The Intelligent Workplace

The Intelligent Workplace

Episode 19

Where is the love for HR Analytics?

Tony Habschmidt
Zegami Technologies

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On this episode of the Intelligent Workplace we take a look at HR Analytics. On previous episodes we have looked at issues affecting humans in the workplace, but what about the management of those resources? Where are the new ideas, systems and solutions that will make the working lives easier for those of you who work in HR.
 
Why is it that HR systems are often forgotten? What types of analytics solutions are available to us, and how does AI play a part in the solution? Changing the hearts and minds of those working in an area of expertise that isn’t always seen as linked analytics is a bit of challenge. HR concepts are, by their nature, highly intuitive and subjective. Solving HR problems requires judgement and patience, analytics aren’t always the first option.
 
Joining me in this discussion on the Intelligent Workplace is Tony Habschmidt. Tony has 10 years of HR consulting experience with the world’s largest HR consultancies, supporting senior leaders tackling their biggest workforce strategy issues.

Tony:                    

Thanks, Chris. Thanks for having me.

Chris:                    

It’s a pleasure. 10 years of HR consulting. I’m feeling pretty comfortable in you being able to teach me a thing or two about HR analytics today.

Tony:                    

Hopefully. A lot of experience in HR consulting and having spent the last couple of years in the tech space now, I’m beginning to realize how much I don’t know and how much more there is to learn because new things are happening all the time.

Chris:                    

I am hearing you. Now before we move on, I will sort of apologize to anybody around the quality of the audio today. We had a few technical difficulties, and it does sound a little bit like Tony speaking from inside a cupboard, but I assure you that he’s not. We’ll do all we can to make sure that the [inaudible 00:02:52] he is good. Now mate, as I said in the opener, HR systems have kind of stayed fairly static for the last few decades. You’ve got personnel records, you keep tabs on annual leave and other sort of accruals. You pay salary, superannuation and maybe there’s a few other functions. We’ve spoken a number of times on this podcast about people in terms of communication, mental health, happiness and productivity, but the data that is stored to help with managing the workforce has remained pretty much in the background. I want to know, why don’t HR systems get the love when considering the future of the intelligent workplace?

Tony:                    

Okay. It’s a good question. Generally what you’re seeing with regard to HR technology and the systems in the workplace is consistency at the core and a lot of change around the peripheries. What I mean by that is if you looked at the HR systems that HR people have in place to support them, you’d see a lot of familiar names. You’d see names like Workday, PeopleSoft, SAP, ADP. A lot of the same tools have been used for decades. If you looked at the core of what HR people are doing, you see a lot of things that they’ve been doing for a long time. Things like talent and performance management, payroll compensation and benefits, things like recruiting, things like training. This is why HR analytics is something that’s really exciting because it’s something new, both in terms of the role that HR people have as well as the technology that they have to support them.

                               

When I say that there’s consistency at the core of a change at the periphery is I mean there’s new innovation coming from around the edges. That innovation is accelerating. Where that’s coming from generally is the tech space, specifically the tech startup space. Even though those core HR systems are the same as they have been for years, a lot of disruptions coming from third party solutions, from tech startups, from entrepreneurs in general, who have realized that the HR tech space is potentially huge. It’s booming and there’s a lot more that can be done from a technology perspective.

                               

What you see is that a lot of this work shuffling to the company life cycle. That is you see a lot of the big players that are out there that are beginning to acquire third party solutions for themselves or open up their systems to allow a lot of these smaller players to kind of bolt on to the core HR system so that companies can have a lot more flexibility than they’ve ever had in terms of customizing the HR technology and the HR software that they have to support them.

                               

If you are in HR or in HR technology at all, what you’re probably going to see is innovation coming from new companies that you can now add their solutions to your core HR systems, or you’re going to see a lot of these big HR tech companies acquiring small solutions and kind of repackaging them as modules in the core HRIS. Whatever you’re doing, innovation is going to be coming your way. Either as new, smaller third party software or as new modules that are coming out of the big HRIS providers.

