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Ep 599: Data Sophistication

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We live in a world of work where understanding, interpreting and telling stories with data has never been more critical. Although Talent Acquisition has become more data-centric in recent years, many TA functions rely on summary statistics that don’t provide a sophisticated enough platform to use data to inform and influence their organizations properly.

So, what can TA leaders do to make their data strategies more effective?

My guest this week is Ben Porr, Chief Customer Officer at Harver. In our conversation, Ben offered some highly actionable advice TA leaders can follow to level up and tell compelling stories with data.

In the interview, we discuss:

• Why data is so essential in TA

• Answering questions with data rather than with opinions or assumptions

• What organizations are missing out on if they only use summary statistics

• What are the innovative organizations doing?

• AI and automation

• What are the most critical data sources?

• Pivot table versus data visualization

• Human in the loop

• Using data to shift strategies

• How AI will empower decision-making based on smaller sets of data

• What will the future look like?

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Transcript:

Matt: Support for this podcast comes from Harver, the industry leading hiring solution helping organizations optimize their talent decisions. Rooted in over 35 years of rich data insights, backed by I-O psychology and cognitive science, Harver delivers a suite of automated solutions that enable organizations to engage, hire and develop the right talent in a fast and fundamentally less biased way. Visit harver.com to learn how you can take the smart path to the right talent.

[Recruiting Future theme]

Matt: Hi, there. Welcome to Episode 599 of Recruiting Future with me, Matt Alder. We live in a world of work where understanding, interpreting and telling stories with data has never been more critical. Although talent acquisition has become more data-centric in recent years, many TA functions rely on summary statistics that don’t provide a sophisticated enough platform to use data to inform and influence their organizations properly.

So, what can TA leaders do to make their data strategies more effective?

My guest this week is Ben Porr, Chief Customer Officer at Harver. In our conversation, Ben offered some highly actionable advice that TA leaders can follow to level up and tell compelling stories with data.

Hi, Ben. And welcome back to the podcast.

Ben: Thank you. Great to be back.

Matt: An absolute pleasure to have you back on the show. Please could you introduce yourself again and tell everyone what you do?

Ben: Yes, definitely. So, hello. My name is Ben Porr. I’m the Chief Customer Officer at Harver. Father of three boys and degree in industrial-organizational psychology. And throughout my career, my goal has been to connect data through the HR lifecycle and really to break down silos between the different HR functions. I lead a global team that consists of technical, psychological data scientists to ensure that we are hiring the best people, an efficient rate for our clients.

Matt: Fantastic stuff. Now you were on the podcast a few months ago. We talked about a number of things, but we touched on data and how important it is in the TA process. And I wanted to have you back on the show to do a little bit of a deeper dive into some of the aspects of that. Before we get into the deeper dive, for people who may not have heard the last episode, just give us a quick recap on your views on why data is so important in talent acquisition.

Ben: I get very nerdy about data, so I’ll try to keep it high level. But I think at the highest level, what we see in research and practice is it’s no surprise data driven organizations are more successful than those that don’t use data. Really, it’s because they can answer questions with data versus opinions and assumptions. And we know in organizations, when it comes to opinions and assumptions, it’s usually the loudest voice, the most powerful part of the organization that’s going to win. So, our goal is really to use data in two main ways. One, it’s to identify areas that we can improve, or two, it’s to confirm assumptions that we have.

Matt: Absolutely. That makes perfect sense. I guess that most people listening will say that they do use data in terms of what they do. But what do you see? I mean, how are most organizations using data in TA, and what are they missing out on?

Ben: I don’t think it’s a surprise. Most HR organizations aren’t at the higher end of data maturity. But we’ve come a long way, especially in the last 20 years. I think now what you see is that organizations are inundated with data. And it’s almost paralyzing to figure out where do I start. The real end goal is for talent leaders to get comfortable and confident using data to inform their decisions, but also to help optimize their processes and monitor the incremental improvements they’re making.

And typically, what we see most TA leaders, they’re using, what we call, summary statistics. You’re looking at time to hire, funnel, conversion, cost per hire. But what we find is when you look at averages, it misses some of the stories within the data, because you might have one group that’s really bad in terms of time to hire, and another group that’s really good. But when you’re looking at the average, it looks like everyone’s at the average, of course. So, by cutting that data, you could start identifying, well, the groups that are really good, what are they doing? And the groups that are bad, what are we missing?

Matt: Yeah, absolutely. I suppose at the other end of the scale, the organizations that are the smartest in terms of how they use data. What is it that they’re doing? What does excellence look like in this?

