E034: Predictive Analytics

This is the world of work podcast with James and James, hi. This is James. Just before we start,

I wanted to remind you that you can read our articles, explore more podcasts, and learn about our online personal and management development programs and workshops by visiting our website, www, dot, World of work.io. All right, onto the podcast. Hello, Mr. James and Jane, and we're coming to you today with a special episode of a world of work podcast. This is part of our summer special series. You know, maybe take it to the beach. I don't know what you do. I mean is that, is that the intent with

this? Well, I think what we wanted to do was there were some people we wanted to have some conversations with, because they do really interesting jobs, or they have interesting roles, they have interesting thoughts about the world of work. And we knew it wasn't a full episode, but we thought it would make a really nice listen to some of our listeners. And so what we've done is we've just quite literally gone off had some conversations and recorded them. Yeah, so

this one today is very much of that kind of mold. And today we're speaking to Christina Dornier wall all the way from Melbourne, which is obviously a long way away from Edinburgh, which is why we're saying all the way from from there. And we speak to her about predictive analytics. She works at culture amp, which is a huge employee engagement organization, and they do a fair amount of stuff in predictive analytics. And we just wanted to explore the subject, learn a little bit more about what it is, how it works, what it means to organizations, what it means to people, and try and bring that subject to light for all the listeners out there, yeah, and

I think as a practicing industrial and organizational psychologist as she is, it's really nice to sort of peek behind that curtain. And for those of you who work in HR or who are passionate about people in organizations, she allows us to understand a little bit more about what the intention is of the organizations using predictive analytics, how they use it, and also how you might engage with that field of study. Yeah, I

certainly found it demystifying conversation. I loved feeling that I know more about predictive analytics than I did before I started. And hopefully all of you who listen will find yourself in the same place

as always. You can connect with us on social media, at the WoW podcast, www dot thewell podcast.org, or also on Facebook, LinkedIn, etc. And if you check out our Twitter feed, you'll see we've connected with Christina as well, so you can understand a little bit more about her through that. But I think probably the easiest thing to do is just to let everyone listen to our conversation and see what they think. Yeah, let's draw it here and head over to the chat. Cool. All right. So

let's jump on to the I guess you know, the core content of this week's episode, and once again, we've got a guest with us. We're going to be focusing on predictive analytics. We're going to be learning a little bit more about what Predictive analytics is. We're going to explore a little bit about some of the mechanisms behind it and what it means for people and how it all works, and hopefully demystify a bit of that for you. But we're doing that with our guest. It's Christina Joni at wall and before we get started, why don't I hand over to Christina and see if Christina would like to introduce herself. Christina over to you.

Thank you. So yeah, I'm Christina doinkwall. I'm an industrial and organizational psychologist based in Melbourne. I've always had an interest in industrial and organizational psychology, I guess for me, people spend the majority of their adult lives working to make that experience, I guess, as fulfilling as possible. Sort of motivated me as a psychology graduate, and even more so now, so I've done a Masters of organizational psychology, and also went on to complete a PhD examining the different factors that affect innovation within organizations. So I'm drawn to thinking about how things fit together, I guess, as a big jigsaw puzzle. So as such, I've worked in roles that allow me to utilize this philosophy. So I've worked in recruitment startups to now one of the leading employee feedback platforms, culture, amp,

cool, it's great to hear you speak a little bit about your motivation there. I mean, certainly when Jane and I decided we wanted to work together on a podcast, the desire to help others was really high on our list of things, so it's something that we find across a lot of the people we speak to, which is pretty cool. So today we're going to focus on predictive analytics. It's a nice couple of words that go together, right? Do you think you could explain it in a little bit more detail to the layperson?

