Keith Goode has been the Vice President of Client Services at ZeroedIn for more than 10 years, where he leads the implementation and support efforts of the ZeroedIn platform used to transform HR, talent and business data into actionable intelligence.
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Previously, Keith held various management consulting and corporate roles at General Dynamics, AON Hewitt and Saba. With over 20 years delivering human capital management and business intelligence solutions, Keith finds it exciting to deploy tools and techniques such as Data Mining, Collective Listening, Machine Learning and Predictive Modeling in a platform that solves real world HR and business issues.
What you will learn
- Discover how AI is revolutionizing HR by turning data into actionable insights for better decision-making.
- See how workforce analytics can help organizations uncover valuable insights from historical data to improve their human capital strategies.
- Learn how analytics can supercharge learning management systems, helping employees get the right training to meet company goals.
- Find out how AI models can identify organizational trends and predict future changes to stay ahead of the curve.
- Explore how data-driven insights can help find the perfect balance between remote and in-office work in the post-pandemic world.’
Transcript
Jeff Bullas
00:00:06 – 00:00:58
Hi, everyone and welcome to the interview with Keith.
Keith has been the Vice President of Client Services at ZeroedIn for more than 10 years, where he leads the implementation and support efforts of the ZeroedIn platform used to transform HR, talent and business data into actionable intelligence.
‌Previously, Keith held various management consulting and corporate roles at General Dynamics, AON Hewitt and Saba. With over 20 years delivering human capital management and business intelligence solutions, Keith finds it exciting to deploy tools and techniques such as Data Mining, Collective Listening, Machine Learning and Predictive Modeling in a platform that solves real world HR and business issues.
Jeff Bullas
00:00:59 – 00:01:17
Welcome to the show, Keith. It’s a pleasure to have you here.
Keith Goode
00:01:17 – 00:01:21
Ah, Jeff, I’m really looking forward to our conversation today.
Jeff Bullas
00:01:22 – 00:02:04
So we both, as we dialed in and we fired up the screens and we looked at each other from around the world, from the other side of the world at each other. Keith said, Jeff, you’re wearing the same watch as me, which is a garment which is to track our lives and capture data. So, uh and it’s true, I use mine for measuring how far and fast I ride, how high I climb on my bike as I uh pedal around my area and also do some uh hills and tours. So, Keith, what, what led you to get into? Hr I know you mentioned to me that you sort of stumbled into what stumbling into her looks like. Tell me about that,
Keith Goode
00:02:04 – 00:02:44
you know. Well, I, I have to admit my, my passion has really been uh lies around generating value from data. You know, we share the same, same watch. It’s, you know, data. I’m a data geek. I, I love uh working with data and, and specifically looking for the value that data can bring and, and just like Garmin can bring value to our, our workouts to our lives. Uh I’ve stumbled into how data and analytics can add value in the hr space. Uh It’s typically called workforce analytics, but ultimately, it’s providing additional information and elements so that people can make better decisions about their, their workforce.
Jeff Bullas
00:02:46 – 00:03:26
So we’ve got a very fast changing world with A A I coming into the picture now, which is being used in a very general way with chat GP T like it’s what we call a horizontal A I, it applies to, you can apply it to a whole range of industries. Um But within that sits with and underneath that sits the vertical, I suppose silos of different apps for different industries. So, let’s go back to data. And hr so you’ve been a data geek, what do you love about data? Is it just the insights is that you uh we and we also were talking before that
Jeff Bullas
00:03:27 – 00:04:10
data can be just, well, it’s the thing about information too. You can actually collect the data, but it’s making sense of the data that becomes where it really brings value, isn’t it? So you collect the data, it’s all this information. But what does it mean to us as humans to make wise decisions? Because there’s a lot of data, there’s a lot of information, but there’s very little wisdom and hopefully that’s what humans should be applying to the world. But we’re seeing maybe a little lack of wisdom at the moment in the world. But so how do you massage? Massage is maybe not a good word for data. But how do you take that data and make sense of it? I’d be intrigued by how that happens in the HR industry.
Keith Goode
00:04:10 – 00:04:59
Well, it’s, it’s interesting if we look in the hr industry and, and analytics specifically in HR, and today it kind of reminds me of high school sex. Everyone’s talking about it. No one really knows how to do it. Everyone thinks everyone else is doing it so they claim to be doing it as well. So, I mean, that’s kind of where we are today. Um But uh you know, I, I think um if you look at hr uh organizations been around for a long time has tremendous amounts of, of data. Uh you know, look at just the payroll data that’s happened in organizations over the organization’s lifespan. The people that have come on the tenure of many people and all the different changes that they, they uh acquire over their tenure at, at an organization.
Keith Goode
00:04:59 – 00:05:49
So all those are data elements that many in her just overlook. Um We see in her a lot of time, a propensity to change from one system to another, learning a matching system to another learning management system. Well, what happens to all of that old data? What happens to, you know, years and years of historical data that believe it or not? I consider it a gold mine. Uh Yes, it may take a quite a bit of effort of mining that, but there’s real information that can be told about uh an organization’s workforce by looking at that historical data and or co correlating data across those different silos in hr hr you know, is made up of learning, as I mentioned, um learning and development payroll comp
Keith Goode
00:05:49 – 00:06:13
ifs performance, of course your hr s system talent acquisition. So typically they’re housed in, in different systems but being able to bring that together so that an organization has one place to go where they can see their, their workforce and the historical workforce to be able to draw conclusions from that they can make better decisions in the future. And that’s really what it gets down to.
