#122 How Organizations Can Bridge the Data Literacy Gap (With Dr Selena FIsk)

You're listening to Data Framed, a podcast by Data Camp. In this show, you'll hear all the latest trends and insights in data science. Whether you're just getting started in your data career or you're a data leader looking to scale data driven decisions in your organization, join us for in depth discussions with data and analytics leaders at the forefront of the data revolution. Let's dive right in.

Hello everyone. This is Adele, data science educator and evangelist at Data Camp. Something we talk about a lot on Data Framed is the importance of data literacy and data skills and how they help both individuals and organizations succeed with data. Oftentimes when organizations engage in upskilling programs on data literacy, one of the common pushbacks people have is I am not a numbers person. So how do you move past that? How can leaders help their people bridge the data literacy gap and in turn create a data culture? Here to answer these questions is Dr. Selena Fiske. Selena Fiske is a data storyteller and advisor, passionate about helping others utilize data to lead positive change. She believes in a world where individuals and organizations are data informed, not data driven, which we'll talk about today. Fisk is enthusiastic about building data storytelling skills and others, and has mentored leaders and many others positively impact organizations they work in. With a background in teaching, she has developed resources for data storytelling in schools, including an online course and two published books. In April 2022, she also released the third book, I'm Not a Numbers Person, how to Make Good Decisions in a Data Rich World. Throughout the episode, we spoke about the distinction between data informed and data driven, the different levels of data literacy, the importance of change management, how leaders can approach upskilling, how data storytelling is a key pillar of a data culture, and more. If you enjoyed this episode, make sure to let us know. And now onto today's episode. Selena, it's great to have you on the show.

Thanks so much for having me.

I'm excited to talk to you about your work on data storytelling and data literacy, how to democratize data skills and data insights, and much more. But before, can you give us a bit of a background about yourself and what got you into the data space?

Yeah, absolutely. So I say that I'm a data storyteller. I'll say from the get go that I'm not a data scientist, but I kind of sit in this really nice space in between the people who use data a lot in the organization and the data scientists and everybody else. And I guess the way I got into this work was I actually used to be a teacher So I used to be a physical education and maths teacher actually, and I did that for about 16 years. And in the schools that I was working in, I started to do some roles in student data and performance. And some of the problems that schools were facing was around. There was so much data and people didn't necessarily know how to use it to actually impact, you know, in our sense, teaching and learning. And I wrote a couple of books and I started consulting in the schools only space. And then I guess as I kind of started on the journey of being self employed and working as a data storyteller, I started to realize that it wasn't just schools that were facing these challenges. And actually the problems and the challenges in education are exactly the same as the challenges and problems that people are facing in other industries. So I wrote my third book, which is the one that you're most familiar with, which is not a school's book. It's available and it's useful, hopefully for anybody in any field. And that kind of taps into that idea of how do we actually tell better data stories and how do we tap into the power of data so that we're using it in a way that really benefits us, our teams, our clients, the people we aim to serve, so that, you know, we can actually put the data to work rather than just having a whole lot sitting there that's not really doing much.

That's very exciting and I'm very excited to unpack with you one your experiences consulting with schools on how they can transform their workflows using data, but also discussing in general how organizations should approach data transformation and skills transformation in general. So I'd love to first set the stage for our chat by trying to understand your definition of data literacy and why it's important and how you think organizations should think about it. But maybe to answer that question, let's first talk about what data literacy is not and what you think are common misconceptions about it.

