The Use Case Podcast: Storytelling about Panalyt with Daniel West

William 0:25
Ladies and gentlemen, this is William Tincup, and you are listening to the Use Case Podcast. Today we have Daniel West on from Panalyt. And we’re gonna be talking about both his company and also his experience. Daniel has a fascinating background. So welcome up on LinkedIn, obviously. But he’s been on both sides. So he can actually, and we’ll definitely talk about this, but he who he can actually talk about both sides about being a buyer of HR and TA tech, and also a seller, if you will. So with all of that backstory, Daniel, would you introduce yourself and Panalyt?

Daniel 1:00
Yeah, thanks very much for having me on and really appreciate it. Yeah, my name is Daniel West. I’m one of the co founders and the CEO at Panalyt. The foundation the origin of the product really comes from my background, as you say I was in in house HR for over 20 years. I was head of HR for Apple in Japan and Australia, during our the the the the the later iPod boom in the first iPhone boom. And then was then with Apple in the US in Cupertino running HR for the US sales operation and the global online store. And more recently, I was with Uber during the super high growth phase I was head of international HR, we went from a few 100 people to several 1000 over the course of 18 months or so. And I then did HR consulting, basically trying to replicate the the the the Uber growth with a large number of startups, high growth startups, I was working for VCs and investors that would parachute me into to their, you know, most recent investi company. And so I got a lot of exposure to Super data driven organizations, very high growth driven organizations. And, and I was in the position, particularly during my consulting a couple of years of buying the HR tech stack for those companies and trying to replicate what we had to do, but which was very, very good investment in HR tech very early on. But I can’t, I started to encounter a problem that really plagued me my whole career, which is getting your hands on the data to support the business and working for these very data driven startups, managers who are used to data and used to having data at their fingertips to make commercial decisions. And HR tends to be still a relatively relative black box of data. We have this explosion in in SAS HR tools. But there hasn’t really been especially when we started Panalyt, what, three, three and a half years ago, there weren’t really tools on the market that would easily plug into all of these different SAS HR tech tools that you’ve bought, plug into them extract the data and put it into useful and meaningful visualizations. And so that’s really what we’ve built Panalyt to fill that gap. And so what Panalyt does is we we connect to the API’s of all of your sources of people data, we consolidated all in a custom built data warehouse that we built specifically to house and support people data, and that we built that data warehouse to deliver data very, very fast to a custom built front end, where we’ve pre designed dashboards to help managers understand their people data better, and help them make practical people decisions on their day to day, people management challenges. And that’s been the core of the tool. And what we’ve been doing in this last year, during this whole COVID quarantine period of people, this explosion of people working from home is that we’ve dug into another source of people data, which is communication data, that data from G Suite, Slack, etc. and pulling that communication data out to layer in how people connect to each other the relationships that people have, and the activities, how much time people are spending in meetings on email, etc. and melding that together with that people data to give managers an even richer view over how their how their team was responding to COVID. So yeah, so that that’s me and that’s the the Panalyt tool.

William 4:29
So when you say that I hear, for whatever reason I hear BI for HR.

Daniel 4:35
Hmm, absolutely. Yeah. It’s BI for HR. It is the data pipelining the warehousing and the visualization. It’s not a we don’t think of ourselves as a competitor to anything like Tableau or Power BI where you have a data scientist who’s building completely custom dashboards for the C suite. We have pre designed dashboards and visualizations where you don’t need a data scientist to build this stuff out, you don’t need a lot of tech resources, because I know from again, having been the buyer having been on the inside, I never had any access to tech resources. No one was no one who’s no no business leader, who is short of developers for their front end of their actual product is going to give me any desktop. So and also, I’m never going to get data science time from, again, from the very get that away from the front end of the business. So you need a tool that is out of the box already set up to just absorb the data from any of your sources, and deliver it to you in in pre designed visuals that will work that will do all the calculations correctly and get all of your business partners and the line managers they support all on the same page as to what their headcount is their attrition, the gender balance, gender, diversity, all of those fundamentals. And then we’ve extrapolated from that to start building out more predictive analytics, attrition risk connecting the correlations between performance engagement, compensation, etc.