Jeff Bullas
00:06:14 – 00:06:56
Ok. So I mentioned learning management systems. So how does that apply to her cos I basically just tossed around. So what is this about internal training? In other words, do people score well on that? Are they interested? Uh Are you trying to pick up, you know, uh I suppose signals from data that reveal a bit more about the emotional side and intelligence of people because data can be pretty black and white in a sense, it’s very hard, it’s getting harder. A is maybe a bit in the same box. But how do you guys use analytics for learning management systems? And what is learning management systems? What does that look like in the brain?
Keith Goode
00:06:56 – 00:07:44
Oh, absolutely. So I think, you know, from an organization’s perspective, they’ve got a goal, a vision, uh uh you know, to, to get to a certain player. So how do they organize their workforce, their, their human resources to accommodate that? They can go out and buy that and, and hire those resources. Uh And they can train and develop those resources internally and that’s where learning management gets into. So when there’s, you know, when you can look across all those silos and say, yes, the best approach is let’s take these resources that are at this level and maybe with additional training and development resources, we can get them to this level where we need to, to be, that’s where uh L and D learning and development can really, really shine and, and play a huge role in that.
Keith Goode
00:07:44 – 00:08:27
So, from an analytics perspective, it might be everything from, you know, identifying those resources in which specific training and the types of training that can help elevate them at the right time at the right place. That’s the most efficient way uh to, to get them to the level the organization needs to be analytics can play a huge part of that. Uh And of course, we can then take that further and look at the historical aspect of the people who say they have been trained a certain way and we can feed that into an A I model to help predict how things are going to go in the future. And that can further refine an organization’s effort and time and resources that go into developing those people
Jeff Bullas
00:08:29 – 00:09:11
isn’t for me. Looking at being human and also my past and also my education in that, a lot of organizations go, we’re gonna cross skill you in the organization. We’re gonna make you a good accountant. We’re gonna make you a good strategist. We’re gonna make you a good person. You are gonna have all these skills. I have a problem with that in the sense that, um, we’ve all got different inclinations and aptitudes and to try and make, turn me into an accountant would be really, really ugly. Right. So my brother and I, he’s an accountant. I’m a bigger picture, strategic sort of guy.
Jeff Bullas
00:09:12 – 00:09:49
I know I annoy the shit out of him a lot of times because he wants to get to the point. I’m going, let’s look at the big picture. He wants to, I wanna look at detail and my eyes are glazing over and I’m running screaming for the hill. So, learning management systems is a little bit of an aside for, but it’s, it’s very much to do with hr is that what is there a movement to actually still do all this cross skilling and make everyone like, you know, Jesus good at everything or is it really about, uh yeah, identifying what people’s strengths are and just doubling down and make them be, you know, an Einstein rather than, you know, just a generalist.
Keith Goode
00:09:49 – 00:10:42
I think more, more of the, the latter. Uh It’s, you know, there, there’s a goal, there’s a need, there’s a uh desire. So how can analytics support and identify the most effective and efficient and cost effective resources and methods to get those people to that level? So, in your case, you know, analytics can look at, at your background, at your tenure at your um uh you know, history and with others and come up and provide uh hr with, you know, hey, this might not be the best candidate to get to that level and, and invest in that. Maybe there’s someone else that has a higher propensity based on what we’ve already seen in our organization. So, you know, it’s, it’s providing that flashlight in the dark
Keith Goode
00:10:42 – 00:10:58
for, for HS because you’re right in, in the past, it be like, yeah, let’s get everyone to hear and then only half make it well, suppose that you knew the better half that has a higher chance of getting it and you focus on that. Imagine how much savings you’ve just, you’ve just made.
Jeff Bullas
00:10:58 – 00:11:56
Yeah, and also might unlock some potential. Absolutely or a lot of potential. So here’s the thing I love about chat G BT, for example, is that it captures the intelligence information of the world. Um It’s the amount of data it is collecting is staggering and that’s why it’s so expensive and that’s why at the general sort of chat G BT uh Chatbot large language model end of town, the investment required is in the hundreds of millions in fact is going into the billions. So making sense of all that data is really what’s um cos humans generally been good for pattern recognition going, there’s something going on, there’s a trend emerging. What is it? Ok. So, I actually can be a great assistant to us as humans to identify trends of what’s moving and what’s changing. Um So
Jeff Bullas
00:11:56 – 00:12:21
Are you using A I to try and identify trends within an organization as well? Not just, you know, trying to predict from current data that, you know, what future may look like, is that what you’re also doing with A I, it is actually trying to get a handle on, there’s a lot of data here, but what’s really going on, what are the trends in the organization? Are we going backwards, forwards, sideways? You know, so I’m interested in your thoughts on that.
