Futureproofing Your Workforce

[0:00:52] John Lynch: Hi, everyone. We'll just take a minute as folks join. You can see the number ticking up here. So, just one second. All right. This looks like a good place to start. Good morning, good afternoon, everybody.

Nice to have you here with us today. Thank you for joining us. My name is John Lynch. I lead communications and content here at TalentNeuron. It's my pleasure to host today's webinar. We're going to be talking about how to identify and harness emerging skills and why. Essentially, because it's important to our customers. HR and organizational leaders that partner with TalentNeuron frequently try to understand which skills are most likely to be valuable to their organizations in the near future so they can figure out how to access or develop those capabilities.

In today's webinar, we want to demystify that process and show you specifically how best-in-class organizations use TalentNeuron to identify the skills they need to be competitive. We'll share some specific and proven strategies for using data to identify the most relevant and emerging skills and how to align them with your strategic goals.

Before we get too deep into the content, I want to take a moment to help you orient yourself. What you can see is your webinar dashboard. If you want to submit questions during the webinar at any time, there's a Q&A function here. You can drop in a question. We'll either get to it during the session flow or come to it at the end. You can also download some related media and drop that into the request demo section 2. We'll be sharing some data and some visuals. If you find those hard to see at any time, you can expand your screen using that little icon in the bottom right-hand corner.

Let me get to the most interesting part here. I'm happy to welcome our panelists, Dave Wilkins and Adam Sherlip. Dave, I'm going to hand it over to you first for a quick intro.

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[0:03:28] David Wilkins: Great. Thanks very much, John. It's nice to see everybody here for today's session. Super thrilled to be doing this with Adam today.

It's a really critical topic, I think, in our space. By way of background, I am the Chief Product and Marketing Officer for TalentNeuron and have been in the HCM space for over 20 years. I spent some time at Taleo, Oracle, and a company called Healthcare Source. And I think I've been talking about skills-based everything for most of those 20 years. So, I'm really thrilled to be having this session today. Over to you, Adam.

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[0:04:01] Adam Sherlip: Thanks so much. Adam Sherlip. I'm a Senior Adviser here with TalentNeuron. I've been working with our clients for nearly 4 years, talking through different industries, the impacts of skills, and how they're evolving in the market. I am excited to walk you through one of those investigations today.

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[0:04:19] John Lynch: Awesome. Thank you both. Appreciate you being here. I'll do some table-setting before we finish the rest of the content. But I really want to paint a picture of why skills are important. The ability to identify and develop specific skills is essential to our customers, as I mentioned, and it's specifically because they're trying to become future-ready.

Now what's driving them forward? Generally, they are concerned that they have the mission-critical skills they need to innovate to grow and even succeed daily. The World Economic Forum presented an interesting report based on 2023 data about emerging skills earlier this year. But honestly, you could pick 100 surveys and 100 data points from the last 10 years, and we'd paint a similar picture. This is neatly illustrative, but when 60% of business owners say that the skills gap is their number one barrier to innovation and future success, that's a pretty compelling story.

So maybe, Dave, if I can hand it over to you, is this what you're hearing out in the market?

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[0:05:30] David Wilkins: Yeah. I mean, these kinds of data points are consistent with what we're hearing from clients and when we go to industry events. I just returned this week from People Analytics World, hosted in London. It's a great event for those of you who haven't been. Strategic workforce planning-focused, people analytics-focused. The question of how to become a skills-first organization is really at the top of the mind of many larger organizations as they think through some of the same challenges of skill gaps and preparing for the future.

Our tagline is navigating the future workforce, trying to navigate the future of work. These challenges are looming large for many organizations. The one thing I'll say that's really becoming the crux of the challenge is how do you do that in a way that moves beyond theory and turns into something actionable? How do you take some of the skills-based intent and roll that into your hiring strategy, performance management strategy, and L&D strategy?

