#135 Building the Case for Data Literacy

Hi, I'm Maggie, VP of Curriculum at datacamp, and I know firsthand how overwhelming it can be to keep up with the rapid changes in data and AI. But staying ahead doesn't have to be hard. That's why our curriculum team has curated the best learning content to help you and your team stay ahead, covering everything from data and AI literacy for busy executives to cutting edge generative AI for developers, and everything in between. Whether you're upskilling yourself or your team, we make learning practical, engaging and flexible. It just works. Power your data and AI transformation journey on datacamp.com.

This capability and comfort with data of the workforce isn't going to develop organically or it would have already societally and organizationally. So if you ignore it and you don't apply some direct interventions and programs and systemic ways to foster data literacy, it ain't gonna happen on its own.

Hello, welcome to Dataframed. I'm Adele, Data evangelist and educator at DataCamp and Data. Framed is a weekly podcast in which we explore how individuals and organizations can succeed with data. Today we're speaking to Valerie Logan, CEO of the Data Lodge. For those who are not aware, Valerie is one of the pioneers of data literacy and is in fact one the of of the main people who popularized the term in her time at Gartner. I think everyone tuning in today who's ever ran a data literacy program or is thinking about running a data literacy program or initiative can attest that getting that executive buy in making the business case for data literacy is extremely hard. Whether it is securing funding, understanding what are the measurable outcomes of data literacy program, what a pilot project looks like, finding relevant partners. It's really uncharted territory for anyone engaged in organizational transformation and Valerie works with tons of folks in this space to help them navigate their data literacy journey. So I wanted to have a deeper conversation with Valerie on how to build that business case. Throughout the episode we speak about what a successful data literacy journey looks like, from securing buy in to rolling out a pilot program and scaling that program. We also discussed best practices for evangelizing learning programs, what ROI looks like, how to avoid siloed efforts in departments working on data literacy, and more. If you enjoyed this episode, make sure to subscribe to the show. And now, onto today's episode. Valerie, it's great to have you on.

So glad to be back. We had a great conversation last time.

100%. Last time was on the webinar, now it's on Dataframe. I'm very excited to be with you here today Maybe before we go into the meat of today's chat, just to introduce you to the Data Frame audience, I've heard quite a few times you actually coined the term data literacy during your time at Gartner. We had Jordan Murrow on the Data Frame. While he popularizes the term quite a lot, he mentioned to us that you coined the term. So maybe walk us through that story and where the inspiration for the term came from.

So coined the term I didn't necessarily like. I mean, if you Google search, you can prove that I didn't like forever coin the term. But I would say that I popularized the term with the work that I was doing at Gartner as a Gartner analyst. And there's nothing like the megaphone and platform that you get from being a Gartner analyst to bring things to light. So I will own that, that I popularized it. Yeah. And the inspiration came from I was working as a Gartner analyst, as an advisor to chief data officers. And in looking at the context of that work and the strategy work, so much of it came down to culture. And so the inspiration really came from a career and a life of often being called on to be a translator between business folk, data it folk and quants, because I'm a quant by training and the literacy. And I did coin information as a second language. I'll take that. That part. I did. But the inspiration is all about communication and connection and translation.

And, you know, we're definitely going to talk a lot about data literacy today. You know, when we had you on the webinar earlier in the year, we talked about the state of data literacy, why organizations should invest in it, how to invest in it a lot more. I highly recommend to the audience to check that webinar. I'm going to link it in the show notes. Incredibly great conversation. And I think that conversation left a lot of openings for us to talk about. Right. To discuss in more detail. So what I want to do really today is focus on that change management of data literacy. A lot of leaders that we speak to struggle with the changing hearts and minds aspect of data literacy, both in securing executive sponsorship buy in, but also actually getting buy in from the learners and the workforce that they speak to. So just like data literacy is a journey, I want to structure our conversation as a journey from getting buy in to building a pilot program, scaling the program, measuring the impact. So maybe at the beginning to set the stage, why do you think organizations still struggle with prioritizing data literacy and data culture?