Yeah, sure. It's very, very fancy sort of terms, but are used to refer to, I guess, using a number of statistical techniques to make predictions about something that might happen in the future. So Predictive Analytics uses a combination of techniques from data mining, statistics, machine learning and artificial intelligence, and leverages their combined power to assess historical data to identify the likelihood of something happening in the future. It's become particularly prominent in the business world as of late, due to the emergence of big data, as well as faster and cheaper computers and software to become a real competitive advantage for organizations. So although it's kind of traditionally associated with the banking and finance sectors, it can now be an. Applications such as, I guess, understanding consumer behavior, predicting fraud, as well as in healthcare, Yeah,

certainly. You know, in my own industry, we were in financial services, and there was a lot of talk of predictive analytics and trying to, you know, understand customer behaviors and understand risks and and even as I was leaving, we were doing a lot of predictive analytics work around the employee population in the organization, trying to look at, you know, capability mapping to the future, trying to predict capability requirements, trying to, you know, design change programs to make sure that we had the skills for the future. And I think predictive analytics supported that. So, yeah, so clearly, it's up and coming up. What specific kind of stuff do you do? You tend to predict. Well, I've

worked for you for a few different startups, and I used to work in a recruitment startup, actually, where predictive analytics was being used to predict who would be the best fit within an organization based on the historic Yeah, sorry, I would just say, that's cool. Yeah, yeah. It was really cool. So you, you know, match the historical data incumbents to outcomes such as tenure or culture fit, and then match it against psychometric data such as personality traits or values. So that was really interesting. And also within that field, when I was working within the recruitment space, people would use facial recognition or sentiment analysis to match candidates to incumbents deem successful and put them forward for an interview. So that was a really exciting time. So I was within that role about a year ago, within that space, and it's really exciting to see how different analytical techniques, I guess, traditionally associated with finance areas, being applied to the world of work. But I've since left that field, and there's still a lot of vendors out there, but there is a bit of an issue with them using sort of black, sort of black box methods as well as limited data sets. Since then, there has been evolution from merely predicting but providing more actionable insights within the world of work. So I'm currently working, as I mentioned earlier, for employee feedback platform called culture amp, and we're kind of looking at flipping, taking that, I guess, predictive analytics, even further, and turning it into actionable insights. So I guess one of the problems with predictive analytics is the ability to understand the why is often lost with using black box methodologies. So I think moving forward, it's really exciting to see you know, the capability to provide reasoning behind the predictions, as that will lead companies to have the ability to make to take immediate action to in order, in order to improve their employee retention, but it can also enhance experience of their employees in a more targeted and meaningful way. Yeah, okay,

cool. So I guess just before we go on, you use the phrase Black Box thinking, or, you know, black box methodology around some and stuff. What does that mean to lay people? Yeah, for sure.

So a lot of the time when you use machine learning, there's different types of algorithms that look at all the different possible interactions, kind of like the way your brain works. The neurons fire, and we don't know exactly how they all work together, but there's an outcome at the end. So that's essentially what we mean when we talk about, sort of the black box surrounding some of these machine learning techniques.

Okay, cool. So we don't really understand how the process works, or the people that you're presenting information to don't necessarily really understand the process. Is that right? It's

more so that the actual method itself that you're doing to create the insights and create the predictions is so complex, and there's so many different pieces of the puzzles trying to fit together, we can't actually see what's happening. So traditionally, that's kind of what's been used in finance, and you kind of look at things and you don't really understand what's happened. You understand a prediction. But we've now taken sort of a step backwards and brought the human more into it, and started to have a look at different methodologies where we can actually work out what's going on and get the why factor behind it as well. So that's really exciting and really, really, really positive for working individuals to help improve their lives and work.

Yeah, so when you're doing this into people's face, and it is exciting that you know you're doing all this kind of stuff, what type of data are you using as your input to this this approach?

A lot of companies use different types of data, so we've heard of some companies using things like bio data or scanning your emails and communications and looking at your sick days, anything that can put into you, know your applicant tracking system or your management system, KPIs, feedback data, anything that you're willing to provide to your organization that can be used to sort of get a holistic view and make sort of predictions for various things such as turnover

cool and like, how much data do you guys need? I mean, do you need to have huge reams of data? Does it need to be large organizations doing this? Is it data at an individual level that matters. Is it population data? How does it work?

Different organizations do different things, the more traditional black box methodologies, they require a lot of data. So I remember I used to work with data scientists in one of my old organizations, and they they just the more data they had, the more happy they were. So more accurate the predictions would be. Yeah, and that's definitely the case.

