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Hosts Stacey Harris and John Sumser discuss important news and topics in recruiting and HR technology. Listen live every Thursday or catch up on full episodes with transcriptions here.

HR Tech Weekly

Episode: 227
Air Date: July 25, 2019

 

Transcript

 

Important: Our transcripts at HRExaminer are AI-powered (and fairly accurate) but there are still instances where the robots get confused (or extremely confused) and make errors. Please expect some inaccuracies as you read through the text of this conversation and let us know if you find something wrong and we’ll get it fixed right away. Thank you for your understanding.

SPEAKERS
John Sumser
Stacey Harris

FULL TRANSCRIPT

00:00:14:07 – 00:00:26:29
Good morning and welcome to HR Tech Weekly, One Step Closer with Stacey Harris and John Sumser, and everytime I hear that opening music I think of a lime green bright shiny field of dewey shamrocks.

00:00:27:11 – 00:00:30:23
How about you Stacey?

00:00:30:27 – 00:00:43:29
I would agree it definitely makes me think fondly of my trip several years back to Ireland so, but it also puts us in a much brighter mood because a lot of stuff going on in HR technology could require a little lightness these days.

00:00:44:14 – 00:00:44:20
How about you John, are you doing well this week?

00:00:44:20 – 00:00:54:13
Yeah, I’m doing fine. I am in the throes of sorting out what I’ve learned in the research the last couple of months and I think you’re doing the same thing.

00:00:55:13 – 00:01:31:19
Yeah. Work data cleaning spells. We’ve got about another two weeks of data cleaning process though. It’s fascinating. You know those who don’t actually get to get their hands dirty with data cleaning and I was just complaining with to you a few months ago about the fact that I’m going spreadsheet style by style because we had a couple a little mishap. Memorization on something that wasn’t quite looked at right. And so you know Amy and I are sort of going back and doing it but I want to say it is really neat to look at the different type of companies who respond to the research. When you get anything from a small little non-profit organization that you know working on sort of creating sort of Better Homes For animal shelters to

00:01:31:27 – 00:01:51:28
You know the largest oil companies in the world in your end. The difference is between those organizations and some of the similarities is just fascinating so. It is kind of fun to go in and do data cleaning you know at a granular level and don’t get to do that this year and yet you know once they get past the fact that I’ve got to look up thousands of cells I’m having fun with it.

00:01:52:14 – 00:02:40:20
And I’ll tell you what my my view has become that if you do get that close to the data you miss things. Yeah right. And that’s one of the things that I think I think people don’t understand about what’s going on with the emergence of machine learning and natural language processing tools all over the place is that we’re living the machine you’re immersed in the data you missed the opportunity to learn some really very good works though well in a back and forth conversation since the age of dawn of technology right the minute you let a piece of technology build the chair for you you don’t really think about how a chair maybe should be engineered for people because it’s just pumping out a bunch chairs right

00:02:41:02 – 00:02:46:01
Where back in the day is when you hand carved or at least pieced together all the pieces of a chair

00:02:46:12 – 00:03:06:14
You literally would test it try it out see someone sit in it it was a much more personal process and you end up with a much better product in some way that’s right. So the question is you know is that dynamic happening here too. The answer is probably yes but we didn’t start making chairs in manufacturing environments because of that.

00:03:07:06 – 00:03:45:16
Boy that’s a long conversation that’s just a little left left that’s just who That’s because you’ve got some really interesting things to cover here but the you know the the essence of the issue the contemporary cultural revolt against industrial stuff is that of teasing or handmade things have this kind of elusive quality. How to describe it but there’s something that you get when the product has human touch all over that you don’t do that the other way.

00:03:48:02 – 00:04:09:08
And I would equate that to looking at the details in a much deeper level. And I’m I’m being a devil’s advocate on this obviously because I spend hours you say how do you get the day that I know the answer you never hear of is yeah no but I know it’s important to do it. I may not like to look line by line but I know how critical is and how valuable this.