Chris:                    

That’s interesting. You talk to executives in areas such as marketing, finance and IT, and they would most likely nod their head when they are asked if technology has helped them transform their jobs in the last few years. But it feels like you might not quite get the same reaction from HR executives. Do you think that’s a fair statement?

Tony:                    

Yeah, that’s absolutely fair. I mean just take a few examples. Think about how technology has changed all of the other functions in a corporate environment. I mean with finance, IT, logistics, marketing. I mean what geo location and logistics software has allowed you to do is track packages or track your supply chain precise to the nanoseconds. Or if you look at what finance has done, you can look at different solutions that are in place. If you look at marketing, if you’re still doing marketing the same way you were five years ago, you’re a dinosaur. There is entirely new things have come up through Facebook ad placement, Google analytics, all that other stuff.

                               

It’s valid to ask the question, why has HR been less digitally transformed than the other functions in a company? If you look at the business literature on this, it’s not always very friendly to HR. You’ll typically hear words like laggard or HR has a resistance to change or slow to digital transformation. If you’re in HR, I’m here to tell you that this is not your fault. If you look at the last five to seven years about what technological innovations allowed companies to do, it’s not necessarily something that helps HR. Like I said, you have the ability to process and track things precise to the nanosecond.

                               

For example, Amazon tracking your packages. But people, human beings are a good deal more complex than packages. HR by its nature has always been kind of a soft skill dominated space. That’s for a good reason. That’s because if you’re dealing with employee engagement or employee training, these are things that happen over months and sometimes years. They don’t happen over nanoseconds. The entire revolution of technology that’s taken place over the last five to 10 years has helped the business process faster, but it’s not going to help something as human oriented as HR.

Chris:                    

I suspect there are many in the HR field that believe just because you can use technology in HR, it doesn’t mean that you should. But I’m also tipping. You might have a slightly different view on that.

Tony:                    

Yeah, that’s right. I do. HR has traditionally been seen as a cost center, not a center that kind of generates [crosstalk 00:08:51] value for the organization. When HR is doing something new like HR analytics, it’s worth asking if you’re going to do something new in HR, particularly in the hyper competitive and tech oriented business climate, if you’re going to be investing hundreds of thousands of dollars, perhaps millions and new people with new skill sets, with new technologies. There’s the ROI test. There’s a return on investment test. If you’re going to be doing that, is there a way that what those people are going to be doing will actually add value to the tunes of hundreds of thousands or millions of dollars a year? I think that in the current climate you have to have that test. I mean there’s always shiny new toy syndrome. What you see is a lot of that tech innovation coming from around the edges as I’ve described.

                               

If you wanted to look at Crunchbase or AngelList or any of the repository of startups or new technology companies that are out there, you see lots and lots of companies that are trying to cram technology into HR in a way that doesn’t necessarily shift. They are engaged in almost like an arms race to deploy something like AI and then an arms race to deploy even more powerful, even more complicated AI in order to provide even more complicated or even more powerful tools. This is what made Zegami so interesting to me, which is that from the HR analytics standpoint, Zegami was one company that said, “What if instead of making technology more and more and more complex in the HR workspace, we went the other direction and we tried to make technology more and more and more simple to the point where you can broaden the base of the percentage of your workforce that can use it?”

Chris:                    

Have people in the field been burned by solutions that aren’t needed in the past or maybe that haven’t been fit for purpose? Have there been mistakes made in that area, do you think?

Tony:                    

Well, yes, I’d say so. I mean, take the example of a company I was working with recently. A huge company, this Fortune 500 company that had taken the time to build out a new HR analytics function, HR analytics, people analytics, workforce analytics. It goes by different names, but take the case of this company and they put the effort into building an entirely new team that allowed them to address these workforce issues from a data perspective. What happened then was that they were creating a team in a very soft skill environment where they were the only ones who really knew how to use technology. They were the only ones who really knew how to use complex statistical and AI tools.