Keith Goode
00:12:21 – 00:13:13
Absolutely. So there are a lot of great classification models out there that we look into to help identify um and, and categorize uh individuals and, and their work efforts and, and say their career path even. Uh and we’re looking at, you know, the great thing about these models is it can support just a tremendous amount of data uh in order to help classify people say, um in in certain areas that will, will provide hr a a further insight into, into the workforce. So yes, we’re looking at classification models and A I prediction models which are fantastic. Um And take, for example, those prediction models not only do we predict an outcome with, you know, high confidence, but we started that and we’ve taken another step where we provide what’s called explainable AI. So
Keith Goode
00:13:13 – 00:13:59
After we come up with a prediction, as you know, uh an A I predictive model, it’s going to look at all the features that go into that model. And when it comes up with a given prediction, it may utilize some of those features higher or others than, say , another prediction. If you and I went through an A I model, it may predict us both as a high risk to leave the organization. But the information and, and the features that used in your model may be different uh may be weighted differently than the ones used in mine because the A I is able to learn across those, those elements as what’s gonna make the strongest prediction. So, it makes a great prediction. But what we found our clients were then asking, well, you know,
Keith Goode
00:14:00 – 00:14:42
there are two people, it predicted somewhat the same. But why, why did it, you know, predict the outcome, the same of these two people? What did it use? So we use what’s called, explain why to take that prediction back through the model and see how it impacted each, how each factor impacted the prediction. And that gives a lot of insights to people as well. Um It might be, you know, based off my age or my, my tenure gave me a higher flight risk as opposed to maybe, you know, in your case, it was, you know, some other uh you know, lack of training or, or, you know, your supervisor ha ha had a history of,
Keith Goode
00:14:42 – 00:15:07
of people leaving underneath them. So again, all these can be pumped into the model, but the A I model is gonna be able to know what features and factors to weigh heavily. And thus, when after the predictions are made, it can feed it back as intelligent insights on why a prediction was made. So 22 elements that are really important, um not just getting the prediction but understanding why a prediction was made.
Jeff Bullas
00:15:08 – 00:15:48
OK? That’s interesting. So I interviewed a guest a while back and we were talking about, he talked about the fact that he seemed to have a lot of his staff um leaving shortly after he hired them six months up to 12 and uh s a good friend of his, I think it was like a performance coach or, you know, entrepreneur coach CEO coach came in and watched him for a while and he said, you’re micromanaging your staff. In other words, you’re hardened to do a job. A and you’re telling them how to do it and they’re going, why am I here? Then you’re telling me what to do, right. So this observation by a human
Jeff Bullas
00:15:49 – 00:16:32
uh resulted in him being aware of what he was doing, which then led him to adjust his behavior, which then led him to find out that now he has people working for him 67 years later, because he actually now trusts them. He also then lets them do their job. His job is to help them with tools and so on. And then get out of the way. How does A I data? How do you collect that data to identify something like that? Because those discussions that micromanaging is done off, you know, off piece, it’s actually not done in front of a computer generally. So how would you identify that? I’d be curious.
Keith Goode
00:16:33 – 00:16:58
Oh, that, that’s fantastic. So we look to, to generate, say a model, take your scenario a little bit further. The, you know, the prediction that we’re gonna try to model out is a flight uh person’s propensity to, to leave the organization. And we’ve got all this historical data and the features that we might drive into the model might include a person’s tenure. Uh Of course, some, some key demographics. Uh maybe there are a number of
Keith Goode
00:16:58 – 00:17:43
emotions, demotions, their pay, uh how their pay has been impacted over the year. Uh And of course, we take into account, you know, supervisors and supervisors history. How long has a supervisor been a supervisor? Um How many people have they supervised over that time period? So all those elements would go into the model. And again, you and I could, could get the same potential for a high flight risk. But maybe you worked for a supervisor that was a micromanager and the system would be able to go through the history and find and find those correlations, find those things that, hm, what’s com, you know, what are the commonalities between the people that left? Oh, the supervisor seems to have a high turnover rate of its, uh, subordinates. So, you know,
Keith Goode
00:17:43 – 00:18:28
whereas maybe it got to my record, I had a totally different supervisor but maybe, you know, my career was ending and knew that, you know, I was gonna retire soon, so I was AAA risk of leaving. So, you know, those models, we’ve used the same similar models in retail organizations and, you know, retail, you’ve got people turning over left and right that work at the stores. You know, you’ve got, uh, during seasonality, people coming and going. But then again, you’ve got back office people, you’ve got people in, in transportation and, and the supply chain that stay pretty long careers. So we were able to take the data with, in the past, you’d have to create multiple models and train multiple models. But now with a I, you can take one
Keith Goode
00:18:28 – 00:18:53
model and it’s gonna learn, it’s gonna learn things about, oh, this person is working in a store. So it’s gonna go through a different path to kind of come up with that prediction versus maybe someone that’s in the uh supply chain that’s usually more career oriented. So it’s, believe me, absolutely amazing to see these models at work. They know if you feed good data into it, they’ll know how to work with it. Yeah.
Jeff Bullas
00:18:54 – 00:19:24
Excuse me. So, zeroed in what it sounds like you’re trying to do is this holistic model. So it’s quiet, it sounds big. In other words, you are measuring a lot of different silos within an organization, including employee monitoring, which is a rather interesting area and I’ve had discussions with a couple of friends recently who run businesses and have been in businesses. Um So zero in
Jeff Bullas
00:19:24 – 00:20:11
When it started, did it try to do it like we’re gonna concentrate on one area or one level of statistics across the organization? In other words, did you go for a minimal viable product solution off the bat? I don’t know whether you know the history, but the issue is that you sound like you’re doing this, but maybe you started here to actually get the product tested in the marketplace. Do people really want this? Because a lot of people develop software and people go, I got it to ABCDefghijklm MP and what they really wanted was just B right? So how did zero de start? Because I’m curious about that in terms of an entrepreneurial start up. Um you know, in data, data knowledge, and interest age.