So I like that we're going on this journey. I like that approach. I think there's still struggle because we're talking about human beings. We're talking about like just helping people see things differently and behave differently and be open to learning and things like that. So I think, I think it's because, number one, we're talking about human change. Number two, I think there's still some struggle around how to even get your arms around what are we solving for, what do we mean by culture, what do we mean by literacy? And I think a lot of folks are just how do you even start with that? How do you even begin to tackle that? And then I always say it starts with defining the term data literacy. And I know people think, oh, are we really going to go there? But you have to be really intentional about what you're solving for. I think it all starts with just human change. And where do you even begin with that?

100%. Let's definitely tackle that and try to answer some of these questions and provide maybe a guidance for the audience here. You mentioned here defining data literacy. Maybe walk us through your definition of data literacy and how do you think that definition should be adapted for a organization and its specific use cases.

For me, data literacy is very personal. I think you have to look at it through an individual lens first. I've boiled it down now. I mean, definition I use is the ability to read, write and communicate with data in context in both work and life. So the in context is it's not a one size fits all proposition. So from the beginning, make sure we're looking at it individually. It is work and life. So if you approach this only as a work thing, it's going to fall flat. And then the mindset, language and skills. Mindset is what are people's attitudes and beliefs and are they even open and curious to this? Language is the terms we use business terms, data terms, analytical and then skills are basically how do we think, how do we engage with others and how do we take action? So that's my framing of it. I know I went through that really fast, but I think we covered it previously. But where do you want to go with that, with that definition? How does that resonate for you? Like, how does that land for you?

For me, I love the in context because it's really important to personalize that data literacy journey. And I think part of that personalization connects to. What I want to speak to you about here is getting executive buy in. Right? Let's say I'm a data or learning leader. I know that data literacy is extremely important for my organization. I'm facing this resistance in securing executive buy in for data upskilling in my organization. I'm trying to secure it and I want to be able to tell that personal story that why data literacy and data upskilling is really important. What do you think is a great formula or approach for securing executive buy in on data upskilling programs?

This is a big question right now. Even those who already have buy in for the program are really leaning into this question of how do you engage the executives or the senior leaders of an organization. So I think I'm glad we're starting here. I think it's really important. So I think the first part of that is, is executive buy in. Let's assume we're talking first about who's going to sponsor this, who's going to sponsor the data literacy initiative or program. And typically I see that coming out of the data office, the person who is responsible and accountable for modernizing the platform and created trusted data. I think first you start with who cares about it. And at the end of the day I'd like to say data literacy is an insurance policy for the rest of those investments. So organizations are spending many millions of dollars on moving data to the cloud and modernizing the tools and self service and cleaning data. Well, if people aren't interested or engaged and caring about that, it's going to be hard to get the value. So I think sponsorship first typically lands in the data office, but in direct partnership, typically with the learning officer or the talent team. So once you get through that, let's assume you have sponsorship and the right alignment on the program. Then I think it's how do you get the attention of the other senior leaders and executives? Because at the end of the day they can either be reinforcing of this or distracting from the data literacy work. So I think the first thing is do they again anyone, do they even understand and does it resonate what we're trying to solve for? So the best way to do that with any senior leader or any leader is what do they care about and why does this matter to it? So what are the strategic objectives of the organization or of the function or division that someone is a leader of? And at the end of the day, how does data literacy relate to that if they're driven by sales or efficiency or quality or customer experience? Absolutely. Look at it through that lens and say who is really affecting those drivers and how does the data literacy of those individuals matter? So for example, if you have someone involved in data entry related to inventory, how does the data literacy of that individual matter? Do they know they affect the data quality that affects inventory control? Or someone on the front line with a customer service experience? Are they aware of the short list of recommendations and metrics that are on their screen? Do they understand where that comes from? So I think you go right to what matters to the leader and how does it relate to data literacy of those people involved? I got one other thought, let me pause there before I, before I continue. Is that making sense?

