Episode Transcript
Intro:
Welcome to The SkillsWave Podcast, where we explore the challenges and innovations in corporate learning.
SkillsWave is redefining workforce transformation with its free AI powered upskilling and education benefits platform.
Sasha:
Hello. I am Sasha Thackaberry-Voinovich, president here at SkillsWave. I'm joined today by Delia Van Heerden, our VP of people operations at a company called Fastloop. She is a thought leader whose journey spans continents and industries, from founding a corporate training company in South Africa to championing data-driven cultures in Canada. She brings us a truly global perspective on the power of data literacy. In today's episode, we're diving into why data literacy isn't just for analysis. It's for everyone. We'll explore how organizations can build a culture where informed decision making is truly second nature. So welcome. We are thrilled to have you on The SkillsWave Podcast.
Delia:
Yeah, thanks, Sasha. I'm so excited to be here. It's something I'm really passionate about so it's a wonderful opportunity. And thank you for such a dynamic intro. It was great. Thank you.
Sasha:
Yes. Thank you. It's truly due to our organization is all about working with amazing people. So we're thrilled to have you on as well.
Delia:
Awesome, thank you.
Sasha:
And in that vein, because of the diversity of your background, it really does span corporate training, people operations, data consulting. Can you tell us a little bit about how those experiences have shaped your approach to building learning programs that really drive data literacy in organizations? You tell us a little bit about like that journey?
Delia:
Sure. Of course. So I would say that my background from training to consulting and now people ops, which I'm in actively has given me a really well-rounded perspective on, you know, two things that's related to this this podcast today. One is how people learn and how learning is important for human existence, essentially, and secondly, how people actually interact with data. And so that for me is really so critical to building data literacy and building this data culture that we find ourselves talking about so much these days. So I've really seen firsthand across many years, across different teams of varying experiences and various stages of data maturity. I've seen firsthand how people still fear data. You know, they often assume that it's only for specialists or they're on the other side where they are a specialist, but they have so much access to data that it's overwhelming or they don't actually know what to do with it. And so those are things that are still very real, that's tied into very real human experiences, human emotions, not just the technical that comes with data itself.
I would say I've essentially started my career as many technical trainers do, you know, in traditional corporate training, teaching what people need to know, but then through consulting and landing in the data space in this new, very multicultural country with a brand new team where people have varying experience and skills and me, myself needing to start over and starting to learn about data, which at the time I knew very little about it. I realized very, very quickly that literacy, you know, true literacy itself was not just about the ability to write and communicate in some kind of shared language, but it was very much also the ability to change and influence how people think and how they make decisions and how you apply that within a shared context. You know, rather than these isolated situations that we often find.
So fine, do I believe that my ability to influence change through the building of that shared understanding is really the core that creates my training approach and the building of programs, really focusing on the differences and how we blend them together to create something new and dynamic and relevant to everyone.
Sasha:
Yes, that relevancy piece I think is so critical. I had actually I had been talking with a fellow a few months ago, and I was presenting what I thought was a very compelling, data-driven presentation, and some of the feedback that I got was, well, you have all the data, all the numbers, but the presentation was sort of bloodless. And to be fair, I did ask for the feedback, wasn’t just unsolicited. So how do you how do you help people understand the data story? Like how. Okay, so the data, like it's one thing to understand what the data is saying, but communicating that data and what it means for the actual business can also really be a challenge. So can you tell us a little bit, maybe about how you think about telling the data story in a way that informs business operations?
Delia:
Yes, that's a very, very good question. I would say that when it comes to data storing and data storytelling or data communication and data literacy, people will often say that the story is about bridging the gap between technical departments and business departments. But to me, you know, going back to that multicultural scenario that we spoke about, it's not at all about bridging the gap. The story is about blending the narrative, blending the business objectives or the different departmental objectives, and embedding that into your everyday workflows, or embedding that into the narrative that you want to tell and the success that the data is trying to share. So by blending that, you're making it easily accessible and relevant to a bunch of people rather than isolating it to a specific use case. And so really trying to understand, you know, what is the objective of the story, what is the success story that you're trying to tell? And, you know, two hours from now, two weeks from now and focusing from that point on, the narrative and the topics of the conversation that's going to drive that data from there and that.
Sasha:
Yes, very, very interesting.
And one of the, challenges, I think, from an L&D perspective is how do you actually teach that? Like how do you how do you teach this, how do you make a culture where this blending can occur? Because skills are needed, right, to be able to do that. So how do you approach, teaching people how to come in like to that data literacy mindset, but then blending that with the story of what needs to happen to inform the business?
