E059: organisational Network Analysis (ONA)

now on to this episode.

Hello. This is James and this is Jane, and here we are again with another episode of a world of work podcast today we're having a conversation with Alexander Schwab from rabbit analytics. That's with an H, R, H, A, B, i, t analytics, they're an analytics and HR and feedback assessment company from the US, and we have a really interesting conversation. What are we chatting to him about today? Jane,

so today we are talking to Alexandra about organizational network analysis and how you can collect data on, interpret and visualize the networks in your organization that go beyond the organization chart. Yeah,

that's right. So we cover a range of things. We cover why these organizational networks are interesting different ways you can measure them, be it through surveys or other analysis tools, and some of the things you can do with them and how to get started if you're interested in that. And we also touch on one potential issue around ethics that we think might be relevant

as well. Excellent. I'm really excited about this conversation. I think that you are too. I am, indeed. It's a great topic. Let's get going.

Okay, so here we are in the core of this episode, and today we're having a conversation with Alexander Fauci from rabbit, an analytics company in Pittsburgh, and we're going to be speaking about organizational network analysis, amongst other things, and seeing where the conversation goes before we get into that topic. Though, Alexander, would you be able to introduce yourself to the audience and say a little bit about yourself, your background and perhaps what rabbit does?

Yeah, absolutely. My name is Alexander. I'm the chief scientist and a co founder of rabbit analytics. We are located in Pittsburgh, in Pennsylvania, in the US, and I'm an industrial organizational psychologist by training. So this is, this is where my mind is at. And he had rabbit. My job is to make sure that, you know, everything is kind of scientifically sound. Everything is working. We started rabbit about three years ago, three years in February, to build really useful, I don't know, software that makes it very, very easy for people to exchange feedback with each other, and then we use that feedback to we roll that up and we give it back to the individual. We give it back to managers. We give it to talent managers and leaders of the company to make good decisions about development, for example. But part of the outcome of this feedback process is that we know who's working with whom, okay, and that's network information, and that's how this ties into organizational network analysis. This is how we, how we, uh, kind of discovered, uh, organizational network analysis. And I thought when thought, wow, this is really cool, and we've been doing it ever since,

yeah, okay, so it kind of grew out of a core of your work, and it's something that you started to bolt on as a deliverable for your clients. Or exactly

we, we our product delivers a lot of data and on a like, unlike most other tools, it's really suitable to visualize this data and puts it in a in a image that makes you understand complex information quite easily. So for us, this is perfect, because we want to empower our users to not just, you know, have the data, but do smart things with it and understand it and have insights.

Yeah, cool. Well, it sounds an interesting journey. Before we get into a little bit more on the on a organizational network analysis stuff itself, would you be able to just have a bit of a lay introduction to maybe what networks are, and a little mini introduction to network theory that might help people who are unaware of this type of

work, absolutely, and I really thought hard about how to do this the right way. Because, yeah, organizational network analysis, this is this really visual thing, right you? If you, if you Google it, and if you look at, you know, the images that that this comes up with, you see a lot of dots with lines connected to it. And but to explain it just verbally is quite hard. So here's here's my step, and maybe I need to take multiple steps so, so yeah, be patient with me. We basically in in any organizations, in any type of organization, people have social ties. Have connections with each other. They talk to each other. They're friends with each other, they collaborate with each other, they share secrets with each other. All of these connections form the organizational network, or the informal network. This is the type of network that describes how people are interacting, how they tie to each other. It's. It's pretty trivial, but it it has enormous implications and just absolutely fascinating applications that the second way to explain this and make them maybe come to life a little bit more is to contrast it with, you know, the old, traditional formal network of an organization. It's kind of explaining what it is not. And maybe I can take a step at that. Yeah? The like, if you ask an organization, what, how they're organized, right? They will say, Oh, yeah, we have a department here. Department they are marketing and accounting. They will talk about who's reporting to whom. They will talk about to teams and what their team leaders are, and stuff like that, and that that is all well and good, but that is, you know, the the formal network that is often visualized in the org chart, so you have departments leaders and all that stuff, and that. There's nothing wrong with it, per se, but it's a really old, traditional way to think about companies. It's goes back to the beginning of the 20 century, like early management theorists you know, thought about how to structure organizations like that. But the reality is, this is not how companies work. The reality is that you have these informal networks, that you have this organizational network of people you know being connected to each other, reaching out to others for help, helping others in return, knowing who knows what in the company it is. It is a very different concept. And while the traditional network is visualized in the in the org chart, right, the org network is visualized in the organizational network visualization, or organizational network analysis. And that's the thing with, like, all the dots, the lines, it looks like a big fuzz ball. It looks pretty chaotic, because that's what organizations are. They are chaos with some organization behind it. So that's, yeah, that's that basically, yeah.