Keith Goode
00:20:12 – 00:21:00
Yeah. So, you know, we, we started and I would say that the main premise is still a major factor in why we started, we saw a clear lack of uh insight around a workforce because uh systems and transactional systems supporting the workforce was very siloed. Um So again, these systems were data rich but information poor. Um And that’s where we, we don’t transaction, we don’t have, you know, someone keying in data, we pull data from multiple different systems. And I think that’s still a key element. I think uh hr organizations of all sizes are still very um uh siloed and, and there’s still that need to, to bring it and get good information about the workforce. So just better decision
Keith Goode
00:21:00 – 00:21:29
decisions can be made. So that was one of the core tenets of, of our, our application and do it in such a way that we weren’t dictating to an organization. Oh, you gotta get your data in this format and then give it to us. Now, we, we didn’t want to be that we didn’t wanna, you know, have one size fit all. So, you know, when we built our application um made everything highly configurable so we can go into an organization and say, look, you know, don’t worry about
Keith Goode
00:21:29 – 00:21:52
cleaning all your data and putting it in this format and sending it to us. We go in and say, well, you know, you, you probably have a ton of data already, kind of either it’s being communicated to other systems or it’s in its historical data. Um Let’s start with that. Let’s get the ball rolling. Let’s uh you know, peel back those layers of your onion, send it over to us. Let’s see if we can’t start working with that.
Keith Goode
00:21:52 – 00:22:15
Um And then based off of the configurability of our system, we can say, OK, this is this is this type of information, this is this type of information we can start bringing it together and quickly being able to show uh people in, in the hr space what their data looks like across those different silos in one dashboard and one report in one environment. Um So it, it normalizes a lot of that.
Jeff Bullas
00:22:16 – 00:22:57
So it sounds to me like you’re going to an organization, you’re going OK? You’ve got salesforce, you’ve got hubspot C different CR MS, you’ve got an ERP that’s, you know, a and then someone else has got an EP that’s B and you would have to plug into each one of those with an API application programming interface is the term that I believe. Um So you are basically going OK, you don’t need to clean it. We’ll actually plug into all your systems, accounting, whatever and we’ll then make s then we provide the intelligence and the data collection to help you and make sense of all that information across all those vertical platforms.
Keith Goode
00:22:57 – 00:23:36
Right. Right. Exactly. And, and what we’re gonna say is like, OK, we have some templates, we’ve been working on this, you know, we have some good places to start, whether it’s, you know, good measurements for finding headcount and turnover and movements. But there are templates, there are places to start and every organization is slightly different. So we found that the key is really to be able to say yes, but in such a way that it satisfies the client’s uniqueness and certainly can be reproduced. So that was a key driver in our application development,
Jeff Bullas
00:23:37 – 00:24:27
right? So the other thing I’m curious about, um there’s been some big trends that have been driven by the pandemic. Number one is remote work. OK? So everyone was sent home. So they didn’t die. OK. So um and it was, there’s now a lot of discussion going well. That was a good idea. Should we have just let run, you know COVID runs its course and herd immunity emerges. On the other hand, what’s happened is a slow movement of work from home became a revolution where now we actually find it very acceptable even for banks and big companies. Some are going, we’re just a remote Atlassian, I think a big Australian company and a software space for collaboration, software and so on. It’s basically
Jeff Bullas
00:24:27 – 00:25:03
we don’t care. We want you to work from wherever you like and you can be in Spain and you can do something for it in the USA whatever. So we’ve got that big movement. So how have you adapted to what we’ve now become? It’s not just remote work now because you’ve got, you’ve got like a whole landscape from companies that are totally remote work to those who are going, you gotta come back to the office, you gotta commute to the, to the tall office tower. You’ve gotta be in a cubicle every day and we’re gonna feed you, you know, bread at lunchtime. So there’s the old school, right? Which is that
Jeff Bullas
00:25:04 – 00:25:18
there were cool offices, which is another thing too, right? Then we have now the hybrid and the total remote. So how have you adapted to that? Have you had to actually add and develop your software to help with that in terms of that type of management?
Keith Goode
00:25:18 – 00:26:04
Yeah, we’ve helped organizations understand the types of work that can be best performed remotely versus work that might be in house. And, you know, I think where you see organizations, you sometimes see some organizations take a stand, we want everyone back to work. And then the others saying, you know, whatever you do. But I think the answer is really in the middle, the answer and it’s not just, you know, if you feel like it comes back and not, I think the answer is let’s analyze, you know, the types of work, the productivity, the cost structure. And let’s find out, you know, where it’s gonna be best that we come back and where it might be best that we stay remote. Um, and
Keith Goode
00:26:04 – 00:26:22
I, I think a lot of organizations that are, are kind of gotten caught up in the political aspect of let’s all get back or let’s all stay out. Um, and I don’t think it should be a political decision. I think it’s a prime example for analytics to help organizations make those decisions.
Jeff Bullas
00:26:23 – 00:27:11
Cool. Yeah, because it’s certainly, it’s not a fad, it’s actually staying with us. We, um, and it seems like the truth lies somewhere in the middle and variations of that, which is the hybrid model. In other words, um If you go into the city of Sydney where I live, Thursday has become the night after work to have drinks. It has become the New Friday. And um because people work from home, Mondays and Fridays and the bosses are going well, I think they’ve just taken a couple of, you know, they’ve taken long weekends, they’re really gone snow skiing really. So, um but that leads me to the next question, which is employment monitoring and management at the desk with software just like a time doctor, for example, I believe. Does this, um
Jeff Bullas
00:27:11 – 00:28:00
I’ve been in your thoughts on this for me. I see it as an invasion of privacy. Others see it as my God. I can make sure that Jimmy’s working 7.9 hours a day out of the eight and if he doesn’t or she doesn’t, Jimmy or Jane, I’m gonna kick their ass because they’re not working hard enough for me. So it’s very much an industrial model applied to the knowledge workers. Um that, you know, essentially people went to the industry, the factory and they were watched by the overseers from the top balconies looking down on the underlings. What do you think of monitoring software? And you can be honest. Um hopefully about this.