                               

The issue was that the new data scientists and HR analytics at this company weren’t speaking the same language as everyone else around them. It was almost like you had an HR organization where everybody was speaking English, but one team was speaking Russian or something like that. They couldn’t align. When faced with that, particularly in an environment where HR is filled with a lot of smart people, but a lot of them may not even be particularly adept at using tools like Excel. What happened was that the HR analytics team became a purely reactionary function. All they did was respond to inquiries that the other HR teams had sent them. The head of this HR analytics team said to me, “We basically just become a reporting function. Every quarter or every year, we put together a series of reports because we’re just used to seeing, but we’re not sure that we’re actually providing the strategic insights that was our original mandate when we were to form the team.”

                               

A lot of that comes from the fact of that not everybody’s on the same page. People aren’t speaking the same language technology-wise. A lot of the things that the HR people might be able to be doing for themselves if they had better technology and better tools, they pretty much had to ask the data scientists for everything. It was bad going both ways. From the sense of the HR generalists, they were upset because everything they need to do had to go through this bottleneck, which is the analytics. But on the standpoint of the analytics team, they didn’t have the bandwidth to pursue their own initiatives to find insights for the organization to be creative because so much of their bandwidth was taken up with responding to requests coming from outside the analyst team.

Chris:                    

Mate, I’m assuming there are so many options in terms of the ways in which technology might be able to help within the HR field. Where do you see it adding the most bang for your buck?

Tony:                    

Well, I think I’ve already tipped this but it’s in the HR analytics space, but I can be more specific.

Chris:                    

Yeah, sure.

Tony:                    

If you think about what you can do with HR analytics now the HR has this mandate to do more and more with the employee and HR data they have at their disposal, they can really augment their strategic decision making in some pretty interesting ways. Generally what we mean by that is you can take the strategic decisions that HR normally makes, normally needs to make. Things like recruiting or training or performance management. With better data on your employees, you can create better trainings because you can identify where your skill gaps are more precisely than you ever have before. Particularly if you’re looking at organizations that can do things like link their LinkedIn profiles to their own employee profiles inherently. You can look at the skill sets you have as an organization and again, identify the gaps.

                               

Strategically, once you know your biggest skill gaps across your entire workforce, you can do two things. You can deploy your training and organization development in a way that addresses the skill gaps directly instead of just doing the same trainings you’ve always done, or you can feed them into the recruitment cycle where you say instead of recruiting for these three jobs because we have these three vacancies, you can take a more nuanced approach and say, “All right. We need a total, not three jobs. We need a total of 15 years of Java experience, five years of Agile experience. At least two years of experience with a competitor.” You can target your recruiting efforts to fill the skill gaps you have much more precisely.

Chris:                    

It feels like with that approach you are sort of getting closer to be able to provide sort of a return on investment calculation, which in the past around some HR things hasn’t always been that easy.

Tony:                    

Yeah. That’s a good question because generally if you’re looking at the value that’s being added, there is measurable and there is non measurable value that can come from these things. Things you can measure are things like turnover. If you can reduce your unwanted turnover or attrition ratio over the course of a year or two, it has real tangible economic benefits. There have been loads of studies that explain how the cost of losing an employee is something like one and a half times their salary. Because you have to hire someone and skill them up. There are tangible measures.

                               

For example, like the attrition rate, you can look at things like performance ratings over time. You can look at employee engagement. Most big companies are on some kind of employee satisfaction and engagement survey. You can look at things that are measurable now. If they’re ticking upwards or if they’re ticking in the right direction, that’s a pretty good indicator that what you’re doing is providing an ROI that’s actually measurable in dollars.

                               

But there’s also the unmeasurable aspects to it too. If you are an HR organization, a lot of that comes down to a gut check. You say, are we building a better workplace? Are building a more diverse, more inclusive workplace? Are we building a place that’s better to work? Are we building a place that produces high-performers better? All these kinds of intangible things are difficult to measure but they’re impossible to ignore. Really the best gut check that I have when I speak to HR people is I ask, “Are you a reactive or a proactive function now? Are you reactive in the sense of you’re responding to the things that are happening, you’re responding to attrition, you’re responding to diversity issues? Or are you really being proactive and going on the offense and attacking these issues and trying to create proactively a better place to work?”