And so I guess if you're looking at using this in your organization, is there like a cost to providing that data? Is that a complex thing for people to do?

It's all about collecting data, making sure that that's meaningful data that's actually gonna be representative of the people. So I guess one of the things I would caution is, you know, if someone's collecting data about you, what would you what would you want them to look at and use to determine your learning, development, or your promotion, or anything like that? So yeah, it's all that sort of data and all the sort of considerations you have to take into account.

Fine. So like, could you explore those considerations a bit more? What type of things are people considering when they're looking at different data sources,

I think feedback is probably the best because getting the genuine truth straight from the employees and understanding their experience within the workplace, and that's probably the most important piece of information you can get. Other organizations like I mentioned, kind of use bio data, use tracking devices. They look at your sick days, they scan your emails and communications for words and use that to make assessments about you. So I think feed always, you know, the safest and most, I guess, non intrusive way to collect data and use for your employees with a with definitely better outcomes as well.

Yeah, and I know your interest, or one of the areas that you're working on, particularly is turnover prediction. How does how does that work? I mean, what exactly do you mean by turnover prediction?

So from a turnover prediction, we're trying to work out who's most likely or which groups of individuals are most likely to leave an organization. So this has been done quite a lot traditionally with things called survival analysis, um. So that's kind of just looking at, you know, over time, who's most likely to leave, um, with basics of analyzes. So that kind of gives you an indicator of who's going to leave, but it doesn't tell us why. And there's a lot of research out there that says that, you know, it's more it's a lot harder to hire in new recruits. I mean, there was a new article recently by Peter capelli in the Harvard Business Review, and it's sort of looking at what are some of the perils of always recruiting externally, instead of recruiting from within, and developing your people. And I think that's really, really important today's society. Because yes, most people will leave jobs quite soon. There's a lot of job hopping. People don't stay as long as they used to. So what is the reason for that? A lot of research says it's not actually salary, it's not compensation, but it's the ability to have learning and development opportunities and those career conversations that really motivate people to look for work elsewhere.

Yeah, that's really interesting. So so it feels like maybe some of the traditional things that used to affect people's retention and departure decisions maybe aren't, aren't as relevant as they were, or were understanding them a little bit or more. You talked to the start of that about survivor analysis, and then you talked about, you know, analyzing the people that did lead is survivor analysis, basically just saying, let's look at the people who stay the longest, and then let's try and find other people like them, because they'll stay the longest, is that what you're getting at with survivor analysis, that's

absolutely right. So I think you can see what the problem is with that is that's making the assumption that you want people who have stayed the longest, they're the type of people you want to work for you. But that's not necessarily the people the most engaged, or the people who the highest performers or reflect the values within your organization. So it's, it's kind of funny, because you're trying to hire people who are willing to stay the longest, but are they necessarily your best people? Yeah,

fine. So I guess it's a bit of a complex decision from an organizational perspective, because there's a cost to replacement, a cost to recruitment. There are risk elements around, you know, organizational knowledge and things like that, retention of knowledge, but at the same time, there, you do want people who'll stay as well. So it's hard to get that balance. I think, when, when you're looking at turnover analysis specifically, are there any like standout metrics that you're looking for, or any standout indications,

I suppose, for that to really understand why people are going to leave. There's a lot of research out there. And also we've done research internally at culture amp that shows people who've been in the organization for the shortest amount of time, so the ones the least tenure are most likely to leave. And as that happens, as time goes on, that dips so people who are there the longest are most likely to stay, which is pretty obvious, right? It's stuff like that. So you really have to think about your onboarding experience. So it's really crucial to get that right and ensure people when they first joined the company, they're showing that they're given those learning and develop opportunities, which is shown to be a better predictor of engagement than something like salary, for example. So it's really ensuring that you understand the people within your organization are able to develop them, and that will help you get more happy, engaged employees that are more productive and reduce that turnover.

Yeah, and do you find at all that that there's any relevance around people's expectations going into the role versus what they actually experience? Is that something? Yeah. Absolutely, absolutely. So is that real? Sorry,

we recently were talking about the psychological contract as a concept, and the idea that people go in with non verbalized expectations of the organizations they're going into. Yeah,

that's right. That's one of our sort of drivers of engagement, which also obviously leads on to turnover intention as well. If people are sold a job that and then when they actually go to do the job, and it's completely different with different aspects to it, they're more likely to become disengaged and look for work elsewhere.