So I guess each person is, you know, I guess a.in an organization, in the network analysis that you're mapping, and then the lines that come off them have relationships with others on certain sort of dimensions. Is that? Right? That's

exactly right. So you think of yourself in your organization, and think of who you're working with, you would be a dot. They would be a.if you work with them, you probably would be connected with a line. And it's also the tricky part of drawing these, because this can get complex very, very easily, but software has made it quite easy to do that for us. And maybe I'll get into that a little later, but you can, you can see where, where am I in relation to the rest of the organization, which can be quite fascinating, actually, yeah. And I

guess in different organizations it's going to be different, but you probably get situations where there are individuals who maybe have only one line or a very few number of lines into other people, and they're quite, perhaps siloed and remote. And then you've probably got some people who have a whole host of lines coming in and out of them, and they're quite at the center of a set of relationships. Is that fair? Is that a fair distinction?

That's absolutely correct. And this is where on a becomes such a useful tool, because it shows you who are the people who are, you know, so called isolates, people who have maybe one connection there on the periphery of the network. And that is something to you know, think about this could be a good thing or a bad thing. It could be normal. And then, of course, you have people who are really embedded in the organization. And this is where on a just delivers tremendous value, because somebody who's highly, you know, central to the company, and there are different types of centrality, but if somebody's highly Central. This could be a really good thing. This could be a really bad thing as well. If this person, for example, you know, gets gets hit by the by the proverbial bus, at least that's what happens to people in the US, you know, how big of a hole do they leave behind, and how much of communication will be, will be, you know, stymied by this person not being there anymore. You can also ask questions around, are we burning people out because they're just over leveraged? They have too many connections. They have too many masters, too many internal customers. So the the you can analyze the structure as a whole, and you can analyze or understand individuals. And this is where you have like these, these moments where companies say, Wow, we were not aware of that, because these people, maybe people who you never would have expected to be so central to Sure, sure, it's like, hi, it's what it's like. You know, when

you start a job and the first thing you get told is make like Well, when I started back in the 80s, make friends with the mail room, which no longer exists, but now the concurrent would be make friends with the IT customer, desk manager, or make friends with the one. Who opened the canteen early. And it's about those people who have huge influence and have huge power within the organization, but may not on any organization chart appear anything other than a small.at the end of a line. You're

absolutely right. They are this. This is exactly it. We've I've seen, I've looked at organizational network charts, visualizations of our customers and people who are not leaders, who are not like, in some really influential, formal role, they have, like, this enormous, Big Dot. They're like, super connected. And these may be people in like, some specialist role where they help others with software. And you know, people like that can be the lifeblood of the organization. They can be the ones who keep this machine running. They help others out. And understanding who they are is really important.

I've just sorry you've just given me this great image of someone very important organization, pointing at a network analysis chart with a giant dot going, who's that guy? Oh, that's Dave. Everyone knows Dave, yeah, just a quick question, yeah, sorry, just, I'm really interested when you talk about that line that connects two people as a relationship. What kind of relationships? Obviously, there's the relationships where you work with someone on a project, or that they help you. But would it include potentially social relationships, like where they might both be members of, I don't know, an LGBT community network in the organization, or where they might car share, or is it much more about their role within the organization?

