Episode 17: How digital health companies can recruit and retain top talent during "the Great Resignation" (with Edlitera CEO Claudia Virlanuta)
Edlitera, a data science and machine learning company, trains engineers and analysts in Python programming, data processing, data science and machine learning topics like NLP and computer vision.
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In this episode you’ll discover:
What brought a Harvard Computer Science expert, ecommerce, and marketing tech veteran to this line of work
How training non-technical staff in data literacy and data skills supercharges innovation company-wide
What’s one thing digital health innovators should do right now, TODAY to supercharge their success
Learn more from Carrie and Rebecca:
Healthcare insights (monthly email) | Telehealth/Virtual Care Mgmt Update (biweekly LinkedIn update)
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Our mission is not really to teach programming, ML, and data science; our mission is to make innovation skills accessible to all.”
Read the transcript:
Claudia Virlanuta (00:00):
What I'm suggesting though is that if this is a gap that they've identified themselves in themselves, this is most definitely a gap that exists in their organization because the culture in an organization is set from the top.
Speaker 2 (00:16):
You're listening to Decoding Healthcare Innovation with Carrie Nixon and Rebecca Gwilt, A podcast for novel and disruptive business leaders seeking to transform how we receive and experience healthcare.
Rebecca Gwilt (00:32):
Hello and welcome everyone to the latest episode of Decoding Healthcare Innovation. I am so excited to be joined today by Claudia Virlanuta who is the CEO of Edlitera, which I will tell you Edlitera, which I will tell you a little bit about shortly. Today, we're going to talk about a topic I know is top of mind for a lot of you, which is how we deal with the great resignation. This is one of the toughest labor markets in years, and especially digital health companies are fighting for the best engineers and analysts, the best folks on their team from a training and management perspective to be able to make themselves as compatible, as competitive as possible. And that is a niche where Claudia sits very, very neatly. And we're going to learn a little bit about her company and grab a bunch of her insight today. So Claudia, welcome, welcome.
Claudia Virlanuta (01:25):
Thanks, Rebecca. Thanks for the awesome introduction, and it's really great to be here. I'm super excited.
Rebecca Gwilt (01:32):
So let's get into it. So Edlitera, this is your company. This is a data science and machine learning company, all of the buds buzzwords, of course your company trains engineers and analysts in Python, data processing, data science, machine learning topics like natural language processing, computer vision. You're right there on the edge. And I'd just love to hear a little bit about what your background is, what human experience and experiences brought you to found Edlitera to your CEO position as at Edlitera, and what are you most passionate about what you're doing today?
Claudia Virlanuta (02:17):
Definitely. So Edlitera was something that started accidentally pretty much and continued out of passion. So I, when I got out of school I had a background in economics and statistics and computer science but I ended up working in biotech for a very small startup at the time. And what ended up happening from there is that the startup environment really jived with me. It really worked with the way my just how my brain works. It was engaging, so things were changing all the time, and I really enjoyed that part of it for a couple of years. And from there I realized that what I liked best about my job was the analytics aspect of it. And I proceeded to basically train myself and teach myself these skills that I needed for this at the time, emerging field of data science. So when this was happening, it wasn't really the buzzword that it is right now. It wasn't the case that every company had a data scientist. So
Rebecca Gwilt (03:37):
What year was this? What year was this?
Claudia Virlanuta (03:40):
Great question. I graduated in 2011.
Rebecca Gwilt (03:43):
Oh, 2011. Yeah. Okay. Yeah, it seems so close. And yet, so far.
Claudia Virlanuta (03:50):
I know, right? I really, really enjoy teaching. And this is one of those things where I knew this going out of school, but I wasn't ready to go full into a teaching career. So really teaching on the side was what worked the whole time. And teaching myself, the funny thing about teaching other people is that it's a great way to uncover all the things that you don't know about the topic, right? So when you
Rebecca Gwilt (04:19):
Mm-hmm.
Claudia Virlanuta (04:20):
Yeah. So when you teach a class on literally anything, you can bet that you're going to get a question that you don't know. No matter how much of an expert you are on that, somebody's going to ask something that you haven't thought about. So that was really what was really kind of a theme in my education. And also after that in my career, I want to learn all these things and then I want to teach them. And by teaching them, it reinforces this desire to learn to get even better at that. When I say that at little, I started accidentally, it started because I got so bored at home that I really needed to do something. So what I did was create a meetup group and start teaching workshops.