Keith Goode
00:28:00 – 00:28:29
Well, I think it’s definitely out there. Um and I think there are even software apps that can, you know, trick it like for my daughter is fairly new in the workforce and, you know, they’re monitoring, you know, how often her mouse is moving and you know, there, there are apps that, that can trick it, but I, I don’t have a problem with collecting that information. Um As long as it’s done in, in a way that
Keith Goode
00:28:29 – 00:28:56
provides better insights and information. For example, an organization might be doing that and they find out that, you know, hm, productivity is, is actually increasing or decreasing. Hey, we have the same number of people online but productivity is decreasing. Why is that? Why is that? And it gives them a better tool to look to help, try to answer that or, you know, maybe by monitoring productivity does increase. Um
Keith Goode
00:28:56 – 00:29:24
you know, it, it gets back to, you know, what if it’s solving a business problem and it’s not just being done because you know, someone wants everyone back in the office and we’re just gonna decide, decide to do this and it’s gonna be now it, it needs to be correlated back to some business uh driver to say, hey, is it impacting performance? Is it impacting productivity then, then let’s, you know, use that data to support that those, those problems and those questions
Jeff Bullas
00:29:24 – 00:30:07
because there are different types of work. So, um and if we want to use a couple of examples, number one is sales. OK. So I did sales in the tech industry. I worked in one of the first PC dealerships, IBM PC dealerships in Australia. And we sold computers to corporations and businesses, right? And government and sales is easy to measure in a way like how many phone calls did you make today? How many doors did you knock on today? How many leads did you generate? Then how many leads did you convert to sales? You generate? What’s the size of that? So these are very data driven, very specific. So, in a sales role, um
Jeff Bullas
00:30:08 – 00:30:44
I’m glad I didn’t have that software back then. Um because I’ve got, I’ve got good sales, but my metrics might have been shit on the other level. So it’s, this is where OK, Jeff only got five leads but those leads, you know, were a million dollar client, right? So anyway, this is where the data gets interesting, isn’t it? So, or on the other hand, if you’re more a little bit management, you need to be strategic, you need to be saying, OK, how can we make this product? I might as Charlie Munger talked about Warren Buffett, he wanted to sit on his ass and just do a lot of reading. So there’s not gonna be a lot of mouse moving is there and that sort of,
Keith Goode
00:30:44 – 00:31:11
right. Well, you know, it would be an interesting correlation too if you’re tracking that data for both the at home workers and the workers uh in the, say the office, what’s the difference of time? Is it? You know, I, I would argue you’d find data that shows people working from home more or more hours than those that are commuting. Um And what is that? How does that impact productivity? Um So, yeah, there’s a, there’s a lot to it.
Jeff Bullas
00:31:12 – 00:31:35
Yeah. And on the other hand, what’s interesting too is that in the office there’s a lot of distractions, especially young people, right? Um You know, there’s, yeah, they’re in their twenties. They like hanging around with water, cool, like having drinks after work and they sort of pop over each other’s desk and one male might be slightly interested in another female. And uh so they’re not going to be very efficient. Um, maybe,
Keith Goode
00:31:36 – 00:31:37
yeah, we’ve all been
Jeff Bullas
00:31:40 – 00:31:52
there. That’s a whole another discussion about workforce management and what you should and shouldn’t do in the office and things we used to do in the eighties and nineties are a little different than what’s allowed today. So, um, you
Keith Goode
00:31:52 – 00:32:08
Now, it reminds me that here in the States there was a country singer, Toby Keith and he had to sign drinks after work and it was all about the work environment and they’re all waiting to go out to the happy hour after work. And I was like, God, those were great days.
Jeff Bullas
00:32:08 – 00:32:20
Uh, I look, you gotta watch your Ps and Qs so much now. So, uh, dating, you know, you know, modern world, um, with a lot of, uh, I suppose so
Keith Goode
00:32:21 – 00:32:33
it would be a really, that would be an interesting study, studying, you know, uh, people that found their spouses at work, you know. Um, hm. Yeah, you think about that one?
Jeff Bullas
00:32:34 – 00:32:56
Well, you think about it, you spend more time with them than you spend with, you know, someone at a pub. And, uh, you know, there’s a phd somewhere and in fact, there’s about 20 or 30 phd s which are great, but sometimes they can be called piled higher and deeper and don’t learn much. But that’s fine. I
Keith Goode
00:32:56 – 00:33:02
I wonder if the people that find their spouses in the same work environment have higher productivity or lower productivity.
Jeff Bullas
00:33:04 – 00:33:26
There’s something for you to, uh, to, uh, do a, a global data search anonymously, of course, that tells you. Ok, maybe you need to have, uh, another piece of data mapped into your system that goes, are they having a relationship? How many got married? How many were having affairs? And were they more? Yeah.
Keith Goode
00:33:26 – 00:33:32
What does that do to the work environment? And what does that do to the other people around the work environment?