Yeah, so for me, I talk about the goal of getting to data storytelling involves a couple of key pieces, and the first part of that is data literacy. So for me, when we talk about data literacy, it's an understanding of what each value, each metric, each number actually means. Because we can't expect people to use the data that we've got access to if they don't actually understand the metrics to begin with. And what I'm seeing across organizations is that sometimes there's an assumption that people understand the numbers, and oftentimes they actually don't. And part of that problem is caused by the fact that there is so many more data sets being added to organizations. There are so many more tech tools that collect different sets of information. And oftentimes organizations haven't had a chance to kind of zoom out a little bit and think about, well, what data actually matters to us, and therefore, how do we build capacity in our employees so that they actually understand the numbers. So for me, there's a quote that I really like by a fellow called Charles ceif. He wrote a book called how you're being fooled by the numbers. And in his book, he said, nobody cares about the number five. And it's not until we put a context around numbers that it actually means something. And I see that all the time. I worked with a team the other day, and I said to them, right, well, what are all the different data sets that you've got access to in your role that you could potentially use? And they came up with 46 different types of information. And we can't expect people to use and act on 46 types of information. But from a data literacy perspective, it's important that we kind of recognize that every single one of those metrics has a different level of understanding around it. So a number five in a percentage profit means something really different to five being the number of people in terms of the turnover, or the number of people who are leaving the organization, or of the percentage variance between actual cost and budgeted cost. So the number might be the same, but it's that context that sits around the number that people don't necessarily understand, but is a really key part of data literacy. In your question, you kind of said, what's a common misconception about data literacy? And I think it's interesting that some people almost use the term or phrase data literacy as almost like something that encompasses and brings everything relating to data in together. And some people even talk about data literacy being the same as data storytelling. I don't think it's that simple. I think it's actually a key piece of the puzzle. But the storytelling part comes later. So when we build people's understanding about the metrics, that's the number one thing that we need to do before we can start any further conversations.

That's really great. I completely agree with you that, you know, shared context around important metrics and data for the organization is a really great first step to create that common data language within the organization. Something adjacent to that that you chat about quite a lot in your writing and research is the distinction between data informed and data Driven. A lot of organ today claim that they want to become data driven. Walk us through that distinction in more detail and why you think data driven may not be the most useful term to describe what organizations should aspire to.

Yeah, you're right. There are a lot of organizations that aspire to be or proudly say that they are data driven organizations. And I don't kind of want to rain on their parade because I respect some of the really awesome things that are happening in organizations that are using data really well. What I would like to do is add to the conversation and I guess that's why I make the distinction between being data informed and data driven driven, and why I believe that we should be advocating for being informed by the data and not driven by it. I often use the analogy of a horse race. So in Australia, horse racing is a key part of our culture. And often trainers with horses that are running in races, if they want to stop them being distracted by the other horses and the riders, the jockeys, they put blinkers on horses. These blinkers sit over their eyes and they're almost like a bit of a shield that sit around the horse's eyes. And the idea is that if they're kind of neck and neck with a horse beside them, the blinker like shields them a bit from what's happening on either side. So the idea is that if the horse is racing towards the finish line and their aim is to win, they are more focused on that goal rather than taking in the context. I use that as an analogy for being driven by data because when I've seen teams and people and organizations be driven by it, that's almost the way that I've seen them operate. I've seen them focus on a goal pretty ruthlessly and relentlessly and, you know, they understand kind of what's going on around them, but they don't actually take any of that into consideration when they're doing their decision making. So for me, it's about being informed by the data. So it's taking those blinkers off and it's absolutely using the quantitative information that we've got and we use that information to influence and inform our decision making. But at the same time, into that decision making, we bring our context, our experience, our professional kind of understanding of what perhaps the market is doing, what the strengths and the dynamic of the team that we're working with. Yeah, it could be economy, it could be weather, it could be geography, depending on what field you're working in and when we can bring all of that understanding and our professional knowledge and experience into the conversations about the data, then we're informed by it, not driven by it. The other, I think important distinction to make between those two things. It's rare that data actually tells us what to do. So when we look at data, I don't believe that we can say that we're driven by it because the metrics aren't actually telling us what our actions are. It's the human element and the conversations and the decision making that then happen after we consume the data that then decides what actions we take. So in that respect as well, I believe that we should aim to be informed rather than driven from an organizational perspective. I also believe that it's a really hard sell if you've got people on your team or people that you work with who are a little bit freaked out by numbers and they're a little bit scared of them and they're worried about how they'll be used. I reckon it's a really hard sell to get those people on board. If you start saying, okay, well, we're going to be data driven in the way that we use numbers in our organization. I think for people who are fearful of the metrics themselves in the first place, I think that just makes it a little harder for them to kind of see the value in it. Whereas if we can even articulate as leaders or as people within our own sphere of influence, if we can articulate that difference and the intention and the desire to be informed, then I reckon we're more likely to bring other people along on the journey with us.

