Trish Uhl is Principal at Owls Ledge and founder of the Talent and Learning Analytics Leadership Forum. Trish is a globally experienced strategist and consultant leading award-winning teams in leveraging data science, artificial intelligence, emerging technology, analytics to deliver people impact and business value through workplace learning, change and project management. She works with clients globally building their Learning & Talent Development teams' analytical capabilities and digital fluency, transforming them from design/delivery of instructional outputs to delivering organizational outcomes. She’s also a fantastic communicator.
Trish Uhl is Principal at Owls Ledge and founder of the Talent and Learning Analytics Leadership Forum. Trish is a globally experienced strategist and consultant leading award-winning teams in leveraging data science, artificial intelligence, emerging technology, analytics to deliver people impact and business value through workplace learning, change and project management. She works with clients globally building their Learning & Talent Development teams' analytical capabilities and digital fluency, transforming them from design/delivery of instructional outputs to delivering organizational outcomes. She’s also a fantastic communicator.
Welcome to the stellar labs podcast. Future learning today at Stellar labs, our mission is to bust the technology skills crunch with effective, measurable, engaging training. We consult on design and deliver the technical and people skills and competencies you need in business. In these podcasts, you'll hear from industry experts and practitioners from the worlds of technology and training. They'll share their experience, insights, and inspiration and their visions for the future with you. Keep listening to start your future learning here today. Hello, I'm Stella Collins, chief learning officer at Stellar labs. Today's podcast episode was recorded live at the learning technologies conference 2020. Trish Uhl is an expert on data science, analytics and AI with the added bonus of huge enthusiasm and communication skills so she can make the most complex topics available to newbies like me. Listen to her explain data analytics using a horse-riding metaphor. It worked for me. So Trish, I'm really interested in what you do because I actually don't think I know exactly what you do. So tell me a little bit about what you're interested in and perhaps you can help us explore that.
Trish:Well, that's fabulous Stella. So, Hey, so thank you so very much for having me on the podcast today. This was really exciting to finally get an opportunity to meet each other here in London. I'm excited to hear what it is that you're working on with Stella labs. But what we're working on at Owls Ledge actually has to do with three different pillars. One has to do with data and analytics. The second is with emerging technologies. And the third is with the future of learning at work. And in my mind and in my practice, all three of those things are integrated together. It's really one big new way of working for anybody who's developing talents within an organization in order to be able to facilitate organizational agility and to help develop an adaptive workforce.
Stella:Okay. So probably if I tell you a bit about what we do, you can apply that to what we do.
Trish:I think that's a fun conversation.
Stella:So at Stellar Labs our mission is to fill the skills gap that's coming up with industry for, you know, racing ahead and all these new technologies that are emerging and new roles people are going to have. And you know, the world is just changing so much and we know that people are going to need to upskill more regularly. They're going to have to learn new skills. So our mission is to make that happen. We're working within the technology sector but we're very much using the brain-friendly methods, the neuroscience, the science based, evidence based learning that we know works, that we're researching it further. We're using that to apply to technology training. Cause traditionally technology training has been very much; an expert stands at the front, throws a lot of PowerPoints at you and then kind of hopes you're going to get it. So we are using technology. We're definitely looking at the future of learning and we really want what we're doing to be very work focused, work-based stuff. So people leave with the skills to go into a new role, or continue in the role they're in but just be better. So I guess there's quite a lot of links with what you're talking about and we're definitely going to be using data analytics to measure the learning of the people we are training in numerous different ways and I've got all sorts of possibly wacky ideas about that. So perhaps that gives you a bit of context.
Trish:It does, it helps. And I think, if we zoom out and take a look at the larger picture, like what is putting pressure on organizations to mandate that people have ongoing skill development, that this becomes a continuous process. Right? Much more so than it has been in the past. And in my point of view, we're looking at whole industries now that are being disrupted because the business models no longer work. And if your business model no longer works, then you have to create a new business model, which means that the organization, the way that they drive revenue, whether you're a nonprofit and need to monetize the message or your a for-profit corporate, how you make money is changing. And if how you make money is changing, then your operating model is changing. And if your operating model is changing, then your people's ways of working are changing along with it. And that's the thing that's putting the pressure on the ongoing skills development. How do people get better? But it's also putting pressure on the front line to be able to use data analytics themselves to have analytical capability to be able to use data to make decisions at the point of work. So one of the things that's really happening on the analytics side is it's certainly no longer just for the business analysts in the back of the room somewhere. And it's not just for the executives and the management team, but we're now in organizations building analytical capability for everybody from the frontline up to the C suite. And in order to be able to move that culture change forward, because that's what it is, it's not just one method that we're introducing into the workflow. It's a whole new way of thinking about how it is that people get work done. Then we in L&D need to be able to learn how to do that ourselves and then be able to bring that forward and actually embed that into everything it is that we do. The skills development programs that we're bringing to the fore regardless of the people that we're developing and what other discipline or domain that's particularly in. So is there like an industry that you are working with particularly or one that you see where like huge deconstruction is happening?