Jeff Bullas
00:33:32 – 00:33:44
Does everyone become really happy because there’s a lot of sex going on or something? I don’t know. But, you know, or are they just blissfully just walking around the office on a romantic day? I don’t know.
Keith Goode
00:33:47 – 00:33:49
Yeah, there’s a lot we could study Jeff
Jeff Bullas
00:33:50 – 00:34:34
anyway. Yeah, but it’s um, a time doctor type software. Um, it, for me is actually I do like the fact that you can actually get software to fool the software, the other software. So look, if I was working for someone, seriously, I would need to get that software because I’d fire myself. I think if you looked at my productivity on purely data, you know, like we live in an information world so you need to collate information to make good informa good, get good data to help you make better decisions. What does that look like? So anyway, and Warren Buffett, Warren Buffett had obviously, um, did ok out of sitting on his arse reading. So, um, but anyway,
Keith Goode
00:34:35 – 00:34:41
he did. Ok, still doing ok. Did he just get remarried?
Jeff Bullas
00:34:41 – 00:34:47
He did. No, no, no, no, that’s Rupert Murdoch. He can’t help. That is another Aussie.
Keith Goode
00:34:47 – 00:34:49
He’s just another billionaire. Right.
Jeff Bullas
00:34:49 – 00:35:09
Yeah. So my partner and I were just looking at, uh, that guy, he is married for the fifth time. He is 96 or 94 and look at his wife, she’s 67 quite, you know, big age gap. Um, I wonder if she’s on employment monitoring as well.
Keith Goode
00:35:10 – 00:35:13
I wouldn’t be surprised. Huh?
Jeff Bullas
00:35:14 – 00:35:37
Ok. So, uh we’ve been distracted, which is actually fun. So, look, uh, so Zarine, you guys have this holistic approach where you try and collect data across a different vertical application within an organization to make sense of the workforce. How lo how long has the in been running for now since uh starting
Keith Goode
00:35:37 – 00:36:28
uh about 20 years, believe it or not. So, you know, this idea has, has been around. Um we’ve, we’ve seen it like we, we, we started in the HR space as one of the transactional uh based systems in supporting learning, man, developing learning at the system. And we saw back then that these transaction systems have tremendously rich data, but it’s even more valuable when you can correlate it to the other areas about human resources. Um And we, we started 20 years ago um and it’s, it’s been a great, great time uh difficult, challenging at times, but you and now with the advent of large language models and, and A I uh we think, you know, we’re, we’re just starting another, another climb up. And uh so it’s exciting.
Jeff Bullas
00:36:29 – 00:36:35
Yeah. So your target market must be more like a large enterprise. Would that, is that correct?
Keith Goode
00:36:35 – 00:37:00
Yeah, I would say, uh, we like the mid market as well. We, you know, typically, uh, you know, over 1000 if you’re under 1000 employees, most likely people because in your hr knows everybody else. Right. But as soon as you get over that, that 1000 you know, it’s more than a couple of people can, can know everybody. They need to rely on the data to help make better decisions about the workforce.
Keith Goode
00:37:00 – 00:37:29
And we’ve worked with organizations that have, you know, 100,000 and North. Um But I think the, the sweet spot is, you know, 10 to, to 20 to 30,000 is, is a, a good organization that’s, you know, really needs a, a tool without having to spend a lot of money that can bring this data together in a way that supports our business and supports the uniqueness. And that’s what we try to do.
Jeff Bullas
00:37:29 – 00:37:34
So it’s sort of like a starting point. You, like, you run it as a model, don’t you? Software as a service? So,
Keith Goode
00:37:34 – 00:38:02
Yes, we do. But we’re also very unique. We have some clients and partners in which we can deploy the whole platform behind their firewall. So that’s unique. I, I don’t know of anyone else that’s doing that. Most of its software is a service model. Where it’s in your cloud environment. But we have that and that’s great and it’s just wonderful, but we also have some unique clients and partners where we deploy behind their firewall. Um We work with partners and we,
Keith Goode
00:38:02 – 00:38:28
We are their analytics engine. Um So uh it gives them a stronger uh model to go out to their client base and say, not only are we providing this service to you, but from a reporting, we can augment that by bringing in additional data, not just what we do, but then we can augment it with more information and provide a more robust uh reporting um and analytic analytic solution. So both were free.
Jeff Bullas
00:38:29 – 00:39:06
Cool that what you talk about then is about operate, putting that data behind a fire firewall and operating and then you analyzing that behind their firewall as opposed to how you having it on yours or that raised a very interesting question which I had a discussion with last week in that it comes down to copyright, it comes down to hang on to your IP as an organization and the world wide web, social media has made everything a lot more transparent. The big language models are going out and scraping the web, collating the intelligence of the planet, putting it in their data centers.
Jeff Bullas
00:39:07 – 00:40:02
In fact, what they’re doing is they’re actually scraping your IP. If all your information about your organization, your culture, how it makes you successful um will turn up in the results. In other words, your competitors can learn about you by tapping into that data collected by the large language models which are staggering inside. They were talking trillions of data points and beyond. So what do you think about the fact of trying cos you must get some credible data about what makes a business successful, what makes this business successful as in they’ve got, they don’t micromanage, they hang on to staff longer, they do internal training and so on. That data could be fascinating and if it was anonymized it would be even more fascinating.