William 6:03
So, because you’re rolling everything up, that would show you current and historical. It does Panalyt currently, or do you see it in the roadmap to do forecasting and predictive?

Daniel 6:18
So the predictive stuff, so there’s some simple forecasting around we can forecast your your headcount, your staff costs based on historical trends. So you can choose linear trends, you can choose a more sort of smart model where it’s looking at, if you have connected your recruitment tool, it’ll look at what your approved wrecks are, what salaries are attached to those kind of racks, and then start to predict it on that basis. Or we’ll just take a linear of the last 12 months of your salary growth and extrapolate that. So that’s the simple forecasting. And then the more more complex predictive analytics, we started with attrition. So we have a predictive model that will predict your attrition over the next six months, you’re the individual risk that someone’s going to leave over the next six months, based on a basket over a dozen different data attributes that almost all of our clients have they one of the main data inside,

William 7:13
Sorry to interrupt, Daniel, is, is that data inside? All the different HR systems? Or some of that? Are you using external daily like, recommendations? Or something like that?

Daniel 7:25
That’s a great question. So we made a decision early on that we were going to focus on verifiable data that the client already has, so we’re not pulling in data that may or may not be correct. We’re not pulling in crowdsource data or LinkedIn data that you can’t validate, we want to have the foundation data that we’re using to be sort of absolutely concrete from from the client systems. So we really focused on leveraging the data that the average client will have, as far as possible. So So if you take something like if we know who the manager is, so that it’s not just a predictive power of just does that manager have a high attrition rate overall, therefore, his people will have a high attrition risk? It’s what’s that managers historical attrition, based on certain gender a tenure roles? How many managers have you had in the last year? What’s that managers own risk profile. So even just with a single data point, we can extrapolate seven or eight other data, data, data points. And we think that’s much more powerful than we think of spending a lot of our time trying to pull in data that we’re not even sure is accurate?

William 8:34
Well, it’s also your again, position by position, you’re looking at some of these different indicators. So for recruiters, if a recruiter hasn’t logged into their ATMs in a month, that’s one indicator. I mean, again, one of many indicators that maybe there’s a problem or a problem on the horizon. I want to go backwards real quick to both your apple and Uber experience and not getting into the weeds. But I know, I know people have have questions, but they also have kind of a fear of scale. So going from that, you know, 100 to 10,000, or whatever it is, you know, you’ve done it a couple times. And you’ve seen it a couple times. So, like what’s your what’s your best advice for both TA and HR as they think about scale?

Daniel 9:27
Now that that’s Yeah, that is the question I’ve been asked most most frequently in my careers. And since you’re doing it, yeah, you’re right. There aren’t many experiences like like Uber, where again, when I when I came into the international part of the business, we were maybe 300 people outside the US, and by the time I left, we were four and a half 1000. And that was 18 months. I mean that like that. The first piece of advice is don’t do that. That’s no, no organization can scale that rapidly and do it well. Or at least if you are going to do it. Just accept that not everything’s gonna go smoothly. But but but yeah, the main advice is invest in technology early in, believe in Believe in yourself believe the company is gonna scale that rapidly and have a clear plan about what your what your scaling is expected to be, and, and invest in technology ahead of time we invested in workday very early. And also, we hired our workday implementation manager, which meant we actually got the value out of workday, which by adding very few companies do actually get the full value out of a workday implementation, we put resources into integrating the tools like connecting them together. So really understand your recruitment, to onboarding to, you know, to being an active employee, understanding that workflow and automating it again as early as possible. And investing in those resources. That means you’re making tough decisions. And I think, again, being brave to make those tough decisions, you’re not going to get to invest in training and development, you’re not going to get to invest as much in business partnering, you’ve got to invest in the processes to support hiring and onboarding early on. And again, that will lead to other problems, but you have to make those tough choices. And from a tech perspective, what I think we didn’t do at Uber, which is again, work part of where Panalyt comes from, is we didn’t invest in, in the the the output of all of those systems, getting it into the hands of the people that had to make the day to day decisions. And so we did end up going down sort of blind alleyways in terms of massively over hiring and one section but ignoring the other because we weren’t as in touch with the what the candidate pool was or where the business was going. Or, or where we were losing people.