That's really great. And I love how you make the case on different levels here, most importantly on the human level to be able to create that excitement, enthusiasm to join on the data transformation and skills transformation effort. And I love how you also clearly make the case for one subject matter expertise and also business context when using data to make decisions. Probably a great example here would be during COVID for example, toilet paper predictions and forecasts, especially during the beginning, you couldn't necessarily trust the data and you had to rely on the outer context economy, what's going on around you to make a decision. If you're working, let's say an FMCG company, would you say that's correct?

Yeah, absolutely.

So something related to the different levels of understanding that you mentioned here and that's present in your book and one thing that I love about your book, I'm not a numbers person, apart from, from the great title, is that it really draws a mental model for how individuals, organizations can Think about the different levels of data literacy or use of data and the impact that this use generates. I would love if you can outline the different levels of data literacy and how they play out in organizations today.

Yeah, absolutely. So I often talk about people being in one of six stages, and that's in the amount of data or evidence that they use in their work. And what we know and what research tells us is that as people start to become more data informed and use evidence in their decision making, the greater evidence informed impact they can have on their clients, their teams, their organization, more broadly. So I kind of talk about six levels, and people at the lowest level are really unconscious users of data. And so I don't tend to work with a whole lot of people that are in the unconscious category because to be honest, they're not the people who are calling me to ask me to come in into their team or organization to speak. But there are some people who are still a little bit, for whatever reason, and there's absolutely no judgment. But there are some people who are a bit head in the sand. You know, I would say my dad's like that. He still goes to the bank with his little deposit book each week and takes out cash from the bank. He's probably unconscious in terms of the amount of data and different technology that's out there. And I think that analogy is similar to the people in our organizations who just don't see that data is a part of their role and it's part of their job description. And in the past, that was kind of okay because we have it. We have data scientists. And you know, even in the last decade, that responsibility for using data has definitely fallen on those people for whom numbers come really easily. And they're really confident with numbers. So it's kind of okay for there to be unconscious users of data in organizations, but we certainly need to move people out of that space now. So what we get from unconscious, the second level is conscious user, or it's just that people have, I guess, a general awareness and understanding that it's a bit of a thing. There is data in the organization that maybe people talk about it, but they haven't yet bought into it. And they don't necessarily understand it or know what it is. They're just aware that there's something out there outside of their peripheral vision that they don't necessarily know much about at this stage. The third level is a superficial understanding. And people who have a superficial understanding more than just know that something's kind of happening. They start to build A really kind of base level of understanding of maybe some superficial or some really big metrics. So often I talk about zooming in and out of the data. So the further we zoom out, the bigger, more global results we get. So if you think about, I'm in a hotel room at the moment and a big metric that hotels often use is like heads on beds or percentage occupancy. So a superficial user of data might be able to engage in a couple of those really big metrics, but they don't have a really in depth understanding of maybe multiple measures or they may not understand the things that contribute to making up that large metric, or they may not have the skills or capacity to maybe generate it themselves, but they're certainly on the path they've started. The next one is more of an awareness around the way in which data might be able to be used in their work. So people who can start to see a connection between the metrics or the different data that exists in their team or organization and then how that actually applies to them. So they're starting to make and build connections between that information. It's not just the big global heads on beds type metrics, but it's things that maybe actually are connected to their sphere of influence and things that they can maybe start to do something about. And they're building again that understanding of their data literacy. They're building perhaps the tools that they've got access to and they're becoming, or they use of data is becoming a little bit more frequent. They're starting to kind of keep an eye on these, these things because they know that they can control those metrics or do something about them. The fifth step is then action. So this is kind of where people step from being quite passive users of the information to being more active. So rather than just keeping an eye on the metrics that are relevant and that apply to them in their role, they start to try things out and take action based on the information that they're getting. So they might, for example, look at sales in a particular geographical region and then think, okay, well we're going to launch a marketing campaign in that region and hopefully our sales will improve. So they're starting to take action based on the data that they've got. That's level five people to then elevate to a level six, which is reflection, are doing the action piece. But they're also reflecting on their impact. They're trying things out and they're thinking, okay, well did it work? Did it not work? How can I tweak it or how can I kind of make it better the next time around? People who are in that stage of reflection, they're almost in like a constant action research cycle. It's constantly thinking, what little adjustments can I make? Where can I get additional information about the work, about the team, about the company, whatever it might be, and how can I kind of improve things? Just do those 1 or 2% things that will hopefully lead to improvements down the track. So I see people in all of those six spaces when I work with teams and no matter where people are, it's absolutely okay. And as I said right at the beginning, there's no judgment at all. I would like to think that it's a helpful frame for people to think about where they are individually. But also, you know, if you're frustrated by a member of your team who is not a huge data user, hopefully in that description, you may have been able to identify maybe where they sit. And then it's important to be thinking, well, how do we then start to build capacity and move them and move ourselves up that continuum? How do we get to that point of reflection? So we're just constantly thinking about the data and doing something with it so that hopefully it has a positive impact.