Jeff Bullas
00:40:03 – 00:40:31
So do you guys in discussions because of the, I suppose the broad collection of data across the web that identifies the IP of an organization, in fact, it’s being used now to collect data about organizations by interrogating chat G BT 4.0. Do you have this discussion with your clients about trying to protect their IP? In other words, creating what we call a walled garden of A I?
Keith Goode
00:40:32 – 00:41:27
Absolutely, I mean, you know, it’s just as important as their data itself. Um And in some respects, it’s basically the same. Um it’s uh you know, it’s, it’s intelligence gained from insights into their data and it’s their data and it’s, it has to be in a secure environment and we, you know, take that very, very seriously. Uh We work with government agencies that are also in uh here in the states that are highly secure. Um So, yeah, that’s important and we treat that as, as insights similar to their personal identifiable data, um you know, that they have and that’s their data. Even the insights I think from an IP perspective, you know, what we bring to the table is the support to generate those models that, that, that provide those insights.
Keith Goode
00:41:27 – 00:42:01
So, you know, that’s where, you know, for coding uh um an A I model and for coding, you know, something that’s, that’s going to be used in getting that insight. The insight is a client. But R IP is that, you know, the code that we bring together to, you know, either bring the data together to generate a model. Um you know, that’s what we considered our ID, but the insight that it produces is the datas and it is just as secure and valuable as their own personal data.
Jeff Bullas
00:42:02 – 00:42:09
Uh That’s, that’s good and that’s cool. And I expected that question um uh that answer, sorry, that’s good.
Keith Goode
00:42:09 – 00:42:46
You know, the other aspect about these two is, is um here in the States, they’re, they’re doing a lot of uh there’s some legal implications too about any analytics or insights. Well, you know, we have to find our people. So if it’s analytics around people, you have to have audits on bias. So is your model and the outcome is it producing any biases to any you know, um ethnicities or genders or, you know, so you have to do the audits into that as well. Um So that’s another key aspect of, of workforce analytics that we take serious from
Jeff Bullas
00:42:47 – 00:43:32
in talking about this data on the IP and collecting culture, gender and so on and everything. Um You must have some incredible data anonymized that you could use to create incredible reports and give insights into the state of hr the importance of culture, the importance of diversity, the importance of, you know, not micromanaging, for example, just pulling things up. But have you guys generated like an annual report on insights on the hr industry, the direction and trends of uh of height shower employment in the USA, for example, have you done any of that? Because that would be fascinating. It,
Keith Goode
00:43:32 – 00:44:23
It would be fascinating. Um and there’s a lot of organizations to do that uh is a common um garner does that um our role is really to, to work with an individual organization so that they have that insight for themselves. Yeah, let’s face it. Each organization believes that they’re very unique in, in most cases, as they are. Um, do they like to be able to trend themselves against some benchmark data? Absolutely. And we can bring in that benchmark data, whether it’s the Department of Labor or other publicly available data elements, we can certainly, you know, bring that in and do some benchmarking. But I think what’s valuable is, you know, an organization where the head of HR might read a Deloitte or, uh, Gartner, an industry analysis report. Right? Doesn’t mean that’s what’s happening in their organization.
Keith Goode
00:44:23 – 00:44:52
Uh They like to know what’s happening in the industry or, you know, at the industry as a whole or outside, you know, whether it’s a ge geographical region, but it doesn’t mean that’s what’s happening inside their organization. And I would argue that what’s happening in their organization is more important because it’s gonna drive their organization’s productivity, their profitability, their, you know, the value of their stock values. So that’s where we like to, to try to focus our energy.
Jeff Bullas
00:44:52 – 00:45:27
Yeah. OK. The other area that interests me is the, is the rise of almost what we call a new species of company. That is what you call your, I suppose, mega multinational organization that’s been able to rise because of the world wide web, the internet, the transfer of information. So we now have, I think, $7 trillion companies. It was unimaginable a decade ago,
Keith Goode
00:45:28 – 00:45:30
larger than most countries.
Jeff Bullas
00:45:30 – 00:46:23
Yeah. So I actually did a little report recently just to try and get some concept around this is that of those $7 trillion companies, they actually are larger than some of the countries in the top 20 GD GDP S. So these have incredible power and influence and they operate without national rules, they work within the rules of the organization, but this is a new species of company. We had them before. OK. Big oil companies, big Pharma. But there wasn’t anything of this scale. Do you guys work with any of those or is that because of the data on those? That would be fascinating in terms of what makes them successful men trying to dive in, write, and write books about it. So um how do you manage 1.3 million people globally?
Keith Goode
00:46:26 – 00:47:06
You break it down. I mean, it’s that simple, you know, how do you eat a uh elephant, right? Break it into the time, right? So, um and the great thing about, you know, what we do in analytics around it is it can support such large volumes of data and generate insights very quickly uh from it. But uh it still has to be made and you know, even those large uh multinational organizations, you know, their Latin America business is quite different from the European business. Um But, you know, they can certainly use some of the same metrics and same some uh same algorithms uh to, to help support, you know, each area. Um
Keith Goode
00:47:07 – 00:47:37
But no, it’s, it’s a great question and uh you know, be interesting, I guarantee you that there are all of those organizations that get to that size by looking at everything by, by getting as much value as they can out of every data nugget. Uh They don’t just happen to, to grow that size they’re, they’re doing things purposefully and uh and analytics and especially analytics around their probably their most valuable assets. Their people are, uh , quite valuable to me.