Well, there's the degrees of that. So there's that in my book there, from what I've seen, what we have used, they're basically four types of network. The one that I've been talking about a lot is like collaboration. Who are you working with? Are you working with? You know, these five people there you network. And that's maybe along the line of the formal network to some degree. But then we can also ask people questions about, who are the people in the company that that you trust, who are the individuals that that you go to for non technical advice? So this is the trust network. This could be along the lines of, you know, you could ask the question, Who are the people you go to when you need help with a difficult situation or conflict, or when you need guidance with a political matter. So this is your trust network. Then another alternative to that is the problem solving or advice network. So this is like technical stuff. Who do you go to when your computer isn't or when, when you when the software that we're using isn't working? Who do you go to when you have a technical problem? And then there's the communication network that's also very commonly used. Who, who supplies you with information, who shares information with you, stuff like that, in terms of what other networks they're part of, this could be included. This is definitely something that that can be used. But you could also overlay, for example, if you know diversity and inclusion is a concern, here's something that you could do. You could check if your network is structured by basically sameness or homophily. So like, there's the idea that people stick together with similar to each other, like birds of a feather flock together. So you could say, Okay, I have this, let's say engineering team, or this marketing team. Are they? Are they? Are they? Are they structured by their, you know, by by factors that are not really work related, maybe by their gender or by their ethnicity, stuff like that. So it gives you the ability to see, can we maybe break up these networks a little bit and make them a little bit more diverse?

Yeah, and I think that's fascinating. And I love the idea of a multiple types of networks that exist contemporaneously within an organization, and the layers to that, I think are fascinating. Do you when, when you're sort of creating visualizations of these networks and exploring them? Do you see relationships between them? Is your trust relation, your trust network, similar to your communication network or your expertise network? Or can they be widely different?

They could be totally different. That's that's a fascinating thing. You have the people that others trust. Let me rephrase this, people that are the technical advisors, that have all the expertise may not necessarily the people that others trust. Now let's take a guess. Who gets promoted in a company. Yeah, you get, you promote people who are, like, really, technically savvy. Now they're in a leadership role, and this leadership role forces them to take a more relational, a more social component, and they're out of their depth. So one use of on a is actually thinking about promotions. To think about, should we promote people who are not only technically strong, but who have been able consistently. To build strong interpersonal relationships with with others in the company. So, yeah, they can be totally different. That's that's a fascinating thing about it.

Yeah, it's really COVID. It fits into the sort of hiring or recruitment promotion decisions that's interesting. Um, when you go about trying to analyze a network, man and to you know you're working with your clients. How do you how do you start that process? I mean, what are your tools? Do you go in and observe? Do you do surveys? Do you get into our systems and see who emails, who? I mean, what are your tools?

This, like in I can talk about, like, the typical, usual way, and then maybe a little bit how we do it. So there's basically two clusters. There's active data collection and passive data collection. Active Data Collection is essentially sending out a survey and asking people please list the people who are in your trust network and your communication network. Who are you collaborating with, and who are the people you go to for problem solving. So the four categories that I talked earlier about, and that's, that's, that's a short, short survey, and you can attach that to your, you know, other company culture surveys and stuff like that. The second one is passive data collection, and that is like the big leap ahead you. You can analyze the digital trace you you mentioned email. You can look at who's actually exchanging emails with whom in the company. Slack messages, same thing. Yeah, some companies have, you know, I have never seen it done, but you know, you if you have RFID badges and your company, you could, like, analyze who's been, you know, co located with others. You know, who has conversations in the hallway? I, again, I've never seen this done. I find it a little invasive. I'm not sure what to think about this.

Yeah, it's really interesting. Yeah, I was going to ask about the sort of Big Brother aspect of some of that stuff. So I'm slightly conflicted as it sounds like you are. I think it's fascinating and a great toy and hugely interesting and lots of great data, but I just feel slightly ethically worried about that.