That's really great and I love how holistic your answer is. It really covers the entire spectrum of the data literacy skill set. I think this also marks a great segue to discuss how to move between the levels of data literacy and how organizations can start building capacity to address these, this movement between the different stages of data maturity. I would love if you can break down what you think are the main components of becoming a numbers person, of moving alongside that spectrum of data skills.

It's a hard one to be completely honest. And I know that while we're talking about data, it's about the data, but it's not about the data. We're talking about leading significant organizational change and behavior change. And that's a really hard thing to be trying to do. Some of the research coming out of the U.S. tom Davenport writes pretty heavily on this in the U.S. and he has estimated in his work with organizations that for teams to go or organizations to go from not using data to be fully kind of data and evidence informed, it's like a two to three year process. So it very much is a 24 to 36 month journey for teams to be on. And I guess that's important to recognize because I often get invited in for a keynote or I, you know, I do a one off masterclass for a team or an organization which is lovely and I really enjoy that kind of work. And at the same time, when I book it, I'm really conscious of saying to them like, this is not necessarily going to create long term cultural change. This is something that a couple of hours once off doesn't fix. It doesn't bridge the gap. So even as somebody who has influence in this space in your organization, I'd encourage you to think about how can you start to embed the upskilling regularly into a calendar in your organization over the next two to three years incrementally. What does that look like? What are the little pieces that people are doing along the way? How are we going back and refreshing and revising? And we know that we can't just show people something once and that they'll remember forever. It doesn't work like that. So even things like the launch of a new dashboard, like if as a team you create a beautiful new dashboard that kind of helps people with their work, you want to be really deliberate and explicit about going back to that dashboard, say in a meeting, maybe even leading like a really directed inquiry as to what the information is, what it's telling them, kind of facilitating a conversation. And so I'm kind of going from the really big picture two to three years down to like almost the month by month. But as a team, as an organization, we need to be thinking, well, what are we doing? And coming up, it's almost like strategic planning in this space over the next two to three years. Because if we don't do that, that we're not actually going to be able to create that really significant cultural and behavioral change that we're after.

That's really great. I love how you focus on the iterative nature of it and how it's super long term and it's not just a one off project. Now I want to focus maybe on just like one segment of the data literacy spectrum that you mentioned. I think a lot of people tend to be part of the first couple of data literacy maturity stages. Like for example, you mentioned here, the unconscious use of data as organizations, how do we focus on getting people out of that unconscious stage and moving them along the data maturity spectrum.