That makes a lot of sense, Valerie. And one thing as well, kind of connecting back to our webinar where we talked about it last time as well, is that a lot of the times there is a promise when anyone tries to secure executive buy in for a project of any kind, whether that's a new tool, a new skill, new hires. Right. There's always a promise involved there that if we do X, Y will happen. If we don't do X, Z will happen. You mentioned leaders today in such an environment, they're always thinking about roi, Right. Return on investment of certain projects. You mentioned another type of roi. You want to maybe expand on that as well?

Yeah. The other ROI is risk of ignoring. So this capability and comfort with data of the workforce isn't going to develop organically or it would have already societally and organizationally. So if you ignore it and you don't apply some direct interventions and programs and systemic ways to foster data literacy, it ain't going to happen on its own. So I think there's a risk of ignoring and you need some deliberate attention to it. And that starts with how the leaders are modeling strong data literacy.

That's really great. And you mentioned here that there's an additional thing that you wanted to touch upon.

Yeah, that's the thing. And then I'll come back to the if, if X, then Y. I think that's a really clear way to say that. But I guess the thing I want to, I want to lean into a little bit with leaders is leaders are some of the strongest teachers of the organization, good or bad. And so when leaders are saying one thing, like endorsing a program like data literacy, that's great, you want them to be advocates with allocating resources and identifying ambassadors and providing program support and encouraging training. That's all advocacy work, which is kind of half of how I think leaders can affect this kind of program. But the other part is modeling their own strong data literacy. So one of the ways that I'm helping organizations to coach their leaders and support their leaders is how do we be really explicit with the leaders to say, hey, can you intentionally model strong data literacy? And so what I mean by that is if you have a leader who's always going into a meeting with the 40 page PowerPoint deck and turn to page 27 and we'll reference this, that is different than a leader who will pull up a live dashboard or a visualization in the meeting and say, hey, let's talk through this. The leader, he or she may know the definition of a term, a business term that's being used in a business discussion. But the leader can also say, I'm not really sure I know how we define that and just put it out to the group and model that it's okay to admit you don't know something or to ask the question, are we clear on what we mean by this? I think that's just so powerful. Adele. Or a leader can say, you know what? We use that term, AI ethics. What do we really mean by that? I just think it's so powerful. What do you think?

I completely agree. The ability of leaders to model that data literacy behavior is incredibly important. And then you mentioned that part of a leader being able to say, I don't know and being comfortable with that and showcasing that behavior. There's a form of data humility that is extremely important in data skepticism, of not taking data immediately at face value. Being able to look at a particular data set or a metric and being able to, even if it tells a great story for that particular team that the leader belongs to, being able to look at it and question it and say, is that data correct? Is that telling the full picture? That is an incredible way to build a data culture, a culture of skepticism, of healthy skepticism with data, and a culture that uses data correctly and thinks about it. How do you view that as well? Well, in context of data literacy for.

Leaders, yeah, you're actually hitting on something that so often I hear data literacy associated with better decision making, which I get that that is a huge outcome. We want people to be more comfortable and confident decision makers based on stronger data literacy. But what you're picking up on is we also want people to be more comfortable and confident communicators about the business with data. So being able to even converse. And I love this term data humility. And you said, like people say, well, is the data? Is the data clean? Is the data trusted? I mean, we can talk about to what degree is data good enough or do we have enough data? It doesn't always have to Be this like perfection of pure clean data, then we have all of it. It's like in some cases having directional enough data, you don't have a choice. That's what you have. So anyway, I think being able to have conversations with data and about data is just as valuable as decision making with data.

Completely agree and kind of. We talk about improved decision making, but there's also that component of clearer decision making. Like you have better parameters to be able to discuss a particular decision. How do you make it? Your paradigm is much more clear even though the data is unnecessarily fully trustworthy. But directionally, at least you're going in the right place.