Secondly, an emerging challenge is connecting the dots between what's happening inside your organization with your internal skills inventory and your ideation on where things need to go. How do you marry what's happening externally in the marketplace with competitors, large market trends, and analyst information? How do you marry that together and have a good overall perspective on how well this will evolve and, therefore, your build/buy strategy? The extent to which you need to invest versus acquire talent externally. All of those are central to the topics of today's webinar. So we're thrilled to be talking about this stuff today. I think it's very topical. These kinds of stats completely jive with what we see, both with our client base and at industry events and publications.

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[0:07:33] John Lynch: Awesome. Yeah. As you mentioned, the key is to ground this in reality. We're trying to understand what this means for HR and the benefit of our audience today. This is mostly their concern. And what it means is a bunch of critical questions that stakeholders and business leaders come to them with to understand: How is our workforce prepared? Is it prepared? What are the new and emerging skills we should be prioritizing? Do we even have the architecture to compete with competitors for similar roles or functions in a given geography or elsewhere?

So I think this would be a good opportunity to hear from our audience on how they're making those decisions themselves. I'm going to stop sharing my screen now. We have time for a quick poll here. One moment. You should be seeing the poll in front of you. You might need to scroll down to see all of it. We would like to know: What sources do you rely on when making decisions about future skills within your organization? Functional leaders from within the organization, insight from peers, competitor trends, market trends, and recommendations. If there's anything else, feel free to drop that in. We can get to those in the Q&A section too.

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[0:08:59] Adam Sherlip: I love seeing the results. These are some great, different results.

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[0:09:07] John Lynch: We have a clear leader early on input from functional leaders.

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[0:09:19] David Wilkins: It's interesting. I think the emerging dataset here is consistent with what we hear from clients and what we see when talking to prospects. Certainly, that was what was prevalent just last week at People Analytics World. I think organizations today are more mature in their ability to pull for internal needs and discover things from inside the business — from functional leaders to hiring managers. We've observed that folks tend to be a little less mature when it comes to understanding the competitive and market landscapes. It's not quite as institutionalized or mature as a set of processes.

Often, folks lack the data associated with those discovery techniques to find out what's going on in the larger marketplace around them.

I don't know about you, Adam, but this feels pretty consistent with what I'm used to seeing in actual behavior or maturity in organizations.

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[0:10:27] Adam Sherlip: Yeah. It tracks with a lot of the conversations I have with clients and that my teammates have with our clients. We often hear that functional leaders or hiring managers often define the skills that organizations need to invest in or investigate further. That one really is a clear leader. I know it's multiple-choice, so I'm sure many of you selected more than that. A few things that stand out to me are the lower volume or percentage of monitoring competitors and the market. It'll be interesting to connect the dots here on both of those points.

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[0:11:02] John Lynch: Yeah. I think this is maybe a good place to close this out, and we can get back to talking about that data part because I think this is really what we are most interested in sharing today. I think we can get back to some of that in Q&A and pass it out if anyone has questions about the poll results or any follow-up. But what I want to do today is really start showing specifically how we use data to help our customers make those really important and critical decisions.

So, we are going to walk through the data and processes that we use with our clients. To do that today, we've chosen a specific example: data engineers. This is one profile. Data engineers, for example.

We'll provide an overview of the skill requirements for data engineers at S&P 100 companies in the United States. Over the past year, we've analyzed job postings from these companies to identify the skills and capabilities that they are hiring for.

All right. As an aside, we'll do a little overview of that data and what it tells us. Let me just interact with the dataset. If you want to explore this further, go to the media section. You can find this link and explore it now or after the session.

Here are the top 50 skill types that we've identified as popular among the S&P 100 job postings for data engineers in the past year. These are color-coded and classified according to knowledge areas like data pipelines, automation, and Python, skills like big data analytics, scaling, and data integration, and tools and technology like Looker, Amazon EC2, Databricks, and AWS.