00:04:10:13 – 00:04:31:13
Yeah. You know I did. It was fun I said as soon as a way it’s a way to deep inside Yep. Exactly. It isn’t funny. There’s nothing worse than getting lost in the data cleaving exercise where you eventually decide that you should eat something up very early on when you have to basically do it.

00:04:31:29 – 00:05:02:05
That’s probably the process that is very painful. Speaking of data cleaning I mean the This Week the topics and I think the news that’s going on in the entire tax base is really much more focused. Look at this concept about data data cleaning and how that sort of plays a role in decisions that are being made in companies. So it’s been a busy week we’ve got some people and talent partnering to recognize top talent.

00:05:02:05 – 00:05:33:12
We’ve talked quite a bit about top talent in the past and Stacy Chapman and being on people as well so. I’ll be very interested in your perspective on this. John you know how is this state of the deep data analysis tech that goes in here is going to work together. We also have a couple of interesting acquisition and investment conversations going on toast which is most prevalent No. The toast is toast this is actually a retail operation to a point of sale system right as well as consulting services organization for retail organization. They acquired an H.R. technology

00:05:33:12 – 00:06:04:20
technology it was focused on the retail environment called strat X and there’s some conversation there to be had about consulting firms and the content and the information connecting with the technology and is that a better conversation versus trait technology. We also saw this week that gusto for those goofball the SMB. Payroll and a term market gusto is one of the sort of newer names in the market but has been growing rapidly they received 200 million dollars in serious funding. And with that they plan to expand to an R.A. location in New York City.

00:06:04:21 – 00:06:40:01
So that sort of interesting. We also have a couple of small organizations that get seed funding that’s worth mentioning Amsterdam based organization five miles attract seven point fifty thousand dollars to improve employee skills. We also got a tour outsourcing provider co advantage being acquired by Aqua lime Capital Partners. So lots of little stories here and then some stuff we missed last week on a light offering daily pay one of your favorite topics. Now Wilf Willis Towers Watson introducing a new product called skill view which is focused on compensation frameworks and if we have time

00:06:40:14 – 00:06:54:14
Mean you had great conversation earlier about the idea of how a companies can avoid ethnic washing a new term I heard this week that we can talk a little bit about that. Where do you want to start. Don you start with a calendar or do you want to start with some of the bigger topic.

00:06:54:16 – 00:07:26:04
Let’s talk about food beverage talent. These are these. This is this is an interesting thing. So freedom on people. Saying they are two four hundred fifty five employees. I’m going. I’m going to see them in a couple of weeks. And. My sense is that that they are growing faster than anybody really risky. But for the 50 or 500 people this is a this is a juggernaut.

00:07:26:04 – 00:07:28:00
This is a big deal.

00:07:28:12 – 00:07:53:14
And they’ve partnered with shrewd talent which is the amazing company run by Stacy Chapman who is a longtime industry veteran and as I understand Stacy is trying to do the it’s trying to build the capacity to use data with their own having to be clean discovered as your equipment is that.

00:07:53:15 – 00:08:33:07
Yeah definitely my take her her you know in the conversations I’ve had with her. Her goal is to extract information from environments that would naturally or generally require still much cleaning that they’re almost unusable right. So they could later put it on and actually create data from them that is usable in your recruiting process and your town selection process. I will be completely honest I do not understand the technology or how it all works because she has this amazing sort of algorithm that you know she has built that underlines a lot of traditional sort of analytic tools and standard sort of data like processes and data like environments.

00:08:33:09 – 00:08:36:05
But yeah that’s my my final understanding what she does.

00:08:36:27 – 00:08:42:06
So if I were to try to explain this small detail to tell a

00:08:43:29 – 00:09:39:02
begins with the premise that your data is a mess and the first thing that they did was build tools to collect all of that data. So they are really really great at process automation data collection process automation they’re good at scooping up your data and carrying it over and putting it in the lake. Then the next thing that they’ve done is they’ve built machine learning algorithms that recognize the behavior of data so it doesn’t have to have a shared title on the road to be merged so you can call it first name over here and surname over there and because the text that said the D the column having behaves in the same way it can be recognized and merged and so they use that to to bring the data under the Government’s

00:09:42:01 – 00:09:55:09
so it seems to me that the Sudan people and troop talent. Immigration is a move for both companies into too much bigger markets.