Do you see in the organizations that you're working with that use predictive analytics over things like turnover or other things like this. Do you see over time an improvement in engagement and I guess, the sort of culture fit within their organization?

Yeah, absolutely. So if you use predictive analytics correctly and look at the actual so what component, instead of just saying, okay, these are numbers of people. These are identified people who might leave actually understanding their motives, what their needs are at work, and implementing action to improve them. Yeah, there's a great improvement in turnover, intention, people more likely to stay within that role and be productive and happy.

You talked there about, you know, people using predictive analytics in the right way. Do you see people using predictive analytics in the wrong way? What kind of watch outs might that be as well?

I think I would always question what data you actually putting in, because rubbish data equals rubbish data in, equals rubbish data out. And always ensure that, you know, it's confidential. There's nothing that's, you know, sensitive going into that, or that doesn't really relate to the workplace. So there is a lot of there was a lot of conversation earlier on, a few years ago, about people using data that wasn't particularly relevant to the workplace, and how, how ethical is that, I suppose.

So if you go through and you're delivering a piece of predictive analytics work, how does that process work? I mean, does somebody engage you? How do you manage that process? From the perspective of somebody working in an organization,

what do they see? So I can speak to culture amp, we make sure our data is anonymous and we only present the results back in aggregated level at a group level, so identify pockets within an organization that might be at risk at turnover, and provide them with information on things that will improve the turnover intention, such as engagement, or perhaps they're not having an enjoyable onboarding experience, so, or perhaps there's issues with diversity, inclusion or well being, so ensuring that we provide actionable insights so that they can actually improve the working lives of those people so they retain, remain engaged and within that organization.

And when you say you identify pockets at risk to turnover or higher levels of turnover, how do you present that? Is it? Do you provide specific metrics around it? What is the assertion that you're making at that stage?

Yes, that's right. So we provide metrics in the form of a dashboard, sort of detailing what this group is, what they what is a driver for them, and what you know in retaining in the organization, and also what might cause them to that they're not quite happy with to leave the organization. So it's really providing those insights that you can take action on and do something meaningful with

you talked earlier about having, you know, different types of data, and you talked about the importance of learning and development as a metric that's important for people. And something else that I read and something you mentioned earlier, you talked about things like culture fit and values alignment. How do you measure culture fit and values alignment? It's a million

dollar question, isn't it?

Yeah, I've worked for many organizations where that has been quite a difficult thing to measure, but it's really about I think you really have to understand it yourself before you try and measure that. So seeing whether people agree with the sort of values you have, whether they exhibit them, so in a behavioral sense as well. And through that way, you can do sort of a matching from that. But yeah, it is, is the golden question, how to measure culture fit and whether certain methods are actually useful or not?

When, when you're looking at things like culture fit and things like, Do you look at whether or not leaders are living in line with, say, the organizational values and the espoused culture? Do you see? Do you see a difference between, I guess, what's espoused in the organization and what's actually the lived experience of people there? Oh,

definitely, definitely. Um, there's many, many, many organizations who will espouse certain values, but when you actually get down to the grid of it, they're not actually embodying those values. At my current organization culture, we are really, really, really into making sure that we're all living and breathing our values, and that's really driven from the top up, and you really feel it as soon as you enter that organization. And that's something that really makes it different, if you're trying. To, I guess, change a culture, or ensure that a culture has particular values. And

I guess I'm really, one of the things I'm really interested in is how to support people in organizations trying to create change. So if you, if I'm an HR manager and I'm working with culture amp, or an organization like that, and you've got some bad news, or certainly, some helpful, useful information that requires us to make some changes at leadership level. How? How useful can predictive analytics be in making the case for sort of middle managers to push upwards? It

really depends on the culture of that organization, right? So one thing that's really useful is we have those hard metrics behind it. So sometimes having the business case, taking that for middle managers, to take that up to senior levels, to the exact team, is really powerful data, and being able to show that small change over time. So one thing we're really quite passionate about is making this is targeting this one or two things that can actually change, so you can show that there's a difference very quickly and show the quick return on making that change. So it's really targeting those small wins, I suppose,

so making those quick wins early so that that person can then feel a bit more empowered and evidenced

Absolutely. And it's all about that's all about enabling them and ensuring they're empowered to bring forward those cases towards their leadership team.