This in itself is an interesting insight, but what I really want to get to here is that if we order these by size, and this is according to volume (the number of postings where these particular skill types are turning up), you can see that the most evident in this set are those central to the data engineering roles. We're talking specifically about knowledge areas like SQL and Java.

This is valuable insight if you're in a position where you're trying to identify the skills that are valuable when hiring for a data engineering role. But what is more interesting and our focus today is the percentage change over time. Over the last year, we sized this according to the percentage change in skill types. We can see which skills are emerging, and then the picture changes, and we're looking at the most interesting tools and technology.

Now, the specifics have become more about tools and tech. The increase in demand is more evident. For example, as with Splunk and Looker, Kibana has grown 407% in popularity. Essentially, the S&P 100 is hiring for those particular tools and technologies.

This is our example, so we will follow this logic line down our pathway.

Adam, is there anything that jumps out to you here?

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[0:14:26] Adam Sherlip: What's fascinating to me is that a lot of organizations tend to increase seeking of tools and not necessarily concepts or knowledge areas.

As we look at one — and we have picked one with the rationale behind it, of course — but as we pick one, and another investigation could follow another skill, we'll start to see how it's related to others and derive a bit more insight into what organizations are actually looking for broadly versus what specific organizations are looking for very specifically.

As we go through today's example, hopefully, you'll get a sense of how skills insights can really give you a deeper and broader understanding of where to prioritize your skills.

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[0:15:12] John Lynch: Awesome. Let me hand over to you a little bit here.

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[0:15:15] Adam Sherlip: Great. Thanks so much. Let me go ahead and share my screen. Everything I'm going to show you today is based on the TalentNeuron platform in our skills module.

Hopefully, the screen size is okay. John and Dave, let me know if I need to change the zoom.

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[0:15:33] John Lynch: It's good.

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[0:15:34] Adam Sherlip: Everything is live today. As we go through this flow, I'm going to walk you through a few different steps of how we investigated this because that data is directly extracted from the platform.

As we said, this is a data engineer in the U.S. looking at the S&P 100 employer group a hundred large companies spanning different industries have all said data engineering is important to us.

When we collectively — not necessarily talking to each other — have defined what a data engineer role entails, it's not just one role. Of course, data engineering is effectively in 100% of those postings.

There are some top skills. We saw that before. What we started to look into, and maybe our hiring manager or functional leader is saying, is that our data engineering role needs certain tools and tech.

In this instance, we were really fascinated by what was going on with Looker. There's a good amount of volume there and significant growth. We can even see what those jobs look like and dive into each of the S&P 100 employers we're pulling postings from to understand how Looker is being mentioned.

Without going down that path just yet, we were able to see a high volume of tools and technologies being defined. Of course, this prompts the question: Why this one, and how is it being positioned in the market?

We could see different titles and companies, but I was curious about what is happening around this skill. Is it evolving beyond just a year-over-year increase?

The data shows that there's been a significant increase in postings. But as we move into our evolution tool, it goes beyond one year. It looks at four years' worth of postings.

Once again, it's consistent with the fact that we're looking at data engineering in the S&P 100 across the U.S. We have five buckets of skill evolution here. We're not yet talking about Looker per se.

We have skills identified as core that haven't evolved dramatically in the past four years. We have skills that have grown significantly over that period. Emerging skills that four years ago were a fraction of a percent of postings have seen dramatic upticks year over year.

This is across all skill categories, but if we want to get a sense of those tools and tech, that's where we saw Looker was evolving. It is an emerging skill over those four years.

So, it's not just year-over-year growth; it's been growing significantly. What's going on with Looker?

Some of you may know it, and some may not. We'll then discuss what that skill is and why it is important.

I see a question has come in, and I'm happy to answer questions on the fly. There's a skill about declining. Hopefully, you can all see that.

Does the skill list labeled "declining" take into account factors such as an economic slowdown that may impact the number of jobs posted externally?