Stella:So we're talking mainly about working within the technology industry. So we're working currently on a program, for instance, for cybersecurity specialists, which of course spans actually all industries in some sense because everybody has a cybersecurity requirement somewhere. But we are looking at the industries that are within the technology sector. So not specifically learning technologies, technology in the broader sense, which as I say does kind of incorporate everybody cause everybody has an it department.
Trish:Well that's a great particular area to take a look at because with cybersecurity, the number one threat in cybersecurity is human, right? It's people that are becoming vulnerable to social engineering. It's people that are vulnerable to phishing scams. And so cyber security professionals, number one, we have a huge shortage around the world. And number two, in developing cybersecurity skills, it's not just the technical skills, right? It's the core and the foundational skills and the people skills to be able to compel your population to take action.
Stella:And that was one of the things we're already looking at on the programs we're working on. We're working with people who are actually going to be doing the sort of defensive stuff, but we're very aware that they need to have the skills to communicate what they've learned. They need to communicate what they've discovered. Two CEOs, CTOs or maybe, you know, maybe somebody even lower down in the organization they've got to communicate. And also I think they're going to struggle also with needing really good grit and resilience because I think some of the things they are dealing with are really high pressure and actually quite stressful.
Trish:Oh absolutely. And the dial of intensity is just going to continue to turn up, right? So because basically what's happening with the hackers and the"bad guys" out there is that the attacks are going to become more persistent. And as we get new technology, like as you know, quantum computing as an example, as quantum computing becomes something that's available to organizations and operations, it's available to the hackers too, which means that they then get even better and more precise. And cause even more potential damage. And so then that puts pressure on cybersecurity professionals to be able to stay ahead of that. So in a cybersecurity professionals domain, keeping up with those technical skills, but then also again, being able to compel action and compel action through other people is going to be critical. And I remembered it was Dan Pink who said many years ago, probably a decade ago now that, to be technically proficient is necessary but no longer sufficient, right? So how do we marry those things together and from a data and analytics capability, then cybersecurity professionals in this particular example are going to need to help people learn how to fend for themselves as one example. And so when I think of cybersecurity too, I think of dashboards, I think of notification, I think of escalation and all of that is driven by data. But you also need people doing their work to have an understanding of what are the signals or what's the feedback loop that's coming to them directly that helps them make an informed decision on what action they should or should not take. Especially when it comes to whether or not their data or system is vulnerable
Stella:and I think it's really interesting that from what you're saying, it sounds as if more and more people are going to have to get comfortable with, with analyzing data, with understanding what data means. And you know, as a psychologist we know that people can be quite confused about data very often.
Trish:Well, and right now we make it a big deal because it is a big deal because for many of us who've been in the workforce for a long period of time, this is a transition in the next five years, it's not going to be a big deal anymore. It's just going to be how it is that we do business. And it's interesting watching some of the next generation come into the workforce now because they actually are more comfortable with data, not because they're a generation that's comfortable with data just because of their age or their particular cohort, but because of the context of their environment, they've grown up analyzing data all around them, whether it's likes on Facebook or it's likes on Instagram, that's data. Those are data points. Like they're making decisions based on that feedback loop that they're getting off of those rating schemes. You know, I remember when movie reviews started becoming online and now I think about how much rotten tomatoes actually informs and influences my decision on whether I'm going to see a film or not. It has to do with their score. So that's fairly new, like within the past 15, 20 years maybe that we've had that kind of capability. And if we take a look at the people that are just entering the workforce, they've never not known that. So why wouldn't they have, you know, five star rating? Why wouldn't they have qualitative comments from peers and people that they trust in order to help influence the decision? And anything else along those lines just becomes another data set that just becomes part of the decision point.
Stella:That was really interesting actually Trish, that makes it sound far less scary because you know, we perhaps think of data as something that's big and perhaps we don't understand it, but when you talk about it like that, it' actually just makes it seem a lot more approachable.