Rebecca Gwilt (05:09):
So tell me, I mentioned a little bit about this before, it's a tough time. I'm hearing it from my clients all the time. It's a tough time to hire technical staff, and even more than that, it's tough for some companies who are well established to learn the skills that they're going to need as things evolve from a technology perspective going forward. Tell me from your perspective why it's so important that these companies are investing in data literacy and data skills, and for whom is that important in those companies?
Claudia Virlanuta (05:49):
Thanks for that question, Rebecca, because honestly, it comes at the heart of why what we do I think is so relevant and so important nowadays. And I'll start by saying that our mission really is not to teach programming and machine learning and data science. Our mission is to make innovation skills accessible to all. One thing that happens when you start learning something that you by definition didn't know about, especially something like working with data or programming, is that you start thinking in a whole different way. So your mind, which was entrenched in a certain pattern of thought, all of a sudden switches over to something completely different. So let's say that you were I don't know, working in marketing before and all of a sudden, and you've worked in Excel and you've worked with data in Excel for the longest time, and you produce reports and all that stuff, and now you are learning to do the same work, to work with the same data in a way that requires a completely new way of thinking, which is by the way, also a lot more efficient and a lot more powerful. Because right now you are, as you're going through this course and you're learning these skills, all of a sudden your mind is exploding. You're like, oh my God, if it only takes me this long to do this thing, think of all the other things that I can do. Can we try this and can we try this? So this is the kind of mindset change that really drives what we do. Right?
Rebecca Gwilt (07:32):
Yeah, it makes a lot of sense. I wonder if you couldn't give a specific example just to illustrate that. Cause I think that can be super powerful in an organization to get not just the technical innovation staff, but all of the staff thinking about how they can work to improve processes and product consistently.
Claudia Virlanuta (07:54):
Definitely. So I'll share an example, and after that, I'll actually go through my prepared three points here, which I wanted to touch on because I think they're really important. And this is just a more kind of organized way to tackle the topic, which hopefully will be helpful as listeners. So one example that I can share, and this is something that we see a lot. So pretty much our bread and butter right now in terms of the skills that we teach, like you mentioned, are pretty hard skills. So they're starting from programming skills all the way to machine learning and lp. So they're pretty hard skills. That said, our bread and butter is really at the beginning. So learning to use Python, learning to work with data, using Python, learning, these concepts that you need. Now, most people who take our courses are usually not completely data naive. So they've worked with data, perhaps they've worked with Excel, or perhaps they've worked with,
Rebecca Gwilt (09:07):
Ah, that's me.
Claudia Virlanuta (09:08):
There you go. <laugh>
Rebecca Gwilt (09:10):
Excel I got. You lose me at Python.
Claudia Virlanuta (09:14):
Well, I am sure that you are not lost for good. I have full faith in you if you ever need it. <laugh> hit me up. But one thing that I see over and over again in these folks is that they come into a class and they have a track record, track record of working in Excel and doing all this analytical, busy work, is what I call it, right? Producing reports, making data, pretty
Rebecca Gwilt (09:42):
Sure.
Claudia Virlanuta (09:43):
So all of these things happen.
Rebecca Gwilt (09:45):
Corrupt shading in the boxes.
Claudia Virlanuta (09:48):
Yeah, exactly. Coloring the little boxes, you know, have to, yeah. Yeah. And that's great, and it's very important for consuming data and for sharing it within the company, but a lot of those tasks are so repetitive that if you think about it, you're using actual time in your life just doing the same thing over and over again.
Rebecca Gwilt (10:10):
Right. You are the algorithm.
Claudia Virlanuta (10:11):
Exactly. Right. So one thing that many people, one wow moment that people have in our intro level classes pretty much is the fact that, oh my God, this thing that I used to do that took me two hours every week, and it's the same thing, will take me five minutes moving forward because I can write this 10 line script that does exactly that. So once you get to that realization, it's kind of like, oh my God, I don't have to spend time doing this, right. And look at all these other things.
Rebecca Gwilt (10:48):
I see. And then they look for other opportunities to create the same kind of efficiencies. I see. I see. Okay. Exactly. Yeah, that makes a lot of sense. That's make sense. Okay, so let's get to, I love three takeaways, get to your three takeaways.
Claudia Virlanuta (11:00):
Great. So three takeaways about really what's happening right now with the great resignation and thinking about why are people resigning, why are people leaving and how do technical skills really fit into this, and how would fostering this kind of growth and skills help companies to get ahead of this? Number one, the most important thing to consider is that this technology technologies that automate work technologies that involve AI to do increasingly more things is not going away. So this is something that's coming. Whether we are ready for it or not, it's happening. So as a company, we can choose to go with it and to take advantage of it, or we can choose to not right and let our competitors do it. So that's one thing that's happening. And believe it or not, your best employees are going to want to be a part of that change, right?
