good um answer from AI. So it's there and I think those who are most um involved um uh in AI in AI are already seeing this as a great way to um to meet their data analysis needs uh or um processing needs whatever it might be. So, um, I feel like I'm still, you know, flying by the seat of my pants and still sort of trying to catch up and still trying to understand exactly where AI will fit in because, you know, it it's these things are always moving. But I think we have to acknowledge that um it's a it's definitely an extra um component of the toolbox of a of any data consumer's toolbox um is AI. And so we need to make sure that we help people use it uh responsibly and understand exactly what they're doing. Um and we can't bury our head in the sands and think no don't use AI do what we've always been doing because who wouldn't and you know you talk to anyone under the age of 25 now and they just automatically ask AI anything um in you know in life in business in whatever it might be. they just go to chap GPT and get their answer. Uh so yeah, I think we need to just make sure that the data is caught up with that. — Yeah, I mean I agree. I it's always been a tool in the toolbox when we go back to classic machine learning. Uh but the intersection of those two ven those two circles in the ven diagram in the last few years has of course gotten uh pretty heavily overlapping. — And to your point, what is that doing? Well, it's lowering the bar in terms of the skill level you need to ask a question of your data. And so now the question becomes again, you know, to your point, it's so important to know how to do that well and how to avoid it. What limitations exist? How do you verify and validate and what are the steps? Because I think human nature is to be a bit lazy and to just take whatever we get and run with it. And sometimes that's okay and other times we're in situations where it's just really not okay and we have to stop and look closely. And so I think people are needing to teach that and that becomes now a huge imperative within the broader data literacy movement and I think people who have been involved in data literacy for so long like yourself exactly this is a huge learning opportunity for all of for all of us right to lean in to really understand what's going on what is the what is happening under the hood at least conceptually and what does that mean in terms of what we do each and every day as we leverage these tools you know um those important questions. I think organizations have those questions. So, that impact is just going to continue to get bigger and bigger kind of going forward. But, oh, go ahead. — I agree. No, I was just going to um say that I really perhaps I really like your um ven diagram analogy and perhaps that's the visual data person in me um because it is true the um the field in which we need to know and the people that we deal with need to know has grown bigger, but there is a very large overlap. So it it's important that if people are going to um uh use AI, they need to bring those data skills

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into using AI and if they're going to um analyze data, you know, that they bring the AI skills in order to do it. So — Right. — Um it's yeah, it's really just sort of um navigating that which is the real challenge. — Yep. Totally. All right. Hey, so we've been talking almost this whole time in the context of organizations, teams, companies, and so forth. So, you know, a big part of what we're trying to do as well here at our organization is sort of think beyond that, think beyond the four walls of our employers. So, what are your thoughts about where we are in societal levels in terms of our ability collectively to make sense of data to leverage tools? You know, what do you think the effect is on life in general and on the planet? I know that's a big broad brush question, but you know, how would you look around? around you and assess what's going on and how we're doing? — Well, I mean, where do you start? I do think that um that data literacy really sort of came to the four during COVID. And here's me thinking that was only recently. Actually, it was 5 years ago. But, um you know, it became really um important to um understand data. It was it became a time when charts appeared on the the main headline news at 6 p. m. every day delivered alongside you know the national leaders um which as an interesting aside is when I was finally able to explain to my mother what I did for a living you know charts like you see on TV. Oh okay I can do it. So things like that became a lot more mainstream. Um I don't think that necessarily means that everybody um sort of started to understand data and I think I think that I don't know I think the downside is more sort of the downside of uh of society generally and the sort of proliferation of um fake news if you want to call it that or sort of different polarized view on sort of social media. I think as people choose their own um view of events and we all know that they do that um you know algorithms that kind of help people do that if you like um data sort of becomes quite an important part of that people choose the way to frame their data. people um whether they're data literate in enough or not um you know they can in the same way that they can choose their own version of truth it seems now data to kind of back up that truth so I think that's something just to be very um aware of um yeah I don't I don't know how that sort of um answers your question in particular but um I do there is just more data out there um and I think people understands quite how much um data there is driving everything they do whether it's you know their watch or their phone or things like that so I hope that just sort of general data literacy is by osmosis just sort of going up slightly things like that because you know if you want to sort of talk to somebody about data literacy it's very easy to think of um dozens and dozens of examples of um you know data decisions people make um every day of their lives. — Yeah. No. And the important things like pandemics and global health crises like COVID you pointed out down to the light-hearted things we talked about at the beginning of the show when it's like Spotify wrapped right. We've got our data there on — number of listens this past year etc. So there's some big things there's little things but I'm really with you Neil. I think that in the sense that you know we're learning a lot. The tools are getting better, but we're still not really doing a great job collectively using data as a as something that brings us together. I see it more like you do where concerns about how people are using data to drive a wedge further between themselves and other entrenched groups. So, if we're going to really make great use of data in the world going forward, we have to find a way to address that. Um, and I think that's a more human tendency than it is maybe related to the data itself. So, there's some things in the way maybe of us truly seeing collectively as a species on this planet the value of data to solve problems and to bring people together. We've got a ways to go. — Yeah, I agree. And whether that's something that it's possible to do, I I don't know. But um — uh yeah, I think we need to it's something that we need to be trying to do anyhow. of the same. — I think you see pockets of it and I think you're right. Even if maybe we can't collectively solve it universally forever, well that's probably too grandiose of a goal, but we can start to introduce pockets of it. change the dialogue and how we ourselves participate in those kinds of

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conversations that tend to go south. We've all been part of them, right? So, okay, how do we show up differently? How do we take it upon ourselves to use data in a constructive and positive way instead of using it as, you know, just another hammer uh to hit people over the head with? And um if we can start to do that ourselves little by little, maybe that'll accumulate over time. You know, that's I think you're right. That's certainly something that's worth a try. — Yeah. Small positive changes amongst yourselves, your family, your work, your colleagues, your organization, just like you did, Neil. It's so nice to hear about how your expertise and everything that you're trying and experimenting with um in your role. So, I we really appreciate you coming on and being our first guest. — Absolutely. Yeah, really appreciate it, Neil. Thanks so much. — No, thank you. It's been a it's been a pleasure and as I've said, you know, that that's been my role in the past and it's a role I tried to kind of um carve for myself because it was an opportunity that I saw that is something that organizations need and it is a way of you know getting people to work better and every organization probably somewhere in their mantra says we want datadriven decisions. So, you know, if we can sort of um demonstrate how that helps, then um I think that's why um data literacy is so important. And there's so many different ways that you can approach it. — Yep. Thanks so much, Neil. And thank you for tuning in to the data literacy show. If you found this episode helpful, share it with a colleague who is also building a data literacy or data fluency program in their organization. — That's right. And also, thank you to the listeners. you're the ones that we had 85% more listens than the rest of the first time podcast this year. So, we appreciate you. And yeah, uh don't forget to subscribe if you're a first-time listener so you don't miss your episodes. Definitely spread the word. We're really trying to start a movement here and get a movement going. You know, we got a long way to go as we talked about today. I really appreciate the time we had with Neil. You'll find all the show notes on data literacy. com, including where to connect with Neil, how to reach out to him if you have more questions or thoughts. and Neil, we're cheering you on going forward and um and you know uh and we appreciate all the time you spent with us today. — Thank you. Thank you for having me on and thank you uh both just for all the support that you've given me and that you give all the uh the data literacy community. — Thank you. Bye. — Thanks everyone. Bye now.

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*Источник: https://ekstraktznaniy.ru/video/45868*