It absolutely could, and that can influence it. But when we look at different data points year-over-year and four-year growth trends, those in combination tell me, at least for emerging skills, that Looker is something the market broadly (the S&P 100, in this case) is defining as important.

Now, "declining," says that these organizations have posted less for these skills, with a four-year annualized decrease. That's not to say these skills are unimportant. It means they are being deprioritized in terms of posting shares.

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[0:19:48] David Wilkins: Adam, I think this is one of the most critical things about accessing this data. Suppose you rely primarily on internal signals from hiring managers or functional leads. In that case, their perspective may be out of sync with the competitive landscape or the evolving market.

The amount of data visible here can help create a more informed conversation with a hiring manager about what's going on in the larger marketplace and what the trends look like.

You can bring that perspective back into the organization as a talent leader. That's one of the most compelling use cases for this information.

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[0:20:37] Adam Sherlip: Absolutely. And, like, we started this dive into the platform with the presumption that we didn't know where we would start. The hypothesis or premise here is that we are looking at data engineering and then isolating a skill. But what if our hiring manager or leader says, well, Looker is the one we need to go for? And so that's where I prebuilt a profile. It is a variation of what we have here. I've just required Looker. What we can start to see as I take a step back and go back to my view of all skills is if now I have two skills required, is the skills mix the same? Are the core competencies of this role the same? Now, there's a whole set of analyses I could do about the scarcity of this talent and the cost of this talent. We have data on this platform that will show that this supply pool and the supply-demand ratio have gotten significantly more difficult and that the average salary has increased quite a bit just by requiring Looker as a nonnegotiable.

But as we start to look at the top competencies that are combined with it, we start to see things like the data management component and the tooling, and we look at some of those tools and tech.

The rest of the API shows up for the SMP and the full-stack frameworks. It's a dense skill set, but Tableau is showing up high. Business intelligence software, Power BI, visualization software. We're starting to see a pattern here. And for those who know Looker, this is obvious as to why.

And so all of a sudden, it's like, well, why this skill specifically? Why have we identified that? In this hypothetical example, why has our hiring manager said, "Let's hire talent for Looker, nonnegotiable? We've invested all of our dollars into this tool, which is mission-critical for our business. And so, is this skill viable for us? What is happening around this skill? We can start by looking at that through several lenses. In the evolution of skills, we could even toggle across different industries or industries seeking this particular version of a data engineer. Is it evolving? So, for the semiconductor industry, it's not. For the consumer goods industry, also. And that's okay. I'm sorry. I have to click all skills. It was just for tools and tech. And so that's it.

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[0:23:12] David Wilkins: I figured you'd pick that up alone in a second.

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[0:23:15] Adam Sherlip: Thank you. And so for consumer goods, for example, a role like this, they had postings with Looker as a required skill, and even those roles are evolving. And all of a sudden, we're seeing a click-in. With a double click into the well, Looker was an evolving skill. What is happening deeper than that? What are roles with Looker requiring? If we are trying to be a future-focused organization, we're trying to be skills-first in our hiring. Hiring for Looker is actually not what we're supposed to be doing here. We're supposed to be looking at all of the skills around it, all related to it.

What is related to it can be determined via our skill adjacencies. So lift, and for now, we could look at all the employers. Now, this is a live analysis, as you can probably all tell. Hence, this needs a moment to consider because it is our platform's most sophisticated analysis.

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[0:24:11] David Wilkins: So as that's coming up, Adam, a couple of questions have come in from the Q and A. And one question is about how we're handling aggregations for multi-location recs. So if a rec is posted in 20 locations, are we counting the skills related to that 20 times? The answer is no. We have a sophisticated ability to dedupe and know that something is a single rec but in a multi-location posting.

Another question is the range of jobs. The answer there is pretty much every job that you can imagine.