(12:05):
Going back to the early two thousands, or perhaps mid two thousands is a better example. When the whole software wave started and everybody started adopting software tools and whole bunches of work got automated from CRM to marketing automation to email everything. So when that happened, there were a lot of people who were skeptical. They were like, oh, is not going to last. Right? It's just the trend. It's just something. It's that flash in the pan and lo and behold, it lasted. And the people who ended up thriving through that wave, through that change were the people who not only were able to master the tools, but who were able to use them to take it a step further to say, okay, well this thing is improving my productivity, it's improving, it's freeing up some of my time. What can I do with the rest of my time to add value to the bottom line?
Rebecca Gwilt (13:07):
Sure. Sure.
Claudia Virlanuta (13:07):
So that is one thing, and you can think about, okay, maybe that's at an individual level, so then people would want to stay competitive. But at the organization level, the best organizations are getting on board with this trend are they're becoming leaner, they're becoming more efficient. Unfortunately, for us whether we like it or not, some of these resignations will impact the bottom line of a company. Some of them may or may not end up ending up being needed. If a company implements the right tools and the right processes, they may be able to do more with less. But for that, there's a very strong investment required specifically in the people who are there because mm-hmm. At the end of the day, there's no amount of tools that you can have that will replace all of your people.
Rebecca Gwilt (14:07):
It's good to know. Good to know. As a business works,
Claudia Virlanuta (14:10):
It's good. The same, right? Yeah.
Rebecca Gwilt (14:15):
And the other thing that it's surfacing for me at this moment is I came into the conversation thinking, oh, this will help you have retain your existing technical staff and train non-technical staff really focused on the product. Most of my work is in technology, focusing on the product that you're creating. But you're bringing up a good point, which is just the business of being a business could use a better level of data literacy and technical literacy for having nothing to do with the actual product that the company is creating, but having to do with how the company is run. That might do well, here's a question. Do you think that in the future, all businesses, regardless of whether they have a technical product, will need to have this kind of expertise in house?
Claudia Virlanuta (15:17):
Absolutely. Yeah. I mean, wow, there's no such thing. Wow. There's no such thing as a not technical product. If you're a company and today's marketplace, first of all, do you guys use, as an example, do you use any of the technologies that were introduced in the early two thousands? Do you use crm? Do you use marketing technology, marketing automation?
Rebecca Gwilt (15:36):
We use it. Well, we have our own stack, but yeah, I mean, our firm has been virtual since its inception. So we've always run it using technology and we consider ourselves pretty technically savvy, but we've never looked under the hood of any of these things
Claudia Virlanuta (15:54):
That's right. Now
Rebecca Gwilt (15:55):
From a technology perspective.
Claudia Virlanuta (15:57):
Yeah. That's good. And that's how it's intended to be. So you were able to adopt these technologies and then something's on and do what you do best, which is creating innovative and advise on creating innovative. Now, let me ask you this. How many companies, law firms in the nineties do you think were using these technologies? And how much time do you think it took them to do the things that these technologies are doing for you right now?
Rebecca Gwilt (16:26):
Well, I mean, it's completely different. I mean, the law libraries, were still not all digitized at that point. And there's lots of lawyers that are still working predominantly in paper. So we follow pretty closely in our industry sort of technology adoption. And we really are fairly advanced in comparison to a lot of our peers. But everyone's using it now. I mean, if at the very least, in a pretty robust document management system.
Claudia Virlanuta (16:56):
Absolutely. Absolutely. And this is also an example. I think you hit the nail on the head, Rebecca, about the difference. So you can choose to do things the old fashioned way, or you can choose to adopt some new technologies. You can choose to play around and see what works for you and how you could make it work for you, and then use the rest of the time and the increased productivity to go further.
Rebecca Gwilt (17:26):
Yeah, absolutely. Absolutely. Yeah. Okay. So I get that this is an ability to train existing staff. This is a benefit for retaining existing staff, investing in their future, et cetera. What are the other reasons?
Claudia Virlanuta (17:46):
So we've been through two reasons so far. So the fact that these things are happening, AI is coming, automation is happening whether we like it or not. The second one is that companies out there are doing it anyway, your competitors are doing it anyway. So yeah, a few years ago it was about staying ahead of the curve. Right now it's a bit about catching up. Sure. Making sure that you are going with the flow. The third reason, the third point that I wanted to make here is about attracting talent and keeping talent, which is really right on the money. At the end of the day. You can look at these at the organization level, and you'll realize that, look, as a company, we can keep doing what we're doing. And in some cases we may survive. We may withstand the test of time and never change, but we'll be a dinosaur. And there are so many examples out there for companies who did not focus on what's next, who did not look at ways to innovate, who did just that became dinosaurs.