There's another model in my book that I use where I talk about the amount of evidence people are using and then the actions that they're taking. And for people who are at that unconscious or conscious level, or even at the superficial level, we don't want to encourage them to take action. That's not the first thing that we need to approach them with. We need to be thinking about how to build that data literacy. So really kind of deliberate professional learning for people around those key data sets. I kind of mentioned before, sometimes there's some real assumptions around people's understanding of the different metrics that we've got and what we want them to use. I was with a team the other day and there was a gentleman talking about the importance for him in his role. He's a project manager, and he was talking about the importance of percentage variance between the project estimated cost and the actual cost. And so what he was trying to do was reduce the variance down to zero because he wants to get his estimations of costs as close to actual as possible. And it was an interesting exercise because while he was writing this in, he was typing it into a shared document and other people in the team were firing questions at him and it became really obvious that they didn't understand what percentage variance truly meant. So they were saying to him, they were saying things like, well, aren't you just trying to increase your revenue? Aren't you trying to reduce expenses? And those two things are really important and provide really useful inputs into the costing. But he was trying to say, well, no, I'm actually not focusing on expenses and revenue at this point. I'm just trying to get my, my predictions right. And so what became really evident was that the team didn't really understand a metric that he talks about all the time, and he even struggled to articulate it. And so I think for some people in that room, they had a really unconscious understanding or conscious understanding of that particular metric. So having time and space, like even having opportunities to have data chat chats, and having an open forum where you're talking about different metrics that you use and how they're relevant is an important way of starting the conversation. At the same time, though, there's also, prior to that, there's value in thinking about, well, if we've got people who are not really on this data continuum or they're in a low level, we don't want to overwhelm them with all of the different data sets that are available. And as a team, we need to be thinking, well, how do we simplify this for them and how do we just focus on the things that are most important? So it's almost like a matter of, as an organization, prioritising the data sets that matter the most to you and then literally starting with training and it might be a lunch and learn. It could be in a kind of team meeting, whatever. It Might be. But how do you start conversations about those most important metrics and then almost work your way through that list?

That's really great. And we covered here that group of people who are early in the spectrum. I would love if you can also cover how to move alongside the data maturity or the literacy spectrum and more generally. So I would love if you can break down maybe the different skill sets of becoming a numbers person in more detail. What do you think are the main highlights organizations or individuals know about and maybe walk us through examples of this skill set in action?