It's kind of like approaching this almost as a scientist. Like so many times leaders are just, they've gotten to where they are in their careers because they have a kind of a natural thought process and a natural analytical process. But to be able to lean into that and say, hey, can you take me through your logic of that? How did you get to that? It's so powerful and such a gift, right? To be like, well, first I think of this, then I think of this, then I ask for this and then someone can go, well, have you thought about using this data? And it stops them in their tracks. So yeah, I just think it's powerful.

Completely agree. And also models a great behavior that you want in a data driven or data literate organization. So we discussed the executive buy in side of things. Let's discuss maybe what happens after that. Our conversation is a journey. So let's go on that journey. What we've seen is an extremely common next step after securing executive buy in is usually a pilot program where the learning organization, the data leadership organization, will do a small learning program aimed at a small population within the organization. Maybe to start us off. What do you think are great hallmarks of an effective pilot program?

First of all, having one, I think that's a good start. I mean, starting somewhere. Secondly, I think going with the friendlies. I use that phrase of go where there's interest. Don't try to push a pilot program just because people want to do a pilot in sales. If sales isn't really engaged and there's not somebody that's a welcoming party, go where there's a welcoming party. Maybe stating the obvious there. I think a good pilot is something where you can really isolate the before and after. So being able to articulate, all right, what is our current state, what are the explicit pain points and what are we aiming at? And then being able to see it Behaviorally and from an outcome perspective on the other side. And then also I think a pilot that isn't a topic that's somewhat explainable to the rest of the organization. So if you have something so remote and abstract that people can't really relate to it, it's not necessarily a good story to share. So something that's relatable.

Okay, that's really great. And you mentioned here that welcoming party. Right. What do you think are characteristics of that welcoming party? What are their behaviors that leaders can pick up on that would make them be like, okay, this is the target population for the pilot program?

Yeah. I mean, I think if they're modeling characteristics that you're looking for. Like if they start by saying, oh, what are you, what are you, what are you doing with that data literacy work? Hey, here's some things that I'm observing. Do you think this could help with that? So I always look for who's asking the good questions. Right. I mean, that's always a good sign. I think the other thing is if they have an explicit business objective that people care about and that is visible, I think that's another way. And I would say if they're willing to commit the time, if they're carving out time, that's a good sign.

One common pitfall you mentioned here, the business goals. Right. Being a great signal for the pilot program population. One common pitfall that we see organizations often fall through when they're creating, whether massive upskilling programs or relatively smaller ones like a pilot program, is that they really focused on skill based outcomes rather than business goals or transformational outcomes. I'll give you an example. A skill based outcome would be we want to upskill people on data visualization, whereas a transformational outcome would be we want to enable people to make faster decisions on X business processes by leveraging these data visualizations. How do you think leaders who are creating data literacy programs can ensure that their learning objectives are much more integrated with the organizational goals and the business goals?

Yeah, I mean, I forget who it is that came up with the five whys, but that's a great way to dig in and be like, okay, I just think it's that conversation you have about what is it that you're really striving to achieve on the end and just meet people wherever they are. If they're like, hey, we want to upscale people, they're not leveraging the tool yet. We know that it could be better be like, well, why? Why do you want to do that? Well, because then they're Going to be more comfort. And they're going to use the dashboard. Okay, why? Well, when they use the dashboard, then they have a conversation with the data in the dashboard and they get their own answers. Okay, well, why does that matter? Well, then they don't go ask our data scientists to create them a custom report.

Okay.

I mean, that's one line of logic is just the efficiency side. The other is, I think, really being able to identify, like, what is a before and after scenario of what great looks like. And asking them, hey, fast forward from here, two weeks from now, at the end of this, what do you want to see? And asking those probing questions to find out where their mental space is at and be like, all right, well, that's good, but can we aim a little higher?

I love how you use that why framework as well. To be able to tease out metrics, to measure over time in the pilot program, to be able to communicate the effectiveness of that. Because that ties in perfectly to my next question, which is, what do you think are great metrics that organizations and learning leaders can look at when measuring the impact of a data literacy program?