Ben: I think it really is being proactive instead of reactive. In HR, we’re used to, you’re fighting fires all the time. When you’re fighting fires, you’re making short-term decisions to put that fire out. And the ones that take a more proactive, planful approach are able to recognize when a fire hits, they’re not going to change processes based on that. They’re going to look at the data to determine what’s the best course of action. So, at a practical level, a leader, we always know that our people are busy. They’re performing activities, but not knowing are those activities actually producing the outcomes we want. And a leader has so many things to think about, so they need data to see, well, what are the outcomes of my people? So, I know, when do I need to dive in and help versus yeah, things are going the way they should. You’re producing the outcomes that I expect.

I think the two major things that we’re seeing, and especially with the advancement of AI and automation, is that we can really use data to automate these more administrative activities versus manually the steps that the administrative tasks that recruiters, talent acquisition professionals have to take. So, if you’re building data pipelines the right way, it can automate some of those manual steps, something as easy as moving someone from one stage to the next, as opposed to finding them point and click moving them. The systems can recognize, if they score at a certain point, automatically move them.

I think the best, and really where Harver focuses is making more informed decisions. And having the right data will help you to see things that you might not get from, again, gut instinct opinions. For example, we’ve had a client recently where they really felt like they knew what was important in the job based on the past experiences and what they’ve seen. And what we recognize is that there are transferable skills that people have that doesn’t necessarily have to have experience in those areas to be successful in a job. It’s really about the skills that they possess. So, we ran a study for them.

We hired people based on their traditional methods and then used assessments. Hired people with the nontraditional backgrounds, but with the skill set. And we tracked over time and found that the people who had the skill set outperform the people that had that traditional experience that the organization was looking for.

Matt: Wow. That’s super interesting.

Ben: I think the best organizations, they’re not trying to do everything at once. It really is, how can we pick one or two of our biggest pain points and just try to solve that problem?

Matt: No, absolutely. In terms of the data that we’re talking about, where does the data come from? What are the some of the key data sources that you would view as being the most important?

Ben: Yeah. I think as an HR professional, you think about, it starts with the systems that the software and systems you use. So, I’m sure you’re using different sourcing platforms. You might have pre-hire testing, whether it be objective assessments or interviews, the ATS, that’s capturing information. I think for a talent acquisition professional, those are systems that you own. So, really understanding the data in those systems and connecting the dots through them.

But then outside of that, it’s really looking at the HRIs system or the payroll processing system, because that’s once they’re an employee, that’s where you could see the outcomes of your actual selection of people. And looking into the LMS, learning management system, if you’re doing an engagement survey, the performance management systems, exit surveys, these are all different data that is being collected in systems. And what we find is the best organizations are building relationships with stakeholders that own those systems, and they’re thinking, how do our strategies align, so that we can accomplish one project, but it helps both of our needs and strategies.

Matt: You’ve mentioned quite a few data sources there. And TA teams, TA leaders, they’re asked to do a lot of things. They’ve got a variety of skill sets and experience. I know that getting this deep with data will seem terrifying to some people. What are the different approaches that people can take to combine and analyze this data? Different tools based on what people feel comfortable with, what’s the best way to make sense of all of this?

Ben: Yeah. It depends on the organization. So, if you’re part of a smaller organization, and really any organization, you’re going to have excel. So, my number one recommendation is understand basic excel functions and pivot tables. This is a starting point to start learning how to look at data in a different way, and just cut data by different subgroups that you care about. It might be, let me look at time to hire, but by job and see are there differences.

The next level is having data visualization software. And this is really for people that they’re not as confident with data and numbers. So, with data visualization, you can output numbers. But the best part is it shows you charts and graphs. So, it’s more of a visualization to compare different groups.

And lastly, if you’re part of a large organization, you definitely have business analysts or data scientists, and they always want to solve problems for you. So, identifying the problem you want to solve the questions that you have and really working with them to dive into the data and to produce the results for you. But I think the key in all of this is you do need to have an understanding of your organization, the processes. Because data can be manipulated. And you want to make sure that it makes sense. And that’s where we think about human in the loop. We want to make sure that the human is the expert and they’re identifying where the data seems inconsistent with what I think. So, we can dive in to determine, well, are we missing something here, or is my thinking wrong?

Matt: I think that’s really interesting. And certainly from the conversations that I’ve had with TA leaders over the years, the ones that really do best at this are the people who can take that analysis and create it into really compelling stories that they can take into their business and build that influence with internal stakeholders, get their point across. I think that data storytelling skill is incredibly important. What’s your view on that, and how should people be doing that or how should people learn how to do that?

Ben: Yeah, I completely agree. I think what you find is, when you really understand something, you’re able to talk to it in a practical language that people can understand. And throwing out technical terms, that’s never going to win over an audience that doesn’t have that technical expertise. So, in that case, storytelling is critical. But it is an advanced skill. It’s something that you need to develop. And really, it starts with just recognizing that people have different perspectives, knowledge and assumptions. And your goal isn’t to convince them, but it’s to make them feel heard and feel like they’re a partner.