Yeah. So obviously, understanding the numbers is the key one. It's the first thing. So I talk about data literacy being a key part, and as I've just mentioned, a key part of that is actually working out what matters to you, your teams, your people, and your whole organization. You can't possibly use an act on every single data set that's available to you. So the first thing is very much as a team, as a leadership team, whatever it might be, actually working out what matters to us and then building people's capacity and data literacy around those different sets. The second one then, for me, I talk about. So data literacy is kind of the first big circle in my model, and then the second big chunky one is data visualizations. When we look at data visualizations, they're incredibly powerful because they reduce the cognitive load, they reduce the thinking time, and they make the trends in the data more easily identifiable. And we can see them far easier than if we were to, say, have a spreadsheet with just black text on white backgrounds. So the difference between a visualization and an Excel spreadsheet, if you had just say, a spreadsheet, if I had a whole stack of data in it, as I said, black text on white background, you would have to be thinking about every value, what it means. You would have to be looking on the spreadsheet up and down, kind of what does that value mean compared to these ones on either side? You'd be thinking about what's happening left and right on the spreadsheet. And again, you'd be really kind of caught up in this really deep thinking around, what am I trying to actually identify in terms of the trends here? What am I seeing? And then, to be honest, you're probably more likely to miss things, particularly the bigger and bigger the data set gets. So when we visualize data, you know, it's. The whole picture is worth a thousand words. We can get, you know, millions of data points into a visualization, and there are some visualizations that People use and organizations use all the time. So heat mapping, where cells in a table or a spreadsheet are color coded based on their values. That's a really good way of helping people be able to see the trends. Obviously, we see line graphs and bar charts quite often, even in, say, the media, on the news, on socials, we see kind of those graphs quite commonly and quite often. But there are other visualizations that we again, possibly sometimes assume people know how to read and interpret. But I guess my experience would indicate that that's probably not fair to assume that everybody understands them. And those ones specifically are things like, like a box and whisker plot. Sometimes people use that to show the spread of results. A waterfall chart is one that is where there are two values, and the waterfall chart just highlights the difference between the values. Or even things like scatter plots and quadrant models, all of them are useful and they have different value and use at different times. But it's important, like, if we're trying to build numbers, people and people who can engage in these conversations, even just a functional level, as well as building their understanding of the numbers, we actually also need to build their understanding of the visualizations that they've got. So there's a couple of dashboards that I regularly use with a couple of companies here in Australia, and they've got box and whisker plots in them. And without fail, every single time I open up one of those dashboards, I say to the team, do you just want me to talk you through what a box and whisker plot is and what I notice and what I see? And every single time I've had them say, you know, yes, please, can you do that? Because it's not something that's commonly used, but yet we've got dashboards producing reports with some of these visualizations in them. So that's a really important part of this. The third part for me, I then talk about. So data literacy being the first part, the visualizations is the second part, and then data storytelling is the third and final part of this puzzle for me. And when we think about the data literacy and data visualization pieces, neither of those two things have an impact on the organization. So we can have brilliant data literacy and we can have the best tech solutions and the best visualizations. But unless we're using those two things, nothing is changing. And that's obviously where we want to get to the point of data storytelling. So there's kind of two parts to data storytelling that I focus on. I focus on the actions that come out of data storytelling. And the first one for me is being able to identify trends and identify insights in the data data. So again, we can't make assumptions that people know how to do that. Gary Klein, the psychologist who wrote a whole book actually on insights, says that insights are actually something that shifts our perspective. So when we see a piece of data that confirms something we already know, he kind of says that's like intuition, it's just confirmation of a pattern. But an insight are those things or the pieces of data or the trends that make it, it's go, oh, okay, I didn't expect that. Or you know, we think that something's going really well and then one quarter we see a value just completely drop off the cliff and then it's that like light bulb moment of oh gosh, this is different to what I expected. So again, we as people who are numbers people, potentially the way we can support other people is to help them work out what actually is an insight. What is an insight to you? Because we also know that what I find insightful won't be the same as what you find insightful. And that applies to everybody in your team. But what does that actually look like? So again, I often spend time with teams where I talk it through. Basically it's actually a teaching strategy. It's called think aloud. And I often do a think aloud, so I'll show a visualization and I will talk through the trends and the things that I see to model some of these that for other people. So when we've done that and when people are able to identify and establish the trends that they've got, we then want them thinking about, well, what do we do about that? And I mentioned earlier, you know, none of the data, none of the insights actually tells us what to do. It's just a prompt to get us thinking about what our possible actions are. So from the trends, we then need to be able to facilitate conversations with people. And again, the actions that I come up with for one particular insight won't be the exact same as somebody else on my team. Team. So just having the time and space to really talk about, well, this is an insight that I've seen and these are some possible actions or next steps that I could take. So yeah, that was a really quite a long answer. But obviously there is so much to this and this is why, as I mentioned, it's kind of a long process to build people's capacity and their almost their automaticity and being able to do these things for themselves, because it is kind of some Significant behavior change and upskilling that we're trying to do.

No, definitely a long answer is welcome here. I think this is a very important conversation, and that needs to be really hashed out in details. You mentioned here at the end, is that kind of the objective behind all of these skill sets? Panacea is becoming like an organization of data storytellers. I would love to understand how you view the importance of building a data storytelling culture when bridging the data literacy gap for organization.