Chris:                    

It’s funny you talk about gut check, and gut feeling is something that sort of is part of the HR landscape. It has been for a long time because a gut feeling about a person or a decision, that sort of comes around based on your experience and your knowledge. I guess in some ways, it’s hard for you to bridge the gap between providing them with more analytics that could then sort of help them have a better gut feeling in a certain situation. But that sort of feels kind of like where you’re heading with this.

Tony:                    

Yeah, I think it’s right. This is why using tools that are intuitive and simpler are really important because the human eye is really good at spotting visual trends in data. I’m a big fan of data visualization as you can probably tell.

Chris:                    

Yes.

Tony:                    

Because if you have a dataset and you are an HR generalist and you want to know, is it a bell curve distribution? I mean, you can spend an hour. You can give it to data scientists, and they’ll be able to run formulas or code to tell you if it’s a bell curve distribution. But if you have a tool that could just build a histogram or something right at the tips of your fingers, you could see that. You don’t necessarily need to know every correlation coefficient or a statistical figure to know what the answer is because so much of understanding HR data is not a formula like it might be in finance. Or like it might be in marketing. You can’t convert everything into ratios.

                               

It’s a matter of looking at big data sets and saying, “Is this a trend that we want to see? Is this a trend that makes sense to us? Is this a distribution that generally feels right to us?” That’s the big difference between HR data and all the other types of data because it’s based on so much more unstructured information about the employees because it’s about real human beings. It’s not about packages or dollars or ads being placed on the internet.

Chris:                    

Yeah. I was worried there for a minute there was going to be a skills gap between the HR people and say the analytics people because do you try and train up an HR person in analytics, or do you bring an analytics person over to the HR function? But I guess if you sort of bring it back to being something that you can visually see, it takes away from that a little bit I think.

Tony:                    

Yeah, I completely agree. I mean that’s really at the core of all of this. Do you make tools that are so complex that only 2% of your organization can use them, the data scientists or the analytics experts? Or do you make tools that are so easy to access and so easy to use, you can build anyone up to … They may not be all the way up to a data scientist, but you can take the average employee in your workforce and build them up 70 to 80% of a data scientist, say.

                               

Let me just take further that example because this hits on something that I like to call designing your organization for serendipity. These kind of serendipitous insights about your organization is where a lot of competitive advantage is going to come from in the future. If you’re a company, it’s going to be the employee that finds some mistake in your manufacturing process. The employee who finds some better way of doing things is going to add in a tremendous amount of value. The employee who questions why we’ve always done it this way, the employee who finds something in the data or employee who designs the next killer app that gives you a huge competitive advantage over your rivals. It’s really important that you broaden the base of people where that killer insight can ultimately come from.

                               

Let me just pose the question to you, Chris. If you have two organizations, one of them has only 2% of the employees are at a level where they can access, say the data scientists? Only 2% of employees are at a level where they can explore data and really find insights from it. You have another organization where they have a shared simple tool where, say 80 or 90%, of their organization is working with data and diving into data and trying to find insights. Which of those two organizations is going to be more likely to find that killer insight?

Chris:                    

I’m with you, mate. That’s a very, very clever segue for you. Because obviously you are an HR expert, but you do represent Zegami Technologies. They have a unique ability to combine traditional and non-traditional data into visual analytics tools. In terms of HR, what types of data combinations would you be seeing as the killer combos that could really drive return on investment for HR professionals?

Tony:                    

Yeah, that’s a good segue because it kind of relates to the point I made earlier about the data on employees is typically less structured than the data on dollars or supply chain [inaudible 00:00:22:35]. I think a real part of the value, particularly in something like what Zegami does is the ability to combine data from different sources. Because that’s been one of the large barriers that we haven’t really touched on with regard to HR because it’s not just the soft skillset nature of HR, but also the fact that if you are an HR organization, your HR systems and the HR data that you have is pretty disparate. It’s very likely distributed across a number of different sources.