00:09:55:15 – 00:10:27:15
I would agree because the organizations that leverage have talent are generally the biggest organization. I mean you sort of have to have large enough data set to make this work. But I understand what Stacey does feed on people not as much I mean they definitely are generally larger companies but they’ve also got the mid-market and SMB than what they do. They were also on the road for artificial intelligence doing a lot of their own categorization and I know you’ve been talking to them for quite some while. So is there going to be some overlap here or is this a natural fit do you think.

00:10:27:15 – 00:10:39:24
I think I think it’s a natural fit. The two companies the two companies make each other better. And the idea that there is some way of

00:10:40:09 – 00:11:03:20
Taking over data giving you an order and then instantaneously Poppy you know the things that you’re looking for. So so. So this project sounds like a lot of the projects that aren’t caught up in in shiny objects things tend to be about making sure poor people work whether this sounds like another approach.

00:11:05:08 – 00:11:25:17
And it also sounds like we’re generating a lot of the work that Stacey does would only be viable or feasible probably a better word to use by the largest organizations would by using the spin on CRM and some other things this could make it more accessible to small organizations I don’t know that’s the case that something to be the SBC and the freedom people but it feels like that might be part of what this would do.

00:11:25:26 – 00:11:32:08
Yeah I’m sure anxious to learn more detail about it but it seems like a really really interesting concept.

00:11:33:08 – 00:11:54:05
Yeah definitely it will be. I think you’re going to see more. I mean we have seen a lot of this but a lot of it with I think bigger names is probably in larger companies right. And you know now I think we’re starting to see some of the work that was done two years ago pay off for some of these smaller companies because they have made the investment in an understanding the data process is doing right.

00:11:54:09 – 00:11:58:13
All right. So you want people to be toast.

00:11:58:26 – 00:12:34:26
Yes. I mean this is again I you know I mentioned a little bit. This is a retail sort of play and so it’s a very industry specific play host a quiet strategy. The strategy is H.R. and payroll software for restaurants right. They’re one of the leading ones in the area. And so unless you’re in the restaurant business you probably wouldn’t have ever heard of static. And same thing with Toast. Toast is a consulting firm as well as a supplier of point of sale systems or processes as they’re called in the retail environment and having worked in the retail environment. When I first came over doing a two hour technology I was sort of flabbergasted by how often we didn’t talk about the technologies that were part of the operations environment of any business because

00:12:35:08 – 00:13:06:22
I could tell you probably at least 50 percent of time when you go into a retail environment the time tracking system is not standalone in the stores it’s part of the point of Bill system the U.S. system. So tracking you know retail point of sales or retail you know time tracking systems has to take that into consideration right. And so this is an area where toast basically you know acquired simply a jar with the idea of sort of merging those things together payroll H.R. and the things that they’re offered in the U.S.

00:13:06:23 – 00:13:53:01
system with their time and attendance as well as all of the point of sale information. But what caught my eye on this is that toast is also what consulting firm they advise on things or restaurant and advise on things from a people perspective. And we also saw just last month some very similar sort of acquisitions or decisions being made. Willis Towers Watson introduced their innovative skill based pay analytics platform called Skills view which is basically taking their data on. Deals and analytics and their compensation framework and tying all that data together into a technology right that will help people make decisions about pay and how that relate to how you should pay personal level not per role in an organization which is absolutely kind of fascinating.

00:13:53:03 – 00:14:27:05
I should note that Will’s terrorist Watson has one of my favorite people over there heading up their strategy work called Karen Leonard. She’s worked with me over at Burson in the day. I don’t know if this is one of her projects but it sounds like it definitely would be part of something she would think of things like that was a light which is another sort of outsourcing organization offering. Daily pay on demand payroll options. They are an outsourcing organization with a lot of services and consulting and they’re adding technology we can talk about the daily pay issue in a moment but what’s your thought on this idea of Operation consulting bringing more technology into the world is this.