I think, I think it's really so I'm really interested absolutely in that ethical question. But just aside from that, for a minute, I don't know if you're familiar with James my background, but my background is predominantly in the third sector, which is the nonprofit sector in US terms. And one of the things we see all the time is that people who work for organizations also hold voluntary roles, and those voluntary roles might be quite senior, but main or have have high levels of power. So classic example would be, I am an administrator for a 300 man organization, but I also happen to be the coach of the national netball team, let's say. And so one of the things that could be extraordinary is an opportunity for sports organizations and non profits to understand the different dynamics and relationships between people with their different hats on, because in those organizations, they quite often have two or maybe even three or four hats, and that visibility of so there's a classic example. I know someone who's in a role, and they're brilliant at it, and they work alongside someone, and you could sit in the meeting and never know that one of them coached the other for 10 years, because they used to coach them as a junior and a young senior, right? And suddenly you can visualize that. That would be so cool.

It's, I've not seen this exact thing done, but, like the research and the data suggests that that you really onto something. I've I've yet to see a network where people visualize, you know, network connections to outside to to other organizations that say you you volunteer work. However, I think this would be a really interesting type of research, because the research suggests that the people who are in these boundary Spanner roles, who are basically between the single connectors between other other networks, basically They are these, these brokers of information, they have a higher probability, or they're in a better position to be truly innovative. And here's why, like innovation is like coming up with new ideas, but you're basing new ideas on what you know research calls unique, non redundant concepts or information. So in other words, you cannot just marinate in your own, your own juices all the time. You gotta get you get some fresh ideas. If you're a coach outside, maybe, or work for a nonprofit in some fashion, maybe that is the source for this unique, non redundant information that can really enrich you work in your in your day job, you know this, this could be very important for organizations to understand. Yeah, it's really about sort of interdisciplinary knowledge, type of pieces. Well, we

see it. I mean, what you're talking about anecdotally, we see all the time in our sector. So we see people who volunteer for one similar organization but work for another. A different sport or a different cause, and because they volunteer and exposed to different processes, you see that innovation spread much faster than you would have done previously, because they just wouldn't have people working in them that they would be exposed to the back end. And I think, I also think it's incredible. So the bit that you talk around, around trust, because, without quoting lots of terrible films with trust covers great power, right? And, and one of the really, really interesting things to me is the power that gets held within those sort of big dots, as you refer to them on the diagram. You know, the guys with all those connections who maybe have no resource power, no legitimate power in terms of their position, and yet seem to be able to block innovation as well as, right? That happens all the time. It

happens all the time. And you ride on and here's, here's the kicker, like companies just don't know who these people are. Like even the most psychologically savvy manager. They can identify a few people, but they usually get it wrong. There's some data behind this. So, so drawing this out can allow you to see who are the people who can either facilitate change or just impede it. And there's, there's, there's actually research on that that that is absolutely lovely to indicate if, if you want to, if you want to, like, initiate change in your organization. So this whole big, big body of change management, you know, the often companies say, Okay, we got to, you know, get the leaders on board and the CEO, and that's all true. Like, if you read HP articles on this topic, they always start with, you know, you could the CEO has to be involved sponsorship, but talk

and none of that is wrong. But to Jane's point, the if we can identify the people who maybe the detractors, but you know the people who are influential in this network, highly connected people that others listen to, that are part of their, you know, technical advice network, for example, and their trust network. How do we, how do we, you know, talk to them? How do we prepare them, and does it make sense to include them with all the other you know, people in your lines, that that you, that you form when you have a change manager?