We had another conversation with somebody else in another podcast where we talked about, I kind of, I guess, the willingness of leadership to accept different ways of thinking and different ways of looking at things from the perspective of providing people with data that supports changes to their organization. How accepting Do you think leadership is of the type of data you provide them and the insight that you provide them. Do you see any pushback on it? Or what's your experience?

Not very often. I guess we we're very lucky. Well, I'm very lucky. I work with a lot of organizations that are hungry to make a change in what they're doing and to become a culture first organization. So a lot of the time, the pushback isn't so much to do with the metrics themselves, but actually fitting that into all the other things they've got on their to do list. Yeah, and that's sometimes where the real challenge and pushback comes from.

And where you say that the organizations want to be a culture first organization. How much of a priority Do you see people actually giving to this type of Change Program? And the lessons I can learn from predictive analytics, it

really depends on the organization, but it is definitely increasing since I started my career 10 years ago, people are definitely, definitely becoming more interested in making sure that people are happy, engaged, that they're working towards common goals. That's become a real, real key priority, and that real, the human has really become the center of work. Has really moved to that that change. So even though we're using, you know, machine learning and artificial intelligence to help us, it's still to aid the better of the person, which is really great to see. I

got a question for you just on the backpack. So it's, you know, putting the human at the center of this stuff and being more human focused seems to be an increasing priority overall. What's your your sense of the employee experience and engagement over the last 10 years has that changed. It's definitely

become more and more important to individuals and employees. People like sort of mentioned before. People aren't leaving jobs because of salary, as they may have before. The main reason people are leaving because they're not given the opportunity to grow and develop their careers so and also that sort of if they don't feel like the organization has the same goals or morals or values as them, that's a really high, high reason people are leaving and looking for work elsewhere. So it's definitely seeing a change in that in the last couple of years.

And do you think organizations generally are getting better at being places that people want to work or do you think they're just putting more effort into it? Or is it? Are they improving?

They are definitely improving. I work with the most corporate organizations that very much about the bottom line, not about the people, and they're really trying to make an effort. They're starting to embed that within their strategy and really make tiny steps to make improvements in that area

you talked about. You know, when you provide, I guess, your outputs to organizations, you've gone in, you've done some analysis, pulled together some data, you've turned it in black box, or otherwise, you've created some metrics, a dashboard to share with leadership. You've provided some insights on the back to them, and I guess you sort of propose certain actions they could take in relation to different metrics. What sort of timelines Do you see on the back of that? How long does it take to go from that stage to implementing some change initiatives, to change the way things are? The

way we collect data now is instantaneous, so we can get those insights in real time, which is really fantastic. And once those insights have come out. It's really up to the managers and how they're able to push that through. But yeah, depending on the size of the change initiatives, they can take a couple of months. Depending on sort of small wins that they take, it can be very, very quick to start to see some of those changes. And do you how

do you find the response of the workforce when you guys go in? So I I know when. We even did some I was working with a relatively small organization, and because the nature of the smallness of it, even collecting self reported data creates some nervousness, because it's really hard to disguise yourself when there's only sort of five of you in a team. How open are workforces? Or how do you manage that to make sure that they still feel confident that they have their own privacy and stuff. It's

very much in the communication ensuring that they feel comfortable they understand that the data is anonymous. One thing we also import is reporting minimums. So depending on the size of your organization or the different demographics within that organization, we only report groups with a minimum of five people, for example, so that they're not identifiable, we also ensure that people can elect whether they want to provide any of that demographic information which might identify them as well, and that's really important to make them feel as if they're not going to be singled out,

is that, like employee segmentation, data around age, location, gender, all that kind of stuff. Is that those stuff people can opt out of? Yes,