William 11:40
I’ve heard that before. phrase is flying blind.

Daniel 11:44
Yep. Yeah, we’re moving so fast. Yeah, you’re moving so fast. And if you don’t have people or systems, pushing the data to you, or making it very easily accessible, you don’t have the time to stop and look around.

William 11:57
Something. That’s something that we did try and do it real quick, just so not semantics. But it’s not just data. It’s inside, right? Oh, yes, lady. Yes, yes. I know that I know what you’re saying data that I believe you’re going you’re you’re really transferring over into, I want to get you the inside as fast as possible. So that then you can write informed data driven decisions. Okay. All right.

Daniel 12:22
Well, this is this is about there’s these two concepts in people analytics that I think is science are starting to emerge as the strategic avenues, you can go down this idea of the push strategy, where you’ve got a small group of like data scientists, specialists, who are they’re thinking of, or what does the business need to know. So we’re going to do a project and then push those insights out to the business. And, and we’re where it falls down. And we’re definitely fell down at Uber, where we had some of those team members, but they didn’t have any mechanism to push those results out, there was a mechanism to get it out to the hundreds of managers that needed it all there’s that pull strategy, which is let’s put the data out there in an interface, where people who actually are running the business have their hands on the business, they can derive their insights if we can make the data accessible to them in an easy to understand and accessible way. And obviously, that’s what that’s directly what we’re trying to do with Panalyt is democratize that data to enable that pull strategy to work. So in some some cases, I am talking about delivering insights. But in some cases, I am actually just talking about delivering data in an easy to understand an interrogator way for a manager on the ground will have a hypothesis about why he’s losing people, he should be able to interrogate the data and explore the hypothesis very quickly, and and derive his own insights. And that’s what we’re trying to do with Panalyt.

William 13:49
Do you feel, HR or TA? Do you think we are terrified about dirty data?

Daniel 13:55
Yeah, like, yeah, I think we’re way too overconcerned. I think we’ve been trapped in this idea of reporting. I think that’s very true. For every TA, leader I’ve worked with, they know they’ve come up through the trenches, and they’ve been beaten over the head that they need to be reporting, reporting, reporting. And if there’s one one data point wrong, they get hauled over the coals for it. And we need to make this transition to well, a I think leaders need to understand lead leaders and, and HR on the ground, we need to all embrace the idea that, that insights analytics isn’t the same as reporting. reporting needs to be accurate, but but analytics doesn’t need absolutely accurate data. You need you need broad sweeping data sets so that you can you can understand trends and and derive the insights from the trends. It doesn’t matter if you know two out of 100 people haven’t got their manager entered correctly or they don’t have their their the start date entered correctly. You know, you want to get that stuff right. Don’t get me wrong. You want that stuff to be right, but it doesn’t mean you can’t Don’t get the insights out of the data, if those couple of data points are wrong. And I think we’re way too scared of the data being unclean, and we don’t even start the the data analytics journey. And the other thought of this is that sunlight is a great disinfectant, right? You, your data will never become clean in time you start using the data and understanding where it’s wrong and then fixing it and, and seeing the result of it.

William 15:28
No one will want to start, because I’m scared that it’s dirty.

Daniel 15:33
Yeah it’s too dirty for me to show anyone so I’m not even gonna look at it. But if I don’t look at it, I can pretend that it’s clean.

William 15:40
It’s like my closet. No different totally, totally different. Totally. Okay, so a couple things what, as a practitioner, but also now, as a as a vendor partner, selling into your former peers. What do we what do we just in general, what do we get wrong about people analytics?

Daniel 16:02
Well, yeah, I think that that point about I don’t, I don’t have enough data or my data isn’t clean is absolutely the biggest one, this idea that I don’t have enough data, we really try and get clients past that, if you’re paying your people, you have this rich, rich set of very clean data you have your payroll data is much richer than you think it is. So we always start with that is that is that the first starting point is Yeah, I don’t have enough or it’s not clean enough. The second part is we’ve got too many other projects on and we can’t think about the analytics. Because next year, we’re gonna have a new hrs, let’s wait until then, or we’re about to change our ETS, let’s wait until then. And the thing is, you’re you’re you’re just delaying your ability to start educating yourselves and your stakeholders on understanding this data, a tool, whether it’s Panalyt, or any of the emerging competitors were built to plug into any tool. So if you switch your hrs in six months time, it doesn’t matter to us, we just switch the API, you delaying your journey of educating yourselves in the business on the on on leveraging analytics and democratizing the data. That that is an error. Because every day you delay is a day you don’t get back. And you’re always going to have another project, right? There’s always going to be another tool on the horizon or your job.