We're collecting between 1,330,000,000 job posts daily across 39 countries, which covers about 90-ish percent of the global GDP. So it's almost every job category you can imagine in any industry, in any collection of jobs by industry. And then similarly, when you think about skills, which is another question here, we're collecting around 100,000 different skill expressions. We then normalized and rationalized those skill expressions to around 30,000 individual skills, again covering multiple industries. So, the coverage here is very broad. And you're looking at a significant amount of distillation of that raw data via machine learning and AI models to take that all down and put it into a normalized and rationalized set for you to analyze and gain insights from. So that's sort of the big picture. Back to you, Adam.

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[0:25:42] Adam Sherlip: No. Thank you for that. And what I'll add to the one question that came in around the US versus specific locations: if you analyze the US, as we are doing right here, but then you want to look at the skills mix in specific markets, those would be very much based on the data of that market. So right now, when we're looking at the US, it's a really sophisticated deduplication process that factors in multi-location posting. But if you want to really drill down into the skills being sought in a market, we can also do that. It would look at all the market postings and get a sense of how that is being sought. Now, it's a great graphic. It's a compelling graphic.

So I want to just take a moment here because why is our primary skill of Looker so small even though it's our required skill? What it's saying is in this broader talent category, other skills are being sought more. And so those directly related to data visualization, Tableau, Power BI, and dashboards are all sought in more volume than Looker. So immediately, there's a niche element to this skill in the market. It's not as well known as Tableau and Power BI. So maybe a lot of organizations are not seeking it. There are a lot of individuals in the market who may not have direct experience in it. So, hopefully, some of you are starting to see where this is going because as we think about what skills are related to Looker, we can look at that relatedness score and go beyond just volume, but really a correlation metric. And you see that we could go in multiple levels on that. And so what is most related to Looker is Tableau, which I imagine many of you have heard of, even if you haven't heard of Looker. Power BI and data visualization and visualization software are most related to Tableau. Now, we have a set of competencies.

So, I want to use this to answer the question: Is the market moving more towards tools and tech or knowledge areas? That is a question I have a conversation around almost every day with clients. I've seen that hiring managers typically seek experience in tools and technologies, and that makes sense. That's what we are using. That's what we've invested in. But when we're looking at what to hire in, what to invest in, what to develop — whether it's a build or buy strategy — we likely will need to focus on the core competencies relating to that skill. And it's not to say that skill is not important. If Looker is the tool, Looker is the tool. And swap that in for any other tool that we're talking about. But we start to see when we do these kinds of tools analysis that a lot of tools are related to it. So what's interesting for me is when I started to look at job postings for Looker. Looker is owned by Google and was purchased a few years ago for those who don't know.

As you likely know, Tableau is within Salesforce. Power BI is Microsoft. So, if I look at job descriptions for Looker, and I know that I saw one not too long ago from Microsoft, maybe I could find it in the list quickly. And if not, we could always dig it up. Microsoft is also asking if you have experience with Looker.

Even though they are not using Looker, they are using Power BI. They're using their proprietary platforms. So, if they can look outside of the specific tools of necessity, can everybody else. And then how do we determine that? That's kind of what we saw today.

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[0:29:30] John Lynch: Thanks, Adam. Thanks a lot. I think we've done well. Number one, thank you for answering so many questions while we were in the flow of that presentation. But also, it's 9 o'clock, and we're nearly up to 11:30. I think we have covered pretty much every one of the questions here. If there's anything we didn't get to, please, we'll follow up with you on those particular questions. If you have any additional questions, drop them into the Q and A, and we will follow up with you afterward. It leaves me only a short time to say I appreciate your time today, and that goes for our audience. It also goes for you, Dave, and Adam. Thanks for your analysis and your insight. Our next webinar on May 23rd will be a great one on preserving expertise and embracing change in the coming retirement surge. Another take on the skills conversation with Erica and Miguel from our internal team, two outstanding experts. Thank you again for your time, everybody. Dave, Adam, it's time to say goodbye.

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[0:30:32] David Wilkins: Thank you all. Much appreciated.

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[0:30:35] John Lynch: Thanks, everyone. Bye-bye.