00:14:27:15 – 00:14:46:08
Is this just you know the cycle going around again or are we going to see more of this now that content is becoming important with the machine learning process big question. My take is that is that venture backed firms get punished brutally for having services income that.

00:14:46:29 – 00:15:17:05
Their investors don’t like. The valuation is affected by it. So what you see in all of the cloud companies is that they have to figure out a way to do consulting that doesn’t hit their books. Damn. Right. And so and so you can’t really buy say workday without a relationship with one two workdays installers because work days installers do the consulting work.

00:15:17:07 – 00:15:40:09
That means that there’s a really big over two that means that means that that the largest complaint about those software things in all of the enterprise deliveries is that the consulting is expensive and incomplete and you can’t just drop a piece of software organization to expect it to work.

00:15:40:13 – 00:15:59:06
It’s a complex human machine interaction. So the idea that what you would do is hire a consultant to me who happens to bring along software. I see this happening all over the place. That is exactly what’s happening in the RPO industry. The RPO providers are becoming technology providers. The.

00:15:59:09 – 00:16:07:06
So this is just this is just an extension of that because the market is hungry for better service.

00:16:07:09 – 00:16:43:28
I think for better service. But I also wonder if because I think what we’ve seen this cycle before I mean when we were doing talent management there was a lot of talent management consulting firms that were buying talent pools and then sort of went back to the technologies to get funding to be innovative need to be standalone technologies. Right. With that cycle. I released them through one of them I’m sure you’ve been through multiple them but the different I’m wondering that might change maybe that is that the data the information is becoming valuable and the only way to get information is not just pulling it in from your you because because I can pull a lot of data into my technology but unless I have a consulting function or an arm

00:16:44:08 – 00:16:59:13
That understands it cleans it puts a value to it. Right. That might be a difference I don’t know. I mean this is me sort of speculating at this point but it seems to me like the consulting component of this might add the value to the data that organizations might be looking for down the road.

00:16:59:13 – 00:17:18:09
That’s an interesting thing you could imagine. If that’s the case that what will happen is it will go back and forth some more times because every time the data volume changes we’re going to have a shift in you bought. If if what you’re saying you saw it works. I’ve got no reason to say that you’re wrong.

00:17:18:21 – 00:17:54:26
So that means that we’ll just see faster and faster cycles alternating between technology and service delivery and maybe that’s a good jump off for the conversation we were having before we got on the radio show which was about the idea of ethics and data management inside of organizations. There was an interesting article put out this week on how A.I. companies can avoid ethics washing now that the definition for ethical washing was a little bit sort of all over the place but it’s the practice practice of fabricating or exaggerating a company’s interest in equitable A.I.

00:17:54:26 – 00:18:26:19
systems that work for everyone while at the same time doing things that would be considered possibly unethical by selling their data or leveraging privacy information or not making it clear what kind of privacy processes they have within their technology environment. So the example that they put out in this particular article was Google’s most recent challenge with this were they had a high profile announcement of their Google’s external A.I. ethics panel which then sort of devolved into a PR nightmare when people started leaving when they felt like that they weren’t actually really serious about doing a AI ethic.

00:18:26:22 – 00:18:51:27
In that panel. So you know we were just talking about this idea of moving faster and faster. This process of technology being more important versus data data being named tag and then I need new technologies so then the technology or any new data the technology gets more important and I get different data set. Who watches all this and is there such a thing as ethics washing from where you’re sitting. Or is this just the process we need to go through.

00:18:51:27 – 00:19:05:03
You know there’s so much noise in the conversation that people work hard to find ways to stand above the noise as it’s washing

00:19:07:15 – 00:19:08:23
a terms.

00:19:09:15 – 00:19:12:02
I could just find out that its content.