correct and so and it sounds like you guys do a really good job of trying to manage that, thinking about it, just with your bigger IOP hat on for a minute. Does it worry you a little bit that there are other organizations that might sense there is an opportunity to be less ethical about it. Or do you get a sense that generally the sector is fairly responsible? Generally

the sector is fairly responsible, but I have come across some cases, larger sort of organizations where they are looking at the nitty gritty of the information they want to try to identify people. And that's not really beneficial. It's not about pointing out individuals and, you know, punishing them, or what is whatever. It's more about, how can we make everyone's working life important and as enjoyable and meaningful as possible? How can we improve that for them? And yeah, I think overall, people are really, really trying their best, but unfortunately, sometimes, you know, there are some cases that slip through the cracks and where it can be a bit of a problem. Okay, so just on that, if

I'm, if I'm working in a in a business, and I want, I'm not, I don't know an awful lot about predictive analytics, but I've been asked to explore it. I think it's really exciting. What sort of questions could I ask an organization like yourself to make sure that they are an organization who value that kind of privacy and that kind of ethical approach?

It's really asking them what data they collecting and what they plan to do with that data. It's really about Are you are you getting a holistic view of your employee experience within the organization, what sources of information they using, and if, if this was your employer collecting data about you, would you be happy if they were using that to determine things such as, I guess, raises or compensation or promotion? So that's a reason. It's also and it's also really important to think about and ask the questions so YouTube analytics and vendors or techniques out there that that will tell you a prediction, but what can actually, what can you actually do with that information to improve people's lives at work?

That's really helpful. Thank you, Christina. I just think there'll be people listening that will start to see this coming over their desk, and it helps to empower them to make sure they're making good decisions. We

were we talked a little bit earlier on, you know, diversity and things like that. Do you see like differences amongst data, between the different industries, you look at different countries, different locations, maybe different diversity strands, things like that? Absolutely.

So there are so many differences between cultures, industries and diversities. So it's particularly powerful to be able to create predictive models unique to an organization's particular set of intricacies. There's a large body of research out there that shows, for example, that with turnover, the factors are going to be different depending on industry as well as location. So it's really interesting. So research at both my companies or externally, has found new employees are more likely to have a higher turnover than olders. So like your older employees, and we're looking at functions, front of house, sales representatives, people and experience and marketing turnover, whereas leadership from registered nurses are more likely to remain in alternative. So that's why it's important to use models look at your unique data.

Yeah, okay, and then I guess, do you benchmark against broader industry trends or larger data pools? I mean, how do you do you do anything like that to get further assurance. Yes, we

do so taking into account data collected because we in our collective sort of intelligence set, so we've got data from 1000s and 1000s of individuals and companies worldwide, and ensuring that that's incorporated into the models to get that accurate prediction.

You know, when you talk about predicting some. Been like turnover or something else that you talk about. How confident are you with your predictions? Recent articles recently come out about IBM being 95% confident of the ask about that. Yeah, yeah. So

yeah, 95% accurate prediction. So it's really hard to know with predictive modeling. So I think one thing you got to ask yourself is, how holistic is the approach, particularly, I guess, with organizational predictions, as nothing happens in isolation. So individuals exist within teams, which exist within organizations, which exist within industry. So see, it goes on and on, so the more pieces of information an organization can incorporate into the learnings, the better the predictions. But like I said, we also have to be cognizant of where we're getting the data. So previously, organizations have script their employees, emails, account, etc, which can lead to questions with regards to privacy concerns on

that. I mean, you know, is it possible to use predictive analytics in an organization without changing the organization. Absolutely,

we collect data from our employees all the time, so you'll be collecting a lot of companies do engagement surveys or onboarding exit any sort of feedback they receive from them, utilizing that to really understand about their their entire employee experience is really, really, you know, we have a lot of, a lot of data at our fingertips that we could use for that.