Well, that's the question of the day. Right? I get that almost every single day, ironically. I just. We had our Data Lodge community call, and I asked them in the beginning of this year, I said, all right, community of data literacy programs around the world, what are the hot topics you want to cover? Number one was the relationship between governance and literacy. Number two was metrics and roi. Number three is how to get executive buy in. So we're hitting kind of, kind of all of them. But the point on the metrics, first of all, you have to again, be really explicit what you're solving for. But what we do is we look at metrics. The number one metric that came out of the discussion is net promoter score. So when you are rolling out a data literacy program, you're involved in these pilots, you're engaging ambassadors of the organization. Are they willing to speak on behalf of the program? Are they willing to advocate and tell other people, hey, this is worth the time. This is making a difference. So that wasn't on my radar. I heard it from my community this week, and they were like, you know what? Multiple people in that community, in our community are using that promoter score of data literacy programs now. So that's number one from that. I think when you look at a data literacy program, not just being about training, but being about engagement, community enhancement, communications, craft, leadership, engagement, there are specific metrics there. So I think you need to look at it at a program level. So what are the engagement metrics, what are the development metrics, what are the enablement metrics? So I'll give you an example, one of each. So engagement metrics would be are people, what is the net promoter score around? People signing up for workshops and advocating for workshops. If this is a two hour workshop, was it a good use of your time or engagement statistics around you're rolling out a newsletter and a blog and quick hit video cheat sheets. Are people accessing them? How long are they staying on? So those are some good ones. On the development side, these are the assessments, the courses, the learning paths. There are some pretty classic metrics there on pre post as well as are people staying in the learning path? Are they completing the learning path, are they applying the objective? And then on the engagement or enablement side it's are they using the resources? Are they showing up at office hours? What is their pre post survey coming in and out of office hours? Are they using the data catalog? Are they leveraging the dictionary? Are they using the local coaches? So all of that to say, I think it comes down to metrics associated with the areas of the program, not just this big lofty data literacy program metric.

Yeah, I love how multifaceted your approach is here. When looking at the data literacy program, you mentioned something that is extremely important for me at least is the communication side of things. Right. Looking at metrics related to the communication side of things and ties in as well to what I want to talk to you about here, which is phase three of a data literacy program. Right after the pilot program is successful, you've been able to see that transformational outcome. You want to scale it to a wider population within the organization. Communications is extremely important. Maybe walk us through our effective models for communication when scaling a data upscaling program.

Scaling to me again, you're scaling on engagement and communications, you're scaling on the development, you're scaling on the enablement. When you're scaling communications to me, there has to be, you have to be looking at that both grassroots as well as top down. So grassroots is are you creating a groundswell where you are all of a sudden picking up more people that are signing up as data literacy ambassadors? That's not like something they're being told to do either. They're opting in and they want to be part of it. They're completing their digital bad. They are volunteering to run workshops and be recorded as subject matter expert videos. So I think the communications, a lot of it is are you engaging the Groundswell movement and is that actually taking on a life of its own in our community call this week, I was starting to hear examples of how you run these initial workshops where you engage people and you invite new ambassadors in. And I was hearing a story from the team over at Mayo Clinic where they were saying they love hearing, like we teach different mnemonics about the language of in different techniques and tools in the workshops. And they say it's a great success when they hear about those mnemonics and those tools and those techniques from people that they never trained. So when they start seeing and hearing the narrative, the ideas, the techniques, the coaching tips, when they start hearing them from people that they had no interaction with, they know it's spreading. So I'd say that's the bottom up. Adele. I think top down is, are you starting to see the leaders embedding this more intentionally into their ways of working and their ways of showing up? Are they making sure that onboarding is including 10 minutes on data literacy? Are they in their all hands meeting, doing a shout out to the data literacy work? So I think communications wise, first you look for the groundswell evidence and then you also look for is it becoming more evident in the executive stuff?

I love that, especially when you talk about the word of mouth. What's really nice about the data literacy program, I think, is that it forces a lot of the stakeholders that are involved with the data literacy program to really think like marketers. Word of mouth is something marketers really think about oftentimes. Right. There's a lot of these small kind of marketing tactics that we've seen. Data can, for business customers, adopt internal podcasts, for example, to talk about data upskilling programs. What do you think are innovative programs that you've seen some of the Data Lodge customers adopt when it comes to communication?