                               

You can have your core employee data, who they are, their title, how much they make. That’s probably in your HRIS. You can have your performance reviews that you do every year. That may be in a completely separate performance review software. You can have the employee satisfaction or employee engagement survey that you run probably in a completely separate database. The market competitiveness of pay, a different database. You have LinkedIn. For a lot of organizations, LinkedIn which is outside of the organization, gives them a better view of the skill sets and prior working experience of their employees than their own internal systems.

Chris:                    

Really? That’s interesting.

Tony:                    

It’s a very common thing. If you go to an organization, they say, “We don’t know who our Spanish speakers are. We don’t know who are people who have worked for competitors of ours. When we staff a project, we don’t know exactly who has Java experience or other types of coding experience.” What was really interesting is that LinkedIn as a completely external database has all this stuff. Organizations are trying to seek to find a way to bring that into the organization themselves and kind of look at their employee skill sets in a fresh new way.

                               

Okay. Take the ability to combine those data sources that we were just talking about and you can do some pretty interesting things that HR hasn’t really been able to do easily before. Take for example something like gender pay gap, which is huge. It is very interesting and relevant to all organizations.

Chris:                    

Yeah, absolutely.

Tony:                    

Not only can you conduct an analysis like that better and have the results shown more clearly with better HR analytic software, but you can combine that with the different data sources that have, to this point, been pretty siloed off and separate from each other. For example, if you want to look, not just do we have a gender pay gap, and where is it most acute? You can say it as having a gender pay gap leads to lower engagement, lower employee satisfaction. Does that lower employee satisfaction lead to higher turnover within the subsets of the population where it’s most present? That relies on data from a lot of different sources. Previously the only way of combining them was taking big dumps of dead data, unlinked data, and trying to put it together, piece in all together in a really complex analysis offline, maybe an Excel or SQL, some other tool.

                               

Now HR analytics software is getting to a point where you can sync all of these different sources into one place and kind of visualize it and look down at it all at a meta level so you can look at and explore these relations in data that was typically separate and siloed apart from each other. You can extend this beyond looking at the gender pay gap and engagement. You can look at things like skillsets. You can say, what if we want them to know not just our greatest skill gaps, but the degree to which the skill gaps are filled by our candidate pool. Or by our contingent workforce. Because if you look at retired, semi-retired, contracted employees, everything that kind of makes up the contingent workforce is becoming a big part of the modern workplace.

                               

Organizations are increasingly looking to contractors or subcontractors to piece together these sorts of agile teams that they’re developing. What if they were able to, when they searched for an employee’s skill, say we need someone with X amount of experience in oil and gas? They didn’t just search their current employees. They looked at their current employees, they looked at all of their retired and semi-retired employees. They looked at all of their contractors. They looked at everybody in their recruitment software, everyone in their recruitment funnel all at once. So to the extent you’re building a new team, you can recognize if you are a line manager or a GM or an HR manager, wherever you are. You can build a team recognizing, okay, I need to staff this 10 person team with six people from within the organization, but two people from our contractor pool and two people that we’re going to need to hire. Being able to do that all in one view that spans across the HR data sources is really, really powerful.

Chris:                    

Well, mate. It sounds like you are ready to turn the HR system world on its head and finally provide those HR execs with some really nice real world analytics to help them do their job a lot better. Mate, it’s exciting times over there at Zegami. Mate, I just want to say thanks very much for joining me today because you have been a wealth of information and really opened up my eyes and hopefully also for the listeners. So many new opportunities in the world of HR analytics. Thanks very much.

Tony:                    

Thanks a lot. I appreciate it. It’s the democratization of big data. That’s what we call it.

Chris:                    

Nice work, mate. Cheers. Thank you very much.

Tony:                    

Thank you.

 

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