William 17:23
It’s firefighting. So there’s another fire right now burning.

Daniel 17:26
Yeah, they will be burning tomorrow and trying to get and I think people don’t have any problem admitting or understanding that, if they had our tool in place, they would be able to fight their fires or even avoid their fires, there’s so much more effectively, but but they struggle to see they struggle to to, I think, prioritize correctly about saying, let’s get these firefighting tools in place. First, they kind of want to have room to breathe. I think the other thing about people analytics is what’s so different about buying a PA tool versus buying another ETS or other hrs, it’ll probably be the first time they’re buying it, you know, we’re almost never replacing an existing people analytics tool, we’re almost almost in 99.9%, we are being brought in to replace nothing, or maybe being bought into replace a bad use of Power BI or you know, a misuse of tableau. You know, it’s not a tool that’s designed for doing what they’re doing with it. And so I think that is also they don’t, they don’t know how to make this choice. And I think that also gets in the way.

William 18:36
So let me ask, and this will sound like a trick question. But it’s not meant to be that way. But who should own people analytics? And the reason I asked this is kind of a backdrop of just discussions I’ve had in the past with people analytics, workforce analytics, folks, mostly, is that it’s kind of a combination of operations, finance, and HR. Yeah. And everyone well, has some type of interest. But then it’s like, well, who owns it?

Daniel 19:03
And the central and the central business data team. Right. So there’s often a business analytics group. Right. And I think I don’t think that I don’t think that there is a right answer. I think pends massively on the nature of the of the organization, the size of the organization, the complexity, I think what we’re seeing, we don’t, we don’t believe that there’s a massive difference in industries particularly, but we do think there’s a method on how to use people analytics, but I do think there’s a massive difference in employee makeup. So if you have a large group of retail workers, logistics workers, field workers, that is a very different employee makeup than that an organization like Uber, for example, or you know, or Twitter or organizations where, you know, it’s at present developers and everyone’s in an office, having a large group of retail or field staff changes the dynamic tremendously. And I think things like that should should help you to understand where the responsibility for people analytics should be sitting and and how close to the business it should be. But But, you know, fundamentally, I think it should be whoever whoever’s got the the passion and drive to take on this democratizing journey who’s got the right leverage to say to the business, you really, you know, you should be getting your own access to the data, and having that drive to push it out. And I think it’s, it’s got to be driven right now by by who understands the opportunity best. So I think I’m not particularly dogmatic about which team should own it. Right. I think it’s got to be driven by by knowledge, experience and confidence.

William 20:46
And with some of the workforce analytics plays, it’s it’s you see, cross functional teams come together.

Daniel 20:52
Oh, absolutely.

William 20:54
Finance learns from HR, HR learns from ops, ops learns from the data science team. You know, it’s, it’s, it’s, we, no one has all the answers, but we can all look at the data and then discuss, like, what do we see?

Daniel 21:09
And that’s exactly what’s been missing is that few, so few companies have got a platform where you can all look at the same data in the same place where you can put people data together with finance data together with commercial data in a single place.

William 21:22
And the larger and the larger the company, the more complex I mean, I’ve had nearly a lot of global payroll, payroll providers as, as friends and clients in the past, it’s like, you know, simple question from the CFO of like, how many employees we have to a company like Bosch 700,000 employees and 18 different payroll systems and all this like that? That’s actually a hard question.

Daniel 21:45
Yeah. And the answer the CEO hates is, Well, it depends what you mean by employee. They hate that they hate it.

William 21:54
Can I get a week. Can I get back? Well, I hate that as well. But but to be able to just roll that up and go, Yeah, okay. Currently, as of right now, here’s where we’re at.