(18:55):
And one thing that's happening right now is that companies are adopting these things, but right now they're kind of AI and automation and data really. And when I say automation, I just mean about, I just refer to automation when it comes to working with data pretty much because that I think is a really big task that a lot of knowledge workers are working on. And when it comes to attracting talent right now one thing that we need to keep in mind, I think, is the fact that smart people want to do smart things. The people that we all want to attract, that we want to work for us, they want to be engaged, they want to be challenged, and they want a company that will invest in them and respect their time. So that hands down, that is an attractive proposal right there. There is record interest in learning how to work with data because so many more functions than so many more jobs that did not involve data before, now involve data, working with data. So the people that companies are going after want this, right? And they're going to go for those companies that enable them.
(20:24):
And finally, the last thing is that keeping talent is obviously self-explanatory, right? That's the number one thing that comes to mind. Why would we invest in training? Well, because if we show people that we care about them, they're going to be less likely to not care about us and to move on, they're going to be a lot less likely. So there's that. And at the executive level, honestly, it's really, really extra crucial to really invest in these skills. Last thing that I want, I'm going to say before I end on this question there was this study that I found, I believe it was done by a company called Splunk. Have you heard of it? Yeah. So it was a survey published on, I found it on the internet. So apparently they surveyed something like 1300 executives. And I use this example a lot. So it's my has been my go-to example for the past month, <laugh>. So pretty much I'm talking to people on this topic because I find it mind blowing. So out of 1300 senior executives, I have my notes here they asked us how important they think are important. They think the data skills are for their role. So that was the question that was asked.
Rebecca Gwilt (21:48):
And these are non-technical, non-technical,
Claudia Virlanuta (21:51):
Non-technical folks. So executives, leaders of companies who do not have a technical role there. There's nothing, there's no product technical product expectation of them. So out of 1300 senior executives, 81, 81% agree that data skills are required to become a senior leader in their company. So no matter what, just a senior leader, you have to have data skills,
Rebecca Gwilt (22:21):
Yeah
Claudia Virlanuta (22:21):
But. Get this. 67% say that they're not comfortable accessing or using data themselves. So just think about that imposter syndrome for a second.
Rebecca Gwilt (22:36):
A gap. Yeah. Yeah. We've got a gap. Yeah,
Claudia Virlanuta (22:39):
Absolutely. And the thing other than knowing about the kinds of skills that your technical staff needs to have and the work that they're doing, let's say that you don't have any technical staff. You've never been in a position to manage or have to recruit technical staff. Now we come back again to the question of how long do you think that's going to be the case? How are you willing to bet your future on that?
Rebecca Gwilt (23:11):
Yeah. I mean, I guess I'm interested in your vision of what the future looks like. What is the workplace for these kinds of companies look like in future? Is it, are the roles changing? Is it just take a completely different skill set?
Claudia Virlanuta (23:29):
The roles are definitely changing and the roles are changing in ways in which perhaps we wouldn't expect them to change. But also if we were to look at concrete examples, we accept the fact that they're changing. So the fact that if you think about it who used to work with data, let's say 10 years ago, 15 years ago, used to be data analysts. And it used to be data scientists. Engineers worked with data because they had their data generated from whatever product they built. But if you look at who's working with data nowadays, and if you're looking at also organizations at a large scale and I say that I make, I'm about to make a blanket statement and the fact that I'm ready already, I'm going to make the blanket statement that everybody running an organization or a department or a team would like to be considered innovative. They would like to be considered to be data driven, to make their decisions in a non-biased way. Mm-hmm. Myself included.
(24:48):
So we all want to be to consider, and we think of ourselves as unbiased and data driven, and we aspire to those things. But what does it really mean to be data driven? If working with data is something that you dread or you struggle with these 67% of executives, and I'm not saying that these people should all go and take a Python course and start to code, not what I'm suggesting. What I'm suggesting though is that if this is a gap that they've identified themselves in themselves, this is most definitely a gap that exists in their organization because the culture in an organization is set from the top. And here's another thing that I wanted to point out, just on the take a python course kind of thing and basically everybody, and I'm not saying that everybody should learn to code. I'm not saying that, but what I'm saying is that everybody should be exposed to this way of thinking.