00:19:13:01 – 00:20:02:23
Yeah well well so so. So I don’t know. It’s such a crazy world where everything that you do is examined closely the bigger you are the closer the examination. And in order to solve some problems you have to start little. You have to do it in a way that doesn’t cover everything and you have to be prepared to make mistakes. Right. And so so the idea that you should be suspect because you have an ethics program in your a that isn’t comprehensive and that there’s something wrong with doing that that it’s evil ethics washing is that this seems so naive to me.

00:20:03:00 – 00:20:40:25
And there are there are things you could see you could see things where people are putting ethics boards in place because they don’t have any choice. You can see that there are a number of ethics boards being formed being around H.R. on issues like video interviewing where if you want to stay in the market there isn’t any choice but to start to have an ethics function. But you’ve got to give it a chance even you know many of the ones that I see are ethics boards built of people who are all in and close to the company and that that doesn’t get good questions yes.

00:20:40:26 – 00:20:53:04
But you’ve got to start somewhere and this this idea that the kicking people because they’re they’re not perfect in their ethics best execution like it’s right.

00:20:53:11 – 00:21:29:12
How about you Well I get what you’re saying but I think we push back a little bit because I think there is a particularly coming out of the technology space right. I can remember back in the days when we first before sort of networks were assumed you know sort of the thing that was going to run our companies right. People putting together these technology boards a similar kind of not exact concept but the idea that I had to really understand where technology would go in my company right and how it might be used I needed some external audience to sort of tell me that right or some external influence to help me with that conversation because internally I didn’t have the skills that

00:21:29:24 – 00:22:01:08
I pushed back a little bit on that and that I think the people inside your company are the most important ethical firewall. Right. They’re the people working on the product. Day in and day out and what. You know this particular article and it and I think that the conversation you know that’s been sort of had as your ethics conversations should be designed to build into your company. If you really want to make a change in anything it has to be built into your business model. So there are financial and performance driven outcomes for being more ethical in some way.

00:22:01:08 – 00:22:32:02
Right. And there are some roadblocks that people within your company set up and you know are being held accountable too. And I agree. You know moving small and getting started doing anything at all is better than doing nothing. But I think that it should be much more of an internal conversation than an external branding and marketing effort. And so for me. That makes a lot of sense in this conversation and I think it’s it’s by calling it nothing ethic blushing which feels just like icky all the way down. It makes you think a little more about it.

00:22:32:03 – 00:22:33:07
Do you of them.

00:22:33:10 – 00:23:04:12
Well I hear what you’re saying. And so so it’s unlikely that the people inside of your company are going to have the answers to some of these questions. It’s unlikely that you have some of the questions. Right. And so if you say that the your people are the front line and that’s that you just set people up for failure because you people are supposed to be doing their job and a good ethics program means thinking about things but don’t get the job done

00:23:06:09 – 00:23:42:13
right. And so and so there’s a difference between the sort of strategic mindset that’s required to to really see ethics in the strategic ones it requires that you see that far outside of your company and the sort of on the ground tactical behavior that you work to get the company growing. Right there’s just a difference there and you can’t see things like the real cost of starting a system in your company is not something that you can know internally and cheating the real cost men by cheating.

00:23:42:13 – 00:24:13:02
I mean not anticipating all the places where the expense will occur is not something that you can really understand internally but it’s got big consequences for the company. Understanding that machine learning programs fail they predictably fail. Every single one of them fails. And so knowing what to do and having the staffing and resources available for handling the failure within companies that’s a that’s a problem.

00:24:13:06 – 00:24:18:00
I don’t know how you get people like me inside to know that without a great deal of training.

00:24:18:01 – 00:24:53:08
I want to highlight that cause because this is part of that earlier conversation we had and I thought it was quite fascinating what you’re saying because I mean most of us this article and most the conversations we we hear come out day after day about the ethics issue right. Is that it’s about bias and about diversity and about sort of whether or not you should sort of ding employees for or for the time they take to go to the restaurant those kind of things right that are that feel very close to sort of you no. Should we be listening to technology or will the technology actually you know basically just re-emphasize what is already our bad trait.

00:24:53:10 – 00:25:12:20
Right. But you’re actually saying there is a different type of ethics conversation which is the impact of tech and say this appropriately. Impact of doing this in a way and how it impacts your business if you’re not prepared to do it right. That that it’s unethical to be doing it without having things in place to do it right. Right.