Delia:
Right. So I think one of the biggest things to do that is to really have everyone on the team acknowledge that data is everywhere today. We literally cannot escape it. And the biggest mistakes that organizations and L&D teams make is that we just assume data literacy exists, or we take it for granted because we have access to dashboards and we have AI in our pockets these days.
But just because we have it doesn't mean that everybody knows how to actually best utilize it. And in 2025, you know, at the rate that technology is evolving and that data is being produced by the minute, like I don't even want to imagine, I can't even begin to comprehend how much data we're probably generating as a society, per minute across the globe. You know, at the rate that that is being produced, a data culture is more important today than ever before. And for data literacy to thrive, for data learning and data opportunities to really take shape, there has to be agreement in an organization that we all have a shared responsibility towards our data narrative and the data culture that we're going to create.
We need to be aware of what we're consuming, what we're putting out there, how we're going to use it so that our time and our investments into data is intentional, not just, you know, some kind of tick box that we have to tick to say that we've done it. So it's really not about just, you know, getting the dashboard or teaching people the tools or the how, like I mentioned earlier, but really integrating what used to be a technical skill set now into what is actually a core competency and very much just a, a mindset, a business mindset that we need to have today. And so I think once people see that as a mindset shift rather than a technical training lane, that's really when the ball changes and when the game gets really, really exciting.
Sasha:
And, and that that sort of mindset that you were talking about, like a data mindset. I think that dovetails so well with an innovation mindset or a leadership mindset because we have so many biases. I mean, not just even social biases, but really, mental habits, right, that constrain how we think about things.
Delia:
Yeah.
Sasha:
And in order to see the data for what it actually is, not just confirming what we think we already know, right, like confirmation bias is very real.
Delia:
Yeah.
Sasha:
It requires thinking differently about how we open ourselves up to actually seeing the data. And that requires in some cases it requires different type of leadership approach. Right. How do people or how should people sort of lead through this? Because especially with everything going on, the minute by minute changes you're talking about, even with the development of AI and like, how do leaders begin to guide through this shift? I mean, it's like change management on steroids, right?
Delia:
Hundred percent. Yes. I'm so glad that you made that analogy to change management, because that is what building a data culture is. It's literally recreating what you knew or what you thought you knew, and getting a holistic organizational buy-in to a new North Star that we all now need to suddenly understand.
And yes, you hit the nail on the head. Leadership is everything in that. And I kind of want to take this in two possible ways in that, you know, us, all of us in in learning and development, we play a very active role in leading this and creating this curiosity around data, which I think a lot of companies still think of as is just the, you know, the training providers. But no, like we are also the nurturers of this culture. We have to stay on top of our leaders to make sure that they are not just advocating for what we stand for or what we're trying to do, but actually leading by example. So I think anyone who's worked in training or is in an organization with very active leadership will know and hopefully agree that our leaders are our greatest teachers and that means it can go great or it could go absolutely wrong, right? Like it could really go either way with us.
And so we have to stay on top of our leaders to make sure that they themselves are data literate, that they are leading by example, by being the first people to pull up the dashboard and not just say, oh, well, what does the data tell us? But then saying, you know, why is this important? Why do we need to action on this? Or why are we not going to action on this? Why are we going to do further research and not just go down this, this road, you know, blindly because the data says so? So it really does require active participation from leaders in our organizations to be vulnerable, to also interact with the data, to share with the team, you know, maybe where they're struggling with data or where it's really challenging them and in reverse to, you know, show the team how they've overcome their own hiccups or biases through the use of data. And so being vulnerable with your team and really leading by example, you know, even if you think you're going to fail, like doing that with your teams is so important to nurturing and creating ongoing data literacy, to create data to culture that we're talking about.
Sasha:
So the culture piece—this is so fascinating to me because I think that, you know, the idea of a business, sort of a business structure and a leadership structure is so different now because expertise looks different now. I often tell my teams that there is no such thing as individual expertise anymore. It's all collective. It's how we do it together. But the data moves fast. It's not just about the dashboard. It's letting the dashboard inform your operations and trying something and being wrong. You know.
Delia:
Yeah.
Sasha:
Like a culture that is allowed to be wrong frequently. Like there shouldn't just be a plan B, right? There should be a plan C and a plan C point 2 and a plan D, like otherwise you're not listening, right? So like market forces are constantly adjusting. You have to constantly adjust to them. And that's a habit of mind. And leading a culture into a habit of mind is, I think, a super challenging thing to do, particularly around something where people are, you know, data can be intimidating, right?
Delia:
Yeah. Correct.
Sasha:
So but when you're approaching sort of data literacy and thinking about that leadership, like what, how have you approached that from an ROI perspective? Like if you're trying to increase data literacy, how do you measure that or the impact of training that you do? How do you measure the impact of that training? And, and really, even just embed having training around this in an ongoing way.