Jeff Bullas
00:47:37 – 00:48:18
Yeah, we only have to look at the likes of Microsoft who’s in that gang of seven. And then you have to look at Amazon again in that group and then guess what they operate. They operate huge data centers which actually sell data access and management. And on top of that, then the rise of A I is then put a whole supercharger under data collection and management. And that’s why we’ve seen the rise in the video that um the processes allow them to actually make to actually process that data. So it’s a fascinating time and you must be celebrating almost every morning when you get up because you got so much more to play with.
Keith Goode
00:48:18 – 00:48:52
There’s not a day that goes by, I don’t wake up and say, how can I, how can we tweak some data to get some value to one of our clients and whether it’s large or small? I I, you know, some of what our clients do is really interesting with clients that, that still make, you know, the manufacturing one that comes to mind, it makes forklifts and, and you know, it just to me a fascinating industry, you know, I I think, well, what can we do to, to help them gain more value about, you know, the data that we’re, we’re working in and, uh, there’s a lot out there, there’s so much things to do that can be done
Jeff Bullas
00:48:52 – 00:49:01
cos that don’t even percolate into product development. Like, how do you build a better forklift? Because you’ve identified within the organization, there’s issues because the data told you.
Keith Goode
00:49:02 – 00:49:05
Mhm. Absolutely.
Jeff Bullas
00:49:06 – 00:49:39
So, Keith, just like one more question, um, I might stick to another one but one more question is, uh, what have you learned along the way? You’re obviously a data geek, you’ve got to go on a watch like me and uh that makes you fantastic. Um So uh amongst many other things, but what have you learned along the way by applying data to the hr industry? What are the couple of big insights and learnings that you’ve discovered and been aha moments or whatever? And we could share with our audience.
Keith Goode
00:49:40 – 00:50:09
But, you know, I think the large language models and the generative A I have a really interesting future uh to, to what we do in around workforce analytics and it’s still emerging and still it’s happening quite quickly. Um And we’re on top of that. Uh We really, you know, find that to be fascinating. But in general, when again, it’s down to, you know, what’s gonna add the most value of making better decisions about the workforce around workforce analytics? I, I would say
Keith Goode
00:50:10 – 00:50:48
there’s no magic bullet, there’s no magic wand that you can wave to say, oh, I’ve got all the pieces of information I need, it just doesn’t exist. However, just like we talked about before one bite at a time, you know, you can start getting into the detail, you can start cleaning data, you can start looking at the data your organization has, you can start thinking, you know, hey, if I could predict X this would gain a certain value. Not because it’s cool, Not because it’s, you know, you’ve heard someone else do it and think about how that prediction could actually be used. Um, and those are the kinds of things. I’m very, I like to get the job done. I like to be things, you know,
Keith Goode
00:50:48 – 00:51:07
Simple solutions for complex problems often are the way to go. And, uh, yeah. So that’s what I would focus on. I, I certainly hope your listeners found this in, uh, information valuable and I’d always love to carry on the conversation.
Jeff Bullas
00:51:08 – 00:51:55
Exactly. Well, I really don’t care about them. I’ve learned a lot so I’m good. I do care, but it’s right. Yeah, it’s, yeah. Yeah. Hopefully, what I do is for me is if I find something curious, hopefully someone else would, I’d be curious about it as well. So, for me, I followed my curiosity until it became compelling and that’s how I actually run my life if you want to still, how I run my life. And ever since the age of 50 I discovered that and I’ve been doing that ever since and um life’s adventure as you watch and then try and make sense of all the information that runs past your plate across your screen. So one last question for you, Keith, what brings you deep joy and you would do it for free if you were able to do it every day.
Keith Goode
00:51:55 – 00:52:20
If a client says man, that’s valuable, that’s something I didn’t know. I would look, I answered that question very quickly because I know if, if I do something where my clients find value from it, I do that, that excites me. That’s what drives me um knowing that, that they’re gaining something inside something valuable. Um And I love that feeling.
Jeff Bullas
00:52:20 – 00:52:25
So what brings you joy is bringing value to the world at
Jeff Bullas
00:52:28 – 00:53:04
At the end of the day, happiness is actually making a difference with others and communicating with others isn’t having those great relationships with others. Um And Aristotle talked about this about two or 3000 years ago, but Harvard research has revealed that beyond $75,000 a year, happiness really comes from our relationships with other humans, friends and family. So by adding value to those, you are just doing what Aristotle said we should do and what Harvard is telling us to do. So,
Keith Goode
00:53:04 – 00:53:05
so easy.
Jeff Bullas
00:53:05 – 00:53:09
Yeah, it is. It’s really easy. Yeah, that’s just just choosing happiness.
Keith Goode
00:53:09 – 00:53:27
That’s right. That’s right. Thank you so much. If any does want to carry on the conversation. I can be reached on my linkedin account, which is Keith a good um, or they can reach our website zero, name.com. Always love to carry on the conversation.
Jeff Bullas
00:53:27 – 00:54:04
Right. Well, everyone knows how to contact you now and we’ll put it in the show notes and uh thank you very much, Keith. It’s been an absolute joy and a pleasure this morning. Uh, despite being seven o’clock, look, I’m getting a bit older so I really don’t want to rush things around. You know, there’s things called priorities. So it’s good. But, and podcasts, I get so much joy out of having these conversations. Um It’s a blast and I learn so much by just having them, uh because we’re all trying to make sense of the world and um each podcast helps make a little bit more sense of it. So, thank you very much. Thank
Keith Goode
00:54:04 – 00:54:04
you Jeff.