00:25:13:03 – 00:25:54:19
Well let’s say in a magical world that there is a system that you can install that does your hiring for you or some significant part of your hiring where you has massive influence over your hiring. The question that you have there is how do you tell if it’s doing its job right. And the answer to how you tell if it’s doing its job right is a feedback loop that’s a year or two long. And so currently I think there probably some easy ways to make the feedback loop shorter but you tell if a hiring process is doing well because the hired the people you hired are still there in a year and people are happy with them and that doesn’t work very well in recruiting currently so people are recruiting for all sorts of stuff.

00:25:54:27 – 00:26:26:12
But if you can’t tell when the system is broken and you know it’s going to break so you can’t see the consequences of it being broken because you don’t know when it’s broken installing without a an infrastructure of testing and inspection and examination so that you can be aware of when the shift happens. That’s that’s a cop out. Well that’s putting your investors money at risk without taking care of it.

00:26:26:16 – 00:26:59:09
Right. But so ethics ethics include fiduciary responsibility. Right. It’s not just problems with categorization and the problems of implicit bias flowing but there are many other ethical issues. And it’s really hard to get your arms around what they are. I haven’t seen anything anywhere that does a good job of saying here is with all the ethical issues are which means that anybody who starts an ethics program in their company is going to be subject to the kind of PR problem that Google has.

00:26:59:13 – 00:27:02:13
Oh here’s an ethics problem that you consider your ethics.

00:27:02:15 – 00:27:11:15
Therefore your ethics program is defective. Oh my God. How do you how do you function with that.

00:27:13:09 – 00:27:19:19
Welcome to modern day social decision making process right. Oh yeah.

00:27:19:22 – 00:27:38:11
Yeah well. Well it’s it’s it’s it’s quite challenging. And if you if you let it run and you get really conservative in your decision making as a result that’s an ethics problem. Right. Giving your decision making to conservative puts your company out of business.

00:27:38:13 – 00:28:14:27
I like your. Your comment on that funny serious side of the ethics. If not we have a lot of patisserie rules about checking like not running a company into the ground for one reason or another and for decisions around ethical issues in your entire technology could definitely cause you I mean you know Amazon is an interesting example of an organization right now dealing with our machine learning being part of it. If they’re warehousing practice right now which is driving huge pushback and all of them and they have lots of strikes around is that the conditions in those warehouses because of the machine learning processes that they workers are sort of being held accountable to you.

00:28:15:01 – 00:28:21:21
I don’t know that that’s exactly the same thing you’re talking about but I think that there is a lot of conversation that it is.

00:28:21:21 – 00:29:03:19
It is though. I mean it’s becoming clear we don’t know a whole lot about what happens when you put machines and human beings together on teams but one of the things that’s becoming pretty clear is that if the machine gets all of the easy work the people burn out faster than you can’t give machines complicated work they can only do easy work. So what happens when you put a machine into a system is it rearranges the work and it causes dissidents in the established retention and promotion veterans and what makes a great boy and what makes a hypertensive employee and understanding those implications.

00:29:03:19 – 00:29:25:10
Those are huge ethical issues that involve the future of the company rather than just the company’s treatment of the employees who are there. There’s just is that there are two sides to the script on this one is about the success of the company and the others about the success of the people who are in the company well on that note we have blown through our 30 minutes.

00:29:25:11 – 00:29:38:26
Boy what a fun conversation John. I mean these are things that. Hopefully are being discussed in the halls of every product development and company management level. But these are things that they’re not talked about. They should be talking about them I think.

00:29:39:03 – 00:30:00:23
You know we’re. We’re having fun here. Stacey thanks. Thanks for doing this. What a great conversation. So you’ve been listening to HR Tech Weekly, One Step Closer with Stacey Harris and John Sumser. Thanks for tuning in. And we will see you back here next week same time. Have a great day everyone.

00:30:01:18 – 00:30:12:26
Bye.