So, when you’re presenting data to people, it’s understanding how you think they’re going to react and really putting yourself in their shoes to make sure that you’re connecting what you’re saying to the pain they feel and make sure you’re making an emotional and or intellectual connection with them. Because again, most people just want to make sure that you’re talking from their lens and not just saying, “Well, the data says this. So, it doesn’t matter what you think.”

Matt: You gave us a great example earlier in the conversation about data informing someone’s strategy and actually uncovering some quite surprising results. Have you got any more examples of TA strategies that have shifted based on the evidence that’s come from data?

Ben: Yeah, I think the whole rise of transferable skills, soft skills, things like that, that’s really what we’re looking at. And it continues to be the dominant form is resume screens. Those are helpful in terms of telling the story of that person. But what you’re not getting out of that is what are the actual skills that were developed and what is the proficiency level of that skill. And really within resumes, what you don’t find is the work ethic of that individual, how adaptable are they?

We had a client that, again, was really focused on a specific set of skills for their job and using the resume screens and then using interviews to measure those skills. What we found was by adding an objective assessment, which gives you additional evidence. It’s not going to overtake the rest of your assessment process, but it provides you a different view, and it also allows you to see, well, what are the strengths and developmental areas of this individual? Because you’re never going to have the perfect hire. And the goal is to understand, well, where is this person now in relation to where I need them to be?

And then this organization worked with their talent management professionals to say, here are the areas when these people start that they’re going to be good at, and here are the areas where they might need a little support, a little development. And managers are able to take that information. And when they assign tasks, they’re monitoring more on those developmental areas, because they know they’re probably going to need some additional support or they’re not going to perform at the level that the manager needs at that point. And what we found is by doing that, people actually hit full performance level quicker, they’re more engaged and they tend to stay longer in the organization.

Matt: Yeah, amazing stuff. I love that way of connecting the journey of the employee through the organization, because that obviously drives fantastic outcomes, as you’ve described there. As a final question, we have to talk about AI, because it’s illegal to have a podcast about talent acquisition or HR or indeed any podcast without talking about AI.

There’s obviously lots of focus on generative AI, and text and video and all this kind of stuff. But actually, the capability of AI to do things, interesting things, things that couldn’t be done before with data is absolutely enormous. What do you think the future looks like from data perspective, and what role is AI going to play in shaping that?

Ben: There are so many things. Yeah, completely agree. That’s what everyone wants to talk about. It’s constantly evolving, as we know. But what we do know is you’re making more informed decisions with even smaller data sets. So, I think in the past, you needed large data sets to have conclusions on data. But with AI being able to take benchmark information and build it in, you can make informed decisions with smaller data sets, which is really helpful, because we know that humans make judgments on very small data sets, maybe a sample of one sometimes. So, why can’t AI do it without the bias that we’re not even tracking?

I think the other great piece is tracking quality. So, we’ve always been able to track quantity. But a lot of organizations performance is more the quality of the work. You think about professional and financial services, and it’s really hard to actually measure– Performance management, everyone complains how difficult challenging, everyone hates their performance management process. And one of the reasons is there’s no real automated way to track quality. So, it’s up to the individual to reflect, assess, evaluate where we’re seeing with generative AI and just– The ability to analyze unstructured text, we can start tracking quality almost as consistently as quantity.

And that’s been my goal at Harver is the last piece is going back to the beginning where we talk about the summary stats, and how those are great to give an initial indication of what’s going on. But what we find is if you can track individuals across the lifecycle and you have a large sample of individuals, you can start identifying trends in that data and start connecting feedback loops to say, “Well, this skill set is typically part of our high performers and not part of our low performers.” I think that’s one of those critical pieces. A lot of organizations will say, “Well, this skill set is important for this job.” But if high and low performers both have that skill set, you can’t select people, or you shouldn’t select people on that, because both high and low performers are going to have that.

So, the ability to see, well, what differentiates the highly engaged high performers from the disengaged low performers, those are the areas that you want to measure and track and identify. You can only do that by looking at what source the person comes in at, what you measured them on, when you hired them, how they perform on the job and their promotion. And then you do that 100 times, 1,000 times, and you start seeing some amazing trends.

Matt: Absolutely fascinating stuff. We do live in very disruptive times. I think it’s going to be very interesting to see how this all pans out over the next year, two years, three years. Ben, thank you very much for talking to me.

Ben: Thanks for having me, again. Happy. I did good enough to be welcome back.

Matt: [laughs] Anytime.

My thanks to Ben. You can follow this podcast on Apple Podcasts, on Spotify or via your podcasting app of choice. Please also follow the show on Instagram. You can find us by searching for @recruitingfuture. You can search all the past episodes at recruitingfuture.com. On that site, you can also subscribe to our monthly newsletter, Recruiting Future Feast, and get the inside track about everything that’s coming up on the show. Thanks very much for listening. I’ll be back next time, and I hope you’ll join me.

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