(25:55):
Everybody should be exposed, everybody should give it a go. And the truth is that you don't have to be an expert really to reap benefits from this. You don't have to end on going and being an engineer. Yeah. It just changes the way you think. And another important thing is that another example, which is very close to home to me is myself. When I took my first programming class in college, I was so horrendous at it. So I mature also because I was not, I guess this is a statement to who I was at the time, very young person that I dropped out halfway through. I was like, this is not for me. I'm not going to be able to make it. I have no idea. And so I dropped out, and then I think it was a year later when I took another one, and it was a very completely different instructor different, same textbook different instructors, same topic. And it just blew my mind. And I got an A in that class, and I liked it so much that I was like, this makes so much sense. And mind you, I was an economics major, right? So I didn't expect to go on and be right an engineer.
(27:17):
So it is possible. So stick with it.
Rebecca Gwilt (27:20):
Yeah. Well, and teaching matters.
Claudia Virlanuta (27:25):
And teaching matters. Exactly. Yeah.
Rebecca Gwilt (27:26):
Yeah. Okay. So I'm going to put all the information about Edlitera in the show notes so that people can look up the company and see what you guys are up to. I want to close with your thoughts about one thing that digital health innovators could do right now today to supercharge their success.
Claudia Virlanuta (27:49):
Absolutely. So one thing that they can do right now to get ahead of the curve is that perhaps it's a bit overused, but to get ahead and to make use, it works. It works, right? There you go. So to get ahead of the curve, one thing that you can do right now is to look, take a hard look at what you're doing, at what you're working on and what your team is working on, and consider the ways in which those processes can be improved. Consider the ways in which there is slack. Consider the ways in which there is, and not slack by people are not doing what they're supposed to do, but what are some ways in which you can remove some of the stuff that is not crucial? What is the way in which, some ways in which you can automate, automate the boring stuff. There's a book that's called that, by the way. I strongly recommend it. It's really good. And it's a Python book incidentally. But yeah, so look at, take a hard look at that. There is and this is another survey study that I'm going to mention, which I don't remember the source of, and this is horrible because sources,
Rebecca Gwilt (29:07):
Well send it to me afterwards. I'll add it to the two notes as well.
Claudia Virlanuta (29:10):
But the most important thing that this study identified, the number one most important thing that non-technical executives found difficult about working, about using analytics more and using their data more was the fact that they didn't understand. They didn't know how data and analytics could help their bottom line, their team, their company. They just didn't, I can think that about it, right? They're like, oh, we're doing this works. Why do we need to do anything else? What can we do with this? And that's really where you need to slip into a different mindset. You need to look for the ways in which you can take advantage of this. Because if you look 10 years down the line, just imagine that we're in 2010 right now, or even before that, 10, 15 years, right? 2005. If you look 10 years down the line, it's not unlikely that that is what the world is going to be like.
Rebecca Gwilt (30:13):
Sure. Yeah. It reminds me that Slack was actually created as an internal communication and efficiency tool in a company that itself didn't, was not successful, but they sort of created this to create they created the technology to help them communicate better and manage projects better. And that became, it's got to be a unicorn, right?
Claudia Virlanuta (30:38):
Absolutely. Yeah. No, I mean, they're huge. They're huge. And that's, they're their product. Yeah. Now that's what they do. Yeah.
Rebecca Gwilt (30:46):
Okay. So I've really enjoyed this conversation, Claudia. I always like to look sort of into the future, what things are going to look like, and I don't often talk about workforce issues, but this is a great reminder that attracting and retaining technical staff and non-technical staff is incredibly important to overall business success and could be assisted by really investing in these kinds of learnings. So I really appreciate your time, and I'm going to follow up with you about that Python course.
Claudia Virlanuta (31:27):
<laugh>, definitely. I will
Rebecca Gwilt (31:29):
Possibly not in 2022, but at some point I'm going to take you up on this.
Claudia Virlanuta (31:35):
I would love to have you in one of my classes. Going to have a great time.
Rebecca Gwilt (31:41):
Yes. Well, to your point, who knows what possibilities that might open up in my mind. So it would be an investment. For sure. For sure. All right. So thank you for listening today. You can access this podcast wherever you like to listen to podcasts. If you'd like to follow us, please subscribe, and we look forward to speaking with you in two weeks.
Claudia Virlanuta (32:02):
Thanks so much for having me on the show, Rebecca. Really, really enjoyed this. Take care.
Rebecca Gwilt (32:08):
All right. Take care.