Yeah, it's cool. It seems like there are lots of uses for this. I'd like to come on to those in a minute, but I guess I've heard about things like organizational, network analysis, on and off for years, and it feels like it's been around for a while, but it feels like it's sort of getting more and more traction. Now. Have you got thoughts on why that is? Is there a reason? Or

I think it's, it's, it's twofold. There's, and it has been around for for really quite a while, going back into the, you know, 1950s and 60s in its beginning beginnings. But I think two things really matter. And the first one is data collection has been, has gotten more and more easy. I mean, you can, you have survey tools for free at your hands. You can go into, you know, Google Forms or whatever, and build the survey. This has always been easy, but it's even getting easier. And then the second one is the passive data collection. So in in like companies may have these data points at hand without having to collect it, so now they can just harvest this data. The third one is the software that allows you to analyze. This has gotten really democratized. You don't have to buy an expensive analytics tool. You can go online. There's, for example, Gephi. There are many different libraries for R and Python that you can use. There are JavaScript, vis, J, s that there's just a lot of stuff out there that you can use without having, like deep, deep training on these to to build your own network analysis. And I think, I think people should do that. It's it. It's not a huge barrier to entry. And I think all of this put together, is the reason why this has been popping up repeatedly. And

so you said, it's getting a lot easier to do and easier to analyze what the software and things like that. What sort of scales of organization do you see taking advantage of ona at the minute and using it? Is there a differentiation between the types of organizations who do look to undertake on a and those who don't

I, I see a lot of organizations do it who have maybe somebody like HR analytics leads, or a data scientist, or somebody like that in there. So in other words, large organizations who have people who have maybe an intrinsic interest in that, who are informed about it. I see them do it. A friend of mine works for for a big insurance company, and they use this to think about what kind of attrition risks they have in their company. And he has found, for example, that attrition is contagious, that you. Know, if one group of people experiences somebody leaving the company, that the probability goes up slightly in that network of people. So for them, this is super interesting. I'm so it's, it's maybe right now, more and bigger companies, but I don't see why small companies, you know, not, not like the MNCs of the world wouldn't use it because, you know, the smaller the company is, the easier it is to implement.

Yeah, when you were talking about analytics piece, I read something a month or so ago, probably a bit longer than that, where IBM said that they can predict with something like a 95% accuracy somebody's departure within six months time. Loads of data points. I don't know how much I really believe it, but I guess that's an indication of all these. Guess it

depends how much information they've got access to, doesn't it? Yeah, well, if they've got a six month notice period, I'd say they can probably do it with 100% I was thinking more if you could say they're a bigger company than me, but, you know,

yeah, cool. So what other types of uses. Are there of an A? I mean, if you were to think about why people should be interested in this and what some of the outcomes and benefits are, we've touched on a few. But could you give a little bit of a summary of what maybe chief execs or leaders in HR could get from undertaking on a Yeah,

so for me, I see them in two rough clusters. So the first cluster is thinking about the individual. And I kind of mentioned, I touched on this a little bit, but you can look for people who are risk of burnout, who may be in a unduly powerful role. So somebody who has a big centrality, maybe they have a lot of influence. You can think about maybe, if you if you need to mitigate that attrition risk is a big one for change management, you can identify influencers, but there, you know, there are other users that are more at the structural level. So one of our clients looked at the network, and they realized that they're deeply, deeply siloed. So people within one department of the organization are not connected to others, and even sometimes within departments, people are not connected to each other, and specifically, they're not exchanging information. So for a talent manager and for a leader of a company, this is often like a bit of an aha moment where they where they say, Oh, I kind of always knew it, but now this really shows me that we have these silos in the on a literature it's called the structural hole, which is, I think, very descriptive. There's a gap in the structure. These parts of the organization are not talking to each other, and that has deep implications.

Yeah, interesting. And how live do on as remain. I mean, how frequently do people need to refresh their understanding if you're talking about redundancy risk, that seems to imply that you know, some of you update it to show that individuals are dropping off of your network. How? How frequently do people refresh these?