Daniel 22:05
Yeah, well, it’s exactly the same. It’s the second same between business units, and between, and a lot of my experiences is operating internationally. So between what what a country and Brazil things that employs 500 people, but on your data, it’s 200. People. So what Where’s that? Where’s that difference of 300 is very often is not a small difference.

William 22:27
Now you unlocked this, this idea. Okay, staff, you know, you know that you’ve got full contractors, exactly. You got all these types of things like, well, are they employees? Are they?

Daniel 22:38
And they might be on a different system, because locally, you need to pay them differently. This is why and the pain that the you know, an HR business partner faces is not necessarily understanding that you can understand it, but you you know, that every time you want to run a report or build a build a build a piece of analysis for your clients, you have to extract the data from all those different places manually, you need to put it all together manually, everyone’s got the sort of five or 10 item, little checklist of all the things they know they need to change in the data every single time to make it slightly more more correct. And then you put it together, and then you put it into PowerPoint, etc. And we’re trying to help clients to go through that just once. Just do that one with Panalyt. And you won’t have to do that again, because it’ll just, it’ll just open up correctly in Panalyt.

William 23:30
So years ago, I read this book, lean analytics, and it came out of the Lean Startup movement. And it was basically about how every business is unique to that business and probably unique to where they are, in their maturity has one metric that everything watersheds for, from Yeah, yeah. And for like Airbnb at one point, it was number of uploaded photos. So they, you know, they could look at the health of their business that day, that week, whatever, just by that number. And and see, again, there was a lot of other things that are measuring. When you look at people analytics. I know you probably get asked this from practitioners. Is there a magic bullet?

Daniel 24:10
Yeah, one critical number.

William 24:11
metric that they should be?

Daniel 24:13
Yeah. That I think is fantastic question.

William 24:16
Put their finger on a pulse. Is that Is there one more? Is it Yeah, go? Again? I know, it’s a tough question. Because

Daniel 24:22
Yeah, no I think it’s a great question. It does come up and it’s something that we put our minds behind. We’ve put some effort in recently into doing what we call the the people balance sheet. So essentially taking out that finance balance sheet format, and putting in all the critical metrics for HR but but that you you’ve your you and your team, your company, you will select the critical metrics for you. And you’ll have them in a very easy to understand balance sheet format. And I think that is the start of the process of understanding what your critical metric is. Because I think again, when you get all the stakeholders seeing the same balance sheet, you will find and I found over time with commercial meetings, you know, whenever I’ve been in sort of senior leadership meetings, you will see a sort of a gravity around one or two numbers, one or two lines that everyone starts focusing on. But what I have noticed is that number that they all that they’ll all focus on, will change will start to change between maybe between q1 and q2 or Macrae, v2 might be the same one, but then q3 and q4, we start focusing on a different number. And so I think I would shy away from having to from advising clients that there is one key metric, but I do think, but I but I would advise them to say there is a limited number of metrics that you can get everyone to focus on. So So understand what what is driving your people challenges today, and drive and identify the couple of metrics that get people to understand and be aware of that issue. But then develop the quality of all of your metrics so that once you’ve solved that challenge, or move the needle on that challenge, you can move them on to the next metric, but what we do advise in the sales process is, is not to try to boil the ocean is what people say, Okay, once I’ve got an analytic tool, I can do this, and I can do this, I can do this. And we go, Yeah, you can.

William 26:19
But the question isn’t can, it’s should? Yeah, it’s funny. 100 years ago, I did an HR metrics conference in Miami, and one of the bits that I did is probably 70 people in the room. And I basically said, Okay, I’m gonna give you some time, you’re all at your tables, so you can discuss, but I’m going to go around the room. And I’m going to say, if you could only measure one thing, what would you measure? And why? And you give people context, in terms of your company size and industry, but you know, you’re not much else other than that. And, and, you know, 70 people, you know, 85 different answers. But one of the things that we got to, which I thought was fascinating was regrettable turnover. Yeah. Now, that is a great one, right? So not just turnover, like turnover. Yeah, you won’t regret retable. Yeah. So not to write two final questions. One is, it’s buying people, analytics tools, being buying pallets. Let’s just make it real specific. What are the questions that you love hearing from practitioners and the questions that they should be asking of Panalyt?