Trish:It's also ambient. And so this is one of the things that I talk about is that it's ambient just like we think of ambient noise. It's ambient meaning it comes from the surrounding environment and so it's everywhere around us. It also means that we're starting to have more and more devices that are coming into our consumer lives that are coming into our personal lives, that are coming into our homes that we also don't see as threatening or scary. I mean, how many people have"Fitbit's" or an"iWatch"? How many people have a"Nest", right, for being able to do temperature control? How many people now have, one of the big crazes in the States right now, is the"Ring'"so that you can like videotape everybody who's standing on your front stoop.
Stella:Haven't even heard of that. But I have got an Eufy.
Trish:What's that?
Stella:It's one little robotic vacuum cleaner.
Trish:Oh, we have a Roomba, but the same kind of thing. So we have these devices and so if you think about like a"Nest" for the temperature control, if we think about like old Honeywell thermostats in our homes, we would have to set it right? We would have to say, here's the temperature that we like and we would have to program it to say, you know, on the weekends when we're home, it's going to be a different temperature setting than perhaps during the week when we're not at home and at work or at least traditionally not at home and at work Monday through Friday in the Western world. But now with something like the"Nest" we don't have to set it, we don't have to dictate it. It learns from our behavior patterns within our environment. So it actually has sensors that goes there, there's movement in this room, so there are people here or not. And then it learns over time how to self regulate. And then that gives us bottom line gains, right? So that's supposed to give us not only a better experience because now it's the right temperature because it does it automatically, but it also then has bottom line results because it's supposed to be more energy efficient and then be able to say, you know, saving fuel and the planet and these are all great things, right? Triple bottom line: people, planet, profits. So we have these types of devices that are informing us. And then like with the Fitbit and with the iWatches and stuff, we're getting the information about how many steps we've taken. Like you know, how well we're sleeping and we're sharing that data too. And we may not always be the best at taking action on the insights that are being generated, but we're getting more and more used to having these types of IOT, internet of things devices in our environments that are performing data analytics at the edge, which means that it's happening like in the local domain. So in this case, it could be in our homes or it could be on our wrist or, but it's happening right there at that particular point. Not necessarily something that's being computed elsewhere, like up in the cloud or some, you know, weird server somewhere else. It's at the edge. It's right there locally. And as we get more and more used to having those things, we can take those same kind of principles and apply them into our practice in L&D in particular. And one of the ones that I like to talk about actually to give us kind of a framework is I've been tweeting about smart toilets and dumb workplaces.
Stella:I'm going to have to follow that one through.
Trish:Well, so you travel a lot going through the airports, now do you see the happy or not buttons on the ladies toilets? So they're actually a huge business opportunity for the airports. And the reason being is that the airports know that their bottom line results are actually dependent on the cleanliness of their terminals and specifically the toilets. We as passengers as consumers will avoid airports that have, we believe unclean toilets. And actually my home airport in Chicago O'Hare has actually now dropped in the rankings. They've actually lost revenue and they've dropped in the rankings of one of the busiest airports in the world because we've lost passenger traffic because we can't keep the toilets clean. So that's why we're seeing these buttons show up in high traffic areas. In a place where it matters most. It's fast feedback, right? It's a feedback loop. It's not meant to be like, you know, an Opus of information or an impact study. It's meant to be quick. Now we as women probably in the ladies room beforehand, we're not going to take a phone out of our pockets and call the number to report an unclean toilet, but we may or may not hit a button on the way out. And so what's happening is you have that IOT device and internet of things device, just like the nest, just like the iWatch that's now actually embedded right in the environment, right in the flow of work that is collecting feedback really fast. It's leveraging connectivity so it's attached to wifi. So those results go back to a central source. They're analyzed right away. And when it hits some kind of an escalation point or a quality threshold, they know that they need to deploy a staff to go and clean that toilet so that they can stay on top of it. And they know how much that cleanliness and being able to provide that targeted intervention actually connects to bottom line results. So how do we in the learning function, use that same kind of paradigm, not to solve the whole world's problems, not to, you know, do something with such academic rigor that we're going after a PhD and I mean that's not to dig on that, but we're just trying to get enough information to take an action that's going to serve people positive people impact and provide a business result.
Stella:I think that's really interesting Trish, and I would love to talk to you again some time to really embed about perhaps how that really works within the L&D field. Cause I've got all sorts of ideas now coming up in my mind about what we can do. I'm sure you've got a huge amount more you can share. So thank you so much for that really fascinating update on data analytics and how they're affecting us all.
Trish:And the emerging technology that goes around it. Thank you Stella. Lovely to talk to you. Bye.
Stella:Thank you for listening to today's podcast. Please share it with your friends and colleagues and visit our website, stellar labs.edu to learn more about what we do and how we do it. Tune into the next episode.