Yeah, this is, this is where, where the passive data connection. I'm sorry, data collection really shines. So in active, you collect data in a service. So that's point in time. If you use the passive. You can refresh this, you know, as as often as you need to. And in our model, this, this data is refreshed nightly, because these networks may be changing on a daily basis. So it really depends on what you what your use case is, if you want to kick off a conversation, if you want to build an initial understanding, you know, you probably don't have to do this very often. You can maybe do this along the lines of of an engagement survey, but if you want to see how things are developing, then you know, more frequent data collection is desirable. This is a lot of trade offs here. You know, you you don't want to overwhelm people with, you know, ever changing pictures. But you know this, this information does change, and you want to keep it, you know, up to date, yeah.

So I guess, thinking about this, I mean, it seems like there are some real, you know, benefits, and we've got some of those concerns that we've identified about, you know, data privacy and that kind of stuff as well. But if organizations were interested in starting to do this and say they didn't really do anything around organizational network analysis, how do you think they can go about starting, and what do you think their sort of journey might be and discovering what it could do for them?

I would, I would really recommend any company to do some of this, even if it's just dipping your toe in the water, and then being a doing it yourself, and then being a more informed consumer of maybe consulting services if you choose to do so. So something that that you can do quite easily is design your own active data collection survey. There are templates on. The internet that you can just use for this type of purpose. I'm talking a four or five question survey. It's not a long survey, collect, collect this data. That's easy enough. It's essentially doesn't involve any any software that you have to buy, and then you need to find somebody, if, if it's not yourself, who has a knack or some interest in data collection, and then you could, you know, put this data into analysis software, like, I like, gee, if you have somebody with with our background in the organization, or other software, they could do that relatively easily. It's a little bit of a learning curve. But, you know, everything has a learning curve, and have a look at it and see if it's something that is helpful for you. Use it as an exploratory tool. I had great conversations with clients where they looked at the network and it triggered just these really in depth conversations that they would have never had otherwise. And I think that, in itself, is a useful outcome at a later stage, if you want to, if you feel like, okay, this is something that I'm interested in, or if you have a high stakes situation, if you have, like a merger or something of that nature, maybe it's appropriate to pull in somebody to help you with that. But as a very first step, such low hanging fruit. It is no, not rocket science. It's something you can train yourself on using the resources that are abundant on the internet. I would just, I would just dip your toes in it. Yeah, I

absolutely love the concept of using it to help organizations manage where they have silo issues. It's something we see all the time in the kind of size organizations that I work with and and it's just we hear it consistently, time again that there are silos within certain functions. And it's always it's never across the board. It's always uncertain. So being able to visualize that is for a very practical organization is just such a great opportunity. Yeah,

there's so many things that people kind of know in an organization. People know that they're silos, or they know that somebody is very well connected, or they know that somebody's very trusted, things like that. But to get the evidence behind it, it's just so helpful, exactly

like an ONA can give you a one number summary analysis of how siloed you are. Can tell you, this is the modularity index of how siloed you are, and then you can restructure, rerun it and see if your modularity index has gone down, if you if you little bit more homogeneously distributed. But it's also, you know, a qualitative analysis tool to say, Okay, it looks like our software folks and the product managers are not talking to each other. Maybe that's a good thing. Maybe we want to do this on purpose. Maybe we don't want our customer support to be talking with our engineers so they are. Otherwise they don't get anything done, you know. But this moves this into, like, deliberate decision making, not just acquiescing to the status quo. Yeah,

and it's, it's funny, the more you talk, the more I am both excited and terrified, because I can, I can see how much help it could be in so many scenarios. But I also being realistic about some of the people in the world, I can see how an intent to improve things. So for example, I could imagine a scenario where, instead of just saying who's in your network and how strong is it, what's the quality of those relationships and how mutually beneficial are they, and suddenly you move from there to starting to look at grading people and the quality of relationships they they create. And we already do that some extent, in some jobs, but it's like you can, like you say about being unsure about some of the ethics. It's so difficult because it is so, like all good psychological tools, right? If it's good and powerful, then there's a challenge to it, and you have to manage it. And I think, I guess my one comment would be, if people are going to dip their toes in just think really carefully about how you're giving your employees a voice to decide how what data gets used, and how,