Delia:
Right. So ROI in this situation is such a big can of worms when it comes to training on its own. When you look at ROI in training, in terms of data literacy, it's a completely different ballgame.
So I, you know, allow me to waffle a little bit here. It will make sense as we get to it. But, you know, I feel like we know as training providers and being in the learning and development side of the world, that training is often one of the first things to go. People want to make budget cuts, training is gone. And, you know, we know that that's hard because it's the worst thing you should be doing in a time of distress. But I like to remind people in this situation that ROI is not just the return on the investment, but often should rather be looked at, what is the risk of ignoring training in this situation? And so with data literacy, specifically in the world that we're in today, organizations are spending, you know, hundreds of thousands of dollars, if not multi-millions of dollars in creating data infrastructure. You know, they're moving data to the cloud, they're creating data lakes and data warehouses. They're investing time and money into data ingestion, data modeling, you know, visualization as an output. You're spending so much money on investing in your company's infrastructure and architecture to support data. You know, leaders need to think of the return on investment or the training as insurance against that infrastructure you've just put in place. Right? So if you're not training your staff, if you're not encouraging your organization to be data literate, to be excited about the infrastructure you've just put in place, to use it and drive it forward and to keep maintaining it and, you know, increasing its value in that regard, then you've wasted all this money on the infrastructure to begin with. So while the return of investment is really, really important, the risk of ignoring is really even more so.
And so if anyone listening today is not yet, you know, prioritizing data learning with data literacy or they're thinking of doing some data training as an afterthought after the infrastructure has been built, then you know you're making a mistake. Pause, reevaluate. Think of data training and tooling implementation as something that goes hand in hand. These are parallel projects that need to happen for there to be adoption and success.
But I know that that's not actually what you asked, you know? So to roll back to your actual question, you know, what are the key metrics that we should perhaps be tracing or tracking to prove that there is a return on investment for training? I would say that that really differs from company to company. It's going to differ on the objective of why you invested in all of this training or these data solutions to begin with. But, you know, like your typical business measurements like decision quality, efficiency, collaboration, business impact, those things still exist. I want to encourage people, though, you know, like metrics are only really successful and impactful if you're measuring the impact both before and after. And so there really—
Sasha:
yes, benchmarking
Delia:
—has to be a strategy here. I often work with clients, and they want to pull metrics out of thin air at the end. And you go like, well, you know, you can’t. What was the point? What were you trying to achieve? Because if we're not comparing it to the start, what's the point of starting now, you know, at the end to prove that it was successful? So we really do want to look and understand if employees are making better data-driven decisions, once they've completed the training. We want to see if, you know, teams are working smarter instead of harder with the new tools that are put in place, like are departments working more cross-collaboratively? Have we broken down those individual silos and do we now see an organization where they are sharing data assets, they're sharing data knowledge, they're working together and pulling each other up rather than, you know, holding on to their data success? You know, and is that new data-driven mindset improving revenue? Is it impacting how you treat your clients? Is it reducing waste? You know, like depending on the organization and what they do that data impact has to form part of the data strategy to measure from beginning to end.
Before I forget, I think something that's also great for learning and development ourselves as we take on these data initiatives or we start looking at building data literacy programs, is also not forgetting, you know, the idea of a net promoter score internally. Like, if we're building these programs, we want to make sure that people who are attending are leaving excited that they want to promote this course, that they want to go out and share and encourage other people to join and to spread the, you know, the opportunities through the training. So not forgetting that net promoter score, I would say is very important because if engagement is low, we're getting rated low, nobody wants to pass on this information or they saw it as a waste of time, then the training initiative is going to fall flat. So as soon as those net promoter scores keep coming in to immediately jump and evolve, iterate, fix, improve and just keep going.
Sasha:
Yes, yes, that term perpetual beta. Right?
Delia:
Exactly.
Sasha:
Always taking feedback, always tweaking, always making it better. Yes.
Delia:
Exactly. One hundred percent.
Sasha:
And yeah, well thank you so much for talking to me today. We went a little awry from the original topic so very excited about it. I don't want to say awry, I want to say expanded. I could have this conversation for like five hours, very interesting to me.
Delia:
Yeah, I could, too—days.
Sasha:
Yeah, yeah. Data. I mean, we could get some serious data geek on, but thank you for talking to us about how to make teams and organizations more data literate and in a very transformational time period for many of our businesses. It has just been a pleasure having you on The SkillsWave Podcast.
Delia:
Thank you. Thank you so much for the opportunity. I hope to come back again and unpack this further.
Sasha:
Absolutely many more conversations.
Delia:
Awesome. Thank you so much, Sasha.
Outro:
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