Yeah, look, I mean, data storytelling for me. So I've been doing work in this space now for about 13 years, and I've always talked about the actions from data. And I only actually came across the phrase data storytelling about five years ago. And it was a real light bulb moment for me because I was trying to articulate how we bridge numbers, humans and actions, and bring them all together. And data storytelling really does that for me. And I think that when we are trying to motivate and bring other people along on this journey, we absolutely need to be tapping into the data storytelling because we know that inherently as humans, where we come from oral language traditions, we know that we don't motivate many people. There's only a handful of people in the world or a small proportion of people that get motivated by numbers. We motivate people by connecting to the purpose and the why and the humans and the people that are being impacted by the decisions that they're making and by the actions that they're taking. So, absolutely. I mean, and that's why I say I'm a data storyteller. I reckon we should all be advocating for that. For me, data storytelling as well. I think there's two parts to it. So Brent Dykes in the US has done some awesome work in effective data storytelling, and he's written a book on that, which I'd really highly recommend. He talks about there being three key parts to a good data story and their data, the narrative and visuals. And that's all very important. And there's a lot of work around how do we actually get up in front of people and tell a good data story at the same time? And that's. While that is super important, at the same time, I believe that there's a whole lot of data storytelling that's happening in our head. So as we're sitting there as a member of a team looking at a data set, trying to work out what's going on or whether we're sitting by ourselves, that data storytelling, bringing the narrative, the data and the visuals Together, that's all data storytelling. We may not be saying the data story out loud, but those connections that we're making between the narrative and the numbers, that is just as impactful, you know, if we're kind of doing it in our own heads versus speaking it out loud to other people. So, yeah, that's absolutely the goal.

That's really great. And definitely I echo the sentiment about Brent. Brent has been on the podcast. Very insightful when it comes to all things data culture and data storytelling. I think this also marks a great segue to kind of discuss the practicalities of how organizations can start bridging the gap. You know, one thing, I think a theme that has popped up throughout the episodes, that there's quite a lot of data hesitancy and data fear within the workforce of many organizations, within individuals. Maybe starting off, where do you think this data fear and data hesitancy comes from?

I think there's a lot of reasons. I think when we grow up, when we're little kids, I think really early on, even the influence from our parents, we establish an identity as a really little kid about whether or not we're good at maths. And I think that's really problematic when we get to adulthood and we have to work in organizations where maybe we went through university and didn't think that numbers were a thing. And now we're in a role where we have to actually be able to understand, engage with numbers and data in some kind of way. So as little kids, when we start school, I was really lucky. Both of my parents were really numbers people. And so they actually used to make me do maths questions at home after school, which was a bit of a grind. But I'm grateful for it now because what it meant was that I was learning from a really young age that I actually could tackle maths problems and I could deal with numbers and they weren't scary. And I had the scaffolding and support, support to build my skill in that area. And then when I went to school, like all of us, we kind of fit into a bit of a stream or we become known as either that person's good at maths or they're not good at maths, or they're good at English. And so I know for me, I went through school being the math science person, not the English humanities person. And that kind of, I think, is a key challenge because even when I finished my degree, so I was trained in physical education and mathematics. And when I graduated from my university, there was 120 physical education graduates, and there were four maths graduates. And I think that gap only continues to widen from when we start school and where either we feel we're good at maths or not, that gap just continues to widen as we age. And as I say, and then people go down a pathway thinking that maths is not a strength and that they won't necessarily need it. And then we're now in 2022, end of 2022, and obviously we're saying, well, actually, data is. Is more than just the role of the numbers people. So, yeah, it's pretty problematic. Staying in those lanes in schools also makes it really tough. So being in maths and then English as separate subject areas is problematic because we actually, when we do data storytelling, well, we're actually tapping into and harnessing the skills in both of those streams. And yeah, I think, as I say, you're generally one or the other growing up.

That's really great. I love that. Especially, you know, I'm not a math person. I know a lot of people in my life who claim that they're not math people. As an educator, how do you think schools should bridge the gap when it comes to creating the sort of confidence in the future's workforce and being able to work with numbers effectively?