absolutely, I mean, we we build a tool in which people give feedback to each other. So this exchange of somewhat sensitive, maybe not sensitive, but very personal information and and we have found that if the company explains and is transparent and communicates very clearly how the data is being used, why it is being used, how it is not used to build like a big brother scenario against you and mind control you. I'm being facetious here, but this is not a laughing matter. If you do this right, and you have a basic trust in your company, it can be done, but I completely agree with you it has to be done the right way. Because, you know, people just get. Scared of, you know, the company, knowing all of these things about you, that's, it's definitely something to consider

well, and you're right. It's about the level of trust you have in your organization and the people that lead it. That's, it's as simple as that, and but where you do and you're getting really rich data. That's that's pretty powerful, right?

Could be a big, big advantage for an organization, as well as being hugely insightful and and as you said, these things can be used to improve organizational performance, but also quality of existence for individuals in organizations as

well. And if your name's Dave, and you work in the work in the IT team and your CEO comes to see you tomorrow. You know that's because they've started doing on a Yeah,

cool. All right, well, I think we're pretty much getting to the end of our time on this episode to as we get there. Have you got any thoughts on what others what people could do to learn a little bit more about on a and maybe what I could do to learn a little bit more about rabbit and what you guys do as well.

Yeah, if you, if you want to dive into the the on a topic, there's, you know, a simple Google search will, will get you started, and you can go down, go down that rabbit hole. I would, I would probably, my recommendation would be to look for good books that capture this topic a little bit more more thoroughly, but they are, you know, tutorials on YouTube, you know, just just to get yourself started and then start your own journey. All of this information is really very easily available on the internet. This is not some arcane you know, knowledge that some people have and others others cannot have. If, if you want to learn more about rabbits, we can be found very easily under rabbit analytics.com and this is, this is rabbit with R, H, A, B, i, t, like the R, and then habit analytics.com and we

provide this kind of information as well, but we're not this is not our only thing that we do. It's not our main differentiator for us. It's a great way to capture and make digestible the other information that we're providing. So it's the other consulting firms who very focused only on on a so that's, that's not us cool.

Well, that's, that's really helpful. It's been a really excellent and interesting conversation. So thank you very much. Thank you. Yeah, lovely. Speak to you. And thank you so much for sharing all of that my delight. And thank you so much for having me.

Okay, so you're back with us now. That was our conversation with Alexander about organizational network analysis on a and I thought it was interesting. What were your what were your thoughts?

I thought it was really interesting. Loved talking to Alexander. I think he's clearly someone who's really passionate about his subject and how it might be able to help people i Damn a little bit scared of it, like it feels like there are a whole host of challenges around making sure that you use data like that and information like that in a ethical stance. But I think done right, it could be amazing.

Yeah, it's fascinating, isn't it? And there are some really interesting things that we touched on there. One of the points that Alexandra raised a few times, but that I really like is that what the ONA shows you isn't necessarily a good or a bad thing. It's for you to interpret a line to your organization. So he said, in some instances, it's good to have a silo between maybe your, you know, frontline staff and your engineers, or maybe sometimes it's good to have somebody with lots of connections, but maybe sometimes it isn't right. So on their own, these organizational networks are neither one thing nor another. It's how they fit with our intentions as organizations that I think is quite interesting.

Yeah, and I just it feels like you could end up going down a rabbit hole. Do you like the pun? But I do. I feel like, you know, it's, it's, you could look at it a very light touch way, but also there is a depth to what you might discover and what you might learn about your organization.

Yeah, I'd love to get involved and actually do some of this stuff, so maybe one day we'll do that. You're so funny, I was literally just thinking, I wonder if there's an organization that would

let me, can I just play please? Yeah, I'm sure we'll find a way. In the meantime, don't forget, if you want to learn more about what Alexander and his team do. You can check them out at rabbit analytics.com that's r, H, A, B, i, t, and I guess it's just time for us to say goodbye until the next time. So thanks again for listening, bye till next time. Hi.