Daniel 27:28
Yeah, yeah, absolutely. The The, the the questions that we do like to hear, you know, reveals that they are sort of thinking about analytics in the right way. So so when we get the question of how do I solve this particular issue when they’ve got a particular challenge, and it’s like, this thing is on us right now, how do we use data to solve this? That’s a great, that’s obviously a great opportunity. But then And then also, when there’s an openness to to our advice, so so when we get asked, what other data point could we be gathering that’ll move the needle? And actually, it’s interesting, you mentioned the regret attrition, because that’s often the one I go to, like, are you measuring regret, attrition? And the answer is invariably, no, actually, it’s still a minority of teams that are measuring that. And I would go look, if you could just gather one more data. But if you go back over the last six months exits, and just just do regret a non regret, you are going to find implementing Panalyt you’re gonna see so much more power from the attrition analysis.

William 28:26
Because turnover does become a beta that is like, the word turnover to most HR people. It’s like, Oh, that’s a bad word. Like No, not really. Trees in the forest die. It’s okay.

Daniel 28:38
You’ve got to, you’ve got to go start distinguishing between that positive turnover, and that and that negative turnover. And then really, you gotta you got to really focus on that first three months first year turnover, because that’s indicating, like a whole other set of issues. But, but no, I, but those two questions, how do I solve this issue? And what are the data? Could I be could I be collecting? So those are really, really,

William 29:02
I love both. But I love the second question, because then thinking you have you as a partner. Yeah, you don’t? I mean, it’s Yeah, you’re buying software? And yes, that’s really good stuff. But but it’s saying you’ve done this a, you do this, you see it all the time. What do we not measuring? What do we not thinking about what’s around the corner, you know, that type of stuff.

Daniel 29:23
Our whole wholesale is about that, that that that partnering approach and discovering what they’re going through, and then getting to see us as partners with my one of my co founders, him heads our Japan business and and she’s ex Google people analytics with Google for three years in Japan in the US. So between her Google experience and my apple and Uber experience, we recover a lot of great brand names that people want to hear from so they were able to. Yeah.

William 29:50
Yeah. All three of those companies figured and figured it out on some level. So great representation. Last question. And and I know it’s a question you get off asked questions. Often is, how do they you know, you’ve built business case, you know, you build cost benefit analysis for for the, your chro, your CFOs, or anybody else you’ve dealt with, but how do you? How do you render? How do you talk to them about the ROI people analytics?

Daniel 30:19
The buying process, the buying process, I think for HR has always been difficult. It’s always been difficult for HR, it’s easier somewhat for ta but but in general, it’s always difficult for TA and HR to, to show that ROI. I think the whole buying process has always been difficult, but I think it’s only just gotten harder and harder. Over the last few years, when you when you also we start factoring in the the technical hurdles, and the data privacy hurdles that you also have to, to jump over just to get to the point of, of even starting to talk about the commercial advantages. I think the the how we build how we advise our clients to build the ROI around Panalyt. There’s a couple of really obvious low hanging fruit. So obviously attrition is a is a key one, if you can understand your attrition, if you can leverage our attrition prediction tool, you can, you can start to lower your attrition. And obviously there are there are calculation methodologies to understand what attrition is costing you. And so if we can reduce your attrition, then then obviously, there’s an immediate cost saving, we also have a tool that that starts to understand the connection between your recruitment behaviors, and the first year success of employees. So who the hiring manager is, what the source is, who the recruiter is, how many interviews they’ve had, what the average interview score is, all of those inputs, correlating that with first year performance and first year attrition. So again, if you can show that I can, I can improve your first year performance if I can help you to improve your recruitment processes, so that instead of losing 10% of your new hires, you’re only losing 2% of your new hires. There’s an immediate cost saving that so we so we help them on those kind of obvious cases. And then beyond that, it’s back to that question of what is their immediate pain point, if you can identify what the immediate concern is of the CFO or the CEO, then you can tailor to that and for CFOs is very often about, especially for high growth startups. It’s about spiraling unpredicted staff costs. And then CFO wants to understand how or wants to wants to have a better predictive model on how staff costs are growing and a more awareness of how your new hires and salaries are increasing your new hires, how that’s gonna impact is, you know, q1, actual cost q2 actual costs, and we have a forecasting tool for that.