It's such a good question. And as you say, there are just so many people who don't identify as numbers people. And while the title of my book is Tongue in Cheek, I'm not a numbers person. I named it that because that's actually what people say to me all the time. They're like, selena, I get it. I get that I need to use numbers, but I'm actually not a numbers person. So what do I do do? And yeah, look, I think schools have absolutely got a place to play in that, but I also think home life does as well. As I say, for me, my family were a key part of me building an identity of being okay with numbers. And so had that not happened, who knows what my schooling would have looked like. And I think that's true and relevant for everybody, really, in saying that. I think schools, I don't know what it's like in other parts of the world, but in Australia at the moment, moment they're saying that in some secondary schools, only about 30 to 40% of maths teachers are actually trained maths teachers. And so what that means is that you've got people whose potentially their passion is in other teaching areas and they've picked up some maths classes almost to fill their timetable or because, you know, they need more teachers and they haven't filled the teaching loads. So I have no doubt that has a knock on effect of how much kids then buy into data and maths and numbers if they don't have people that are really super passionate about it to begin with. As a maths teacher, I know and I taught a lot of 15 year old boys maths over my 16 years of teaching. You know, it is that relentless. We've actually just got to back people and when they don't believe they can do something, we need to be telling them that we believe that they can and be almost their biggest advocate and cheerleader and trying to remind them that for any of us at any stage of our life, our intelligence is not fixed. We can always get better at things. Yes, sometimes we pick things up quicker in some areas than others and that's just human nature. But it isn't as simple as saying, well, I can't do data, so therefore I'm pulling the pin and I'm never going to engage in this. We know about neuroplasticity. We know that our brains can grow and evolve. And so within schools or not, I think that that's a really healthy conversation to be having with people. And also the fact that, you know what, this is possibly a challenge and that's okay and let's get through it together. They're kind of the ways. I reckon as leaders we can lead this conversation as well with our people.

That's really great. I love how you think about, you know, education really empowering future workforces and art learners today and students today to really feel empowered when thinking about their math and number skills. Now Selena, as we wrap up, I'd be remiss not to ask you where you think the data literacy and data storytelling space is headed into 2020. So at the time of the recording, we're nearly one month away from the start of the new year. So with that, what do you think are the main trends to expect in 2023 in this space?

It's only expanding. Right. Which is exciting for me. I think people are absolutely recognizing that data is more than numbers. And if we are to use numbers, well, then we need to bridge that gap between the human element and engage humans in the process. And one of the things I always talk about is, is most of the data that we use in our organizations has been generated by humans. It's humans that are doing the analysis and looking at the data and having these conversations. And therefore it's got to be the humans who benefit from it. So I'd love to think that in 2023 and beyond that becomes clearer to leaders, particularly especially when they're articulating their hopes and trying to build capacity in other people. Another thing for me is I often talk about the challenge with or the perception. And this kind of comes back to that fear question that you asked earlier. One of the other that I often see is that data is not necessarily used for celebration. So people kind of look at or they think of data as oh goodness, this is a stick. You know, there's going to be gaps, there's going to be holes, I'm going to get in trouble. Like it's a real kind of negative deficit model of looking at it. But at the same time, for all of the data we've got, there's actually a whole lot of really awesome things that we can see emerge from the data. So we can see the trends that, that are going up and things that are improving and we can see kind of achievements. And everywhere I go people say to me that is not celebrated or recognized at all or very minimally, if at all. So I'd certainly like to think that in the next couple of years framing the use of data as both the deficit and gaps kind of conversations. But also let's actually look for the really good stuff and reasons to celebrate. I'd like to think that that is far bigger part of these conversations moving forward. Obviously there's a whole stack around like AI and predictive technologies which I think is really exciting for me. The technology is only as good as the human buy in. So we certainly need to get the data storytelling piece right to be able to tap into that really cool tech that's on its way.

That's really great. I couldn't agree more. Now Selena, as we end today's episode, do you have any final call to action for our listeners before we wrap up? Wrap up.

If you wanted to read any more, like I have a couple of articles and stuff sitting on my website that you can just download at any stage. So if you did want to have a conversation with members of your team, there's plenty of stuff there that you could just grab the PDF and even just facilitate a conversation in a team meeting about, you know, maybe the data that matters the most to them or how they want it to be used or where they see kind of the hurdles. But yeah, I'd like to think that hopefully there's something there that you can take back to your team and have a bit of a chat with them. You if about.

Awesome. Thank you so much, Sanana, for coming on the show.

All right, thanks so much for having me. You've been listening to Data Framed, a podcast by Data Camp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment, and share episodes you love. That helps us keep delivering insights into all things Data. Thanks for listening. Until Till next time.