I think first, I love that idea of representing it as marketing. And the first one that comes to mind is branding. So how are you branding this program? Because the big fear factor here is this is going to be viewed as yet another corporate change program coming from the ivory tower. That is like the antithesis of what we want. Right. So I think engaging different ambassadors to come up with what is the brand, what is the logo, what's the tagline, what is the identity of the program? So I think program branding is really, really key. I think the other is, how are you infusing this into the flow of the organization? Right. How are you actually embedding it and having it pop up and relevant at the moment that it's needed, not necessarily as like an item on an agenda or a separate meeting. So I think seeing this really emerge in the flow. Business and the branding are probably the two biggest elements of kind of the marketing.

I couldn't agree more. And then let's move beyond the marketing. Let's talk about also the administering actually of the learning program. I think the main challenges when it comes to scaling a program beyond a pilot project or a pilot population is that you want to keep that personalization of the learning in the pilot program that you saw, but also building it out to a much wider organization, different departments, different functions, and ensuring that DLC is not siloed within a few departments. And you want to infuse that and decentralize the data literacy effort. Maybe starting off with the first challenge, what do you think are some of the most effective ways to tailor data literacy to the specific needs of different departments, different units? How do you create that groundswell of personalization?

First, I think you do have to have a clear foundation from which the organization is building upon. So I clarified the whole mindset, language skills, framing, but it goes deeper in that. So I think you really have to have a clear like Data Literacy 101 foundation that is understood and aligned before you can get some contextualization that becomes powerful. So when I say a foundation, what I mean is. So at the Data Lodge, I've put together the 20 data literacy essentials. So if you get that kind of consistency around what are the data literacy essentials, and you have the course and the learning paths related to that, let's call that the 100 series stuff that that spans functions and spans divisions. From that, then you can start layering in 200 level work that is contextual to a division or a domain, and I like to refer to those as the dialects of data literacy in the language. So what that could look like is instead of a general data literacy workshop that's about the essentials, you start saying, all right, let's start digging into use cases, or digging into bias and ethics, or digging into data storytelling that is very specific to the business problems and the opportunities within a division or a business domain. Then you can start layering in things, all right, like where's the catalog and the glossary related to that dialect of the organization? And who are the 200 level subject matter experts that really know this dialect? But that only takes hold if you've got a good basis for this is what we mean by data literacy. Here's the foundations. Now we can grow. Is that what you meant Is that what you're asking for, Odell?

Yeah, 100%. Like how are you able to personalize beyond that foundational level to the different departments? You mentioned something earlier in our conversation, something like a data ambassador program. And this kind of reminds me of how do you approach the different learning modalities when you're scaling a data upskilling program? Of course there's online learning, there's kind of instructor led learning, doing workshops, creating an ambassadorship program. Maybe walk me through that community aspect. Right. For example, having local coaches, data ambassadors, how does that help out the personalization?

So communities take a few different forms as it relates to data literacy. So I think the most natural, and I'm not saying we have to create brand new communities, I'm saying look at the communities you already have naturally and maximize the heck out of those. So what I mean is a lot of organizations have an analytics center of excellence or a user group around different BI or visualization tools. These people in those communities are already local coaches. They're already trying to bridge this gap on literacy. They don't just necessarily have a common toolkit or support or resources to do that consistently. So first off, maximize those community channels you already have and start arming them as being data literacy ambassadors. Give them the toolkit, the mnemonics, the consistent 20 essentials. So they are all operating in a consistent way. That's first layer, community. The second is how do you actually connect with other change agents in the organization who are operating on different change agendas. What I mean by that is the talent development, the future of work, the people officer, the learning and development teams, they are all out there operating on a broader agenda, bigger than data. So they're out there with the digital transformation. Right. They're having to do how are we help people understand RPA and agile and cloud and cybersecurity. So you gotta weave into that community too and go, hey, let's bring the data literacy piece to that messaging because that's a whole other web of community advocates.