William 32:48
In general CFOs hate variable cost.

Daniel 32:52
They they held they they hate unforeseen pieces. And so if you can start to if you can show that a as an HR team, you’re aware that everyone you hire is impacting their, their the cost model, but also that I can start to show you predictions on you, we’ve already seen an X percent increase over the last X number of months in our, in our salaries, therefore, whatever you’ve budgeted for q2 is going to get blown out because of this trend. But not just telling the CFO that telling him that he will have his own access to it, he can just open it up and see it himself. But I think also what frustrates CFOs is they that HR is often a black box to them. And if you can open up that black box in a in a secure no secure and confidential way, and give them the data they actually need, then then you’re you’re making a real friend there.

William 33:46
And the beauty of that is also they might see something that you can’t see you don’t absolutely right. And so I lied and said it was the last question. But now I’ve got to ask you about quality of hire. Are you being asked? Because I get asked a ton about quality of hire as a mentor? Are y’all being asked about quality of hire?

Daniel 34:04
Yeah, that’s specifically what we build this. There’s what we call the recruitment outcomes tool. So so we’re interpreting, we got a fairly, let’s call it specific definition of quality of higher ROI looking at that first year outcome. So within the first year, are you a high performer, a low performer or a top quarter second quarter performer? Did you get a pay increase? Did you get a promotion? Did you quit? If we are connected to their communication data we can show you in that first year AI, are people becoming engaged with the company? are they building the relationships inside the organization that you see in high performance? Or are they actually still isolated even after six months or a year inside the company? And some of that is of course, impacted by onboarding, but you can correlate it back to who the hiring manager was how many interviews they had, who was on the interview process, who was essentially selecting this person and who was also selling them on what working at the company is like, and therefore setting the right expectation or the wrong expectation. So it’s about selection. It’s about the candidate quality, but the quality of the hiring process that’s going to produce that good outcome versus a negative outcome. So yes, it’s a great tool,

William 35:20
You use the metaphor about light being an answer. I like that. And because eventually, what you’re wanting to things you’re trying to solve for, is bad managers.

Daniel 35:33
Yeah. And bad managers, bad processes. Yeah. Yeah. I mean, you can also you can have some very good managers that are just very bad at hiring. So it’s about understanding in what way are you a bad manager? And can we help you to be a better one, instead of just going you’re just bad manager. But what’s interesting about that attrition prediction stuff, you can identify managers who are, for example, just really bad at managing people who are remote, versus they’re great at managing people who are in the same office as them. Okay, that’s changed with this COVID period. But but it is, it is important to understand the strengths and weaknesses of managers. And I think it’s something that that he has been very, very poor at, because the data wasn’t immediately available and was just too hard. It was just too hard to do. We were trying to make stuff

William 36:23
Painted with the same brush. Yeah, absolutely. Bad manager, good manager. And

Daniel 36:27
You might know the dangers in there somewhere but the you know, you don’t have the skills to go and get it and you’ve got like a million other things to do. But if you can just literally just look at one chart, and it’s telling you, then you can do something about it.

William 36:38
I love it. Daniel, we could talk for hours. Thank you so much for coming on Use Case Podcast. I absolutely appreciate you. I love what you’re doing and Panalyt

Daniel 36:49
You too, I really love what you’ve been doing for years now William and I if I can just take a minute that a while ago, I don’t know if you still do but but several years ago you were doing or were involved in a annual review of the HR tech market. And and I found some of your your work right? It would be about four years ago now when I was putting together my first investment pitch for Panalyt. And I ended up using I definitely use some of your some of your writing in my in my very first pitch deck, which got us the initial funding for Panalyt. Back in the day. I said I kind of always followed you since then. So but I really appreciate all the work you’ve done to to raise up this this profession that we’re in so thank you,

William 37:33
Well, it takes a village So thank you, thank you for what you’re doing and I appreciate the compliment and kind words and and also thanks for everyone listening to the Use Case Podcast. Until next time.