I guess. Do you think that people change their behaviors if they know that they're being, to some extent, analyzed through the data trails that they leave? Does that happen at all? Do you know, um,

depending on how the data is being obtained, I guess they do definitely. But it's about being clear with your employees and being very transparent about what you're doing with any sort of data you collect with them. They're more likely to be honest in their responses. Yeah, a

lot of it comes back to that intention, doesn't it? I mean, and we've talked about the intention of improving experience, of improving, you know, quality of working life and things like that. And we did touch earlier, but maybe some people aren't as well intentioned as other. But I guess if you get that intention across them, that's really, that's really helpful, you know, indicator to organization to what's going on here. Um, you, you mentioned something in something I saw the other day about potentially starting to bring in predictive analytics for turnover that includes things like employee performance. Can you say a little bit more about that? I mean, how do you how do you interpret and pull together data on employee performance? If you can speak about that? Yeah.

So this is a massive buzz within our product camps at the moment, so it's a bit of a top secret within my organization, but it's really exciting. So yeah, we're implementing an integrated solution that will help our customers drive the development and performance of their employees across a comprehensive set of measures that will help them reduce bias, inspire action, improve performance, so both at the individual and company level, and ultimately help create high performing cultures. So I guess what I can say to that it's about back to that feedback, collecting that feedback from employees that they're willing to provide in a very consensual way, and that, you know, is not going to be of detriment to them. So using that performance data in that way as well, and

in some ways, that's, well, I'm making assumptions here, but in some ways, collecting data with the intention of helping people to form better, somehow feels like you'll get more collaboration potentially. I think there's something that some of the academic research I've read around turnover, the challenge is always that there is a there is a for those who genuinely are considering leaving. There may well be a genuine reason why they're not acknowledging it to themselves, even yet, or thinking about it, whereas when you start talking about performance, that's something that you you know, if your communication is right, you can get the whole team on board with right.

Yeah, that's right. So yeah, there's a big shift in performance and how we talk and think about for performance at the moment, and how you know, you look at it with a more developmental focus, and that's more likely to be less of an icky conversation, because a lot of the sort of traditional ways we've been combating performance at the moment, No one enjoys the performance conversation, and it's even harder because you're only doing it every two twice a year, maybe. So it puts this real barrier between actually communicating and understanding what the developmental needs might be of an individual.

Do you think do organizations need to be at a certain kind of level of development or maturity before they can start to use predictive analytics? Are there any like prerequisites that you know, listeners might need to achieve before

they start looking at this type of stuff?

I think anyone can use predictive analytics at this point. We all collecting a lot of data, and it's really about sitting down ensure that, if you are hiring data scientists, that they're working in multi disciplinary teams with people who understand behavior, such as psychologists, and that way you can really understand what that's happening with data and ensure that it's being used in, I guess, the right way. Cool.

And do you for you working in a multi. As a psychologist in a multi disciplinary team. Do you? What are some of the benefits for you as a practitioner in that space?

I absolutely love it. So all my all my favorite workplaces have been where I get to work with developers or data scientists or customer success coaches or sales people. For me, it's the real beauty of seeing the world through a different lens, and also being able to enable others to understand psychological theory and how, how different factors might impact someone's life. So I think for me, that's my favorite thing. It's always, it's always a bit of a challenge to get on the same page sometimes, but it's really, really rewarding. And I guess the outcomes that you get from that, in terms of innovation, phenomenal, yeah,

and I think, I think certainly, as we move to a place where we can collect more data as organizations, then we're in a place where there's a reason for different disciplines to come together, right? Because they all want, they all want to have a look at the data. They want to analyze it their own way. And if we can do that as a group rather than as individuals, I feel like we could move organizations much quicker forward with that, definitely,

and that's definitely and that's definitely what I've seen in my practice over the last couple of years, is teams that are working from different disciplines together on a task will achieve it a lot quicker than those trying to do in isolation. It's trying to share that knowledge that we have in different disciplines and use it in new ways.

Okay, brilliant. I think that's all the questions I had. James, anything from you? No, that's good.