Yeah, I completely agree because then it ties into the overall organizational strategy which we talked about as well in terms of aligning the upskilling strategy as well. Now you mentioned one thing, is that the community leaders, when it comes to tooling and data skills that are specific to the analytics center of excellence, maybe walk me through, what is the role of the data expert in being able to help the organization upskill and create that higher level of literacy when it comes to data. Are they supposed to be able to lead workshops? What are maybe the modalities by which they share their knowledge.

I like to bring this back to the way that I teach the language of data, which again I call it information is the second language. There's a little mnemonic, it's a triangle. We can all align around a triangle and the triangle is called the via model. So any use case of data can be explained with three sets of terms, business value, information and analysis. The communities align with those parts of the triangle. So your experts, your data governance, data stewardship experts, any, your data engineers, they know the I portion of the via model the best. Your quants, your developers, your AI modelers, your business intelligence experts, your analytics center of excellence, they know the A. I'm not saying they don't know the other pieces, but they are the experts in the A. Your business stakeholders, your people that are business process management, business analysts, business leaders, they know the V, the business value the most. So when we're looking at community, what we're doing is we're really bridging these three natural sets of communities and helping them to share a common language around use cases. So I'm glad you brought that up because that's where the experts fall. But what we're trying to do is bring them together into a common language.

I couldn't agree more. And that's the power of data literacy here, creating that common language within the organization. So Valerie, we talked about getting executive buy in, we talked about setting up a pilot program, we talked about scaling the program. And I feel like we need to talk about what's next. Right? Data literacy is a nascent space. You mentioned this in our webinar that we're at year three of a ten year wave. I want to know from you, what do you think the next seven years are going to look like? What are emerging trends that you're excited about? Where do you think we'll be in a few years?

I think the one that's really top of mind for me right now is the education sector and how through I'm seeing there's significant movements underway in the K through 12 sector. Here in the US universities have been doing some work with Harvard center for Education Policy. How is data literacy manifesting in data informed educators? I'm seeing in the US there was recent legislation about the Data Science and Literacy act being passed. So I am just seeing more pervasively this is becoming a societal thing, data literacy, not just a workforce development program. So I would expect to really see a lot of that convergence happening. What will happen as a result of that? Is there has to be some standards and some consistency across this landscape. It's still pretty fragmented. There's a lot of questions on what do we call this? Is it literacy, is it fluency? And I think we have to start seeing some forcing functions there. So I think education consistency across the domain and then I think there's going to be. I think it's going to take the form of what Six Sigma did. I think it's going to get consistent. We're going to see professional development certifications, we're going to see competition and talent models around this entire domain.

That's very exciting. One thing definitely on the education space, something we've been following as well. It's very good to see movement, but we're definitely excited to see how it will play out. Right. Like consistency is extremely needed not only in the data literacy space, but also in data science in general. If you look at the tooling space in data science and contrast it to software engineering, you'll also see quite the fragmentation in comparison to where we are today. So, Valerie, as always, it's a pleasure to speak to you on all things data literacy, data culture, data science. Any final call to action before we wrap up today?

Final call to action is whoever you are, whether you are a leader, whether you are an enthusiast, whether you are a data scientist, someone is doing something in this area and you need to join forces. So if there's a data literacy program started and you're not part of it, offer a hand, get involved. If you're struggling to get started, please connect with me on LinkedIn. I'm more than happy to share some ideas of how to get started. On that last point that we just had, Adele, about where this whole topic is going, I focused on the human side. I think the other side is let's keep an eye on the automation and the technology. I think there's a whole bunch going on. ChatGPT is absolutely getting people curious about what's going to happen with data literacy. I think there's a lot to watch there too, 100%.

Maybe another episode. Talk about the data fear component and the AI fear component. Yeah, I couldn't agree more. Valerie, as always, it's a great pleasure speaking with you.

Thanks for having me, Adele. Appreciate it.