I thought that was really interesting a great overview of predictive analytics. So nothing extra

for me. Christine, anything else that you would feel like we haven't covered, or that you'd really like to talk a talk about or share with the audience? No, all good from my end. Thank you. Okay, so

just to recap, we've talked a little bit about what Predictive analytics is, how it can be used, some of the responsibilities and the ethics around using them, and also some of the experiences of you yourself in that environment. It's very much an introduction to the topic, because we know it's quite new to some of our listeners, although some will be much more versed in it already, but I'm sure they'll have enjoyed to hear from behind behind the curtain, so to speak. But hopefully we've, you've, we've given everyone a little opportunity. Thank you very much for your time. I know it's evening time, your end of the world, but it's it's been a real pleasure to hear a little bit about your work. And just from my point of view, it's really lovely to hear about someone who's so passionate and enthusiastic about their work. And given that what we're trying to do is help other people, it's a, it's a real endorsement that you, you come to it with such a, such a passion and enthusiasm. Thank

you. Yeah, definitely, definitely something that's important, okay, definitely something I'm motivated to work towards. Brilliant.

So that's about it from us, and time for us to say goodbye to down under James, yeah, it's been a pleasure chatting to you. Thank you very much for your time, and I'm sure our perhaps we'll cross again at another point. Yeah. Thank you very much, Christina. Have a great evening. Thank you. Thanks for having me. Cool. So

welcome back. That was our chat with Christina. I thought it was really interesting. I thought there's a lot of interesting topicality around predictive analytics at the minute. It's one of those things where, you know, the growth of computing power, of data, storage of big data, of interest in data, has really broadened this field up. And so lots of organizations are starting to explore, you know, this definition of predictive analytics, and really trying to look for, I guess, insights and an understanding of their their teams through data. Now, yeah,

and I think probably a couple of things that I really liked, I love that she called it outright at the beginning, but it's just statistics, but in an applied approach with a specific purpose, right? Yeah, because I think that's really important. Sometimes we hear these terms predictive analytics. There's loads of terminology that comes into the business language all the time, and it sometimes feels deliberately a little bit obscuring of what's going on. Well,

language is such a powerful thing, right? I mean, people bring it in and it sounds special, right? I mean, yeah, you know, I might not want to buy statistics, but I might really want to buy predictive analytics,

yeah. And I get, absolutely get what it's calling that, because what they're doing is they're extrapolating that data to try and make predictions. But I think it's, it's good to hear that honesty, yeah. And I think the other thing that I really enjoyed with my ethical hat on is how clear she was about her own personal her organization's approach to data privacy, and what does and doesn't get shared with clients, and what can and can't be collected. And I think, I think in a world where there is always going to be a drive for more information, giving people the tools, the questions that they can ask of an organization to make sure that they that their organization they are procuring from is behaving ethically, is really important. Yeah, you want it to be the right organization, yeah? And I thought that was, I thought that was especially helpful. Yeah, that was good. What

another thing that I really liked was, I guess, kind of a focus, to some extent, on intention. So clearly, you know, a lot of Christina's motivation in this is to help people have better working experiences. And I think a lot of people in the field are doing this and following on from that. The bit that also stood out for me was the need to take data from just data to real insight and action, right? So you use data science and you use predictive analytics to identify. Like specific areas that are predictive of a future, but then it's, what do you do with it? Right? You know, we've got this data, great. How are we going to make it better for our organization and better for our employees and colleagues as a result of having that data so that real, sort of applied nature of it, and, you know, using it to make changes to organizations, I thought was really powerful. Yeah, yeah.

I think, I mean, overall, I've come away from the conversation thinking, you know, if my organization was using people like Christina, yeah, to look at my data, I'd be pretty comfortable with that. Yeah. And so I think as an overriding field, it's one to watch, and it's one to understand about. Because I think as as organizations try to extrapolate more and more information from a given set of data, we need to understand that, even as individuals understand where our rights lie, but also how our organizations might be able to use our data to help us. Yeah,

and as data pools get bigger, as data costs reduce, this is the type of technology and practice that will reach out and be, I guess, affordable, accessible and potentially beneficial to a wider range of organizations, so it could end up being used in very small organizations, looking at comparative pools with other data sources.

Yeah, so that's our quick, bite sized look at predictive analytics with Christina Dorney at wall from culture amp. We hope you enjoyed it. As always, you can find us on social media at the WoW podcast or on our website. You can also sign up to the WoW mail at our website, but for now, it's goodbye from me, and it's goodbye from me.