In this week’s podcast, Aoife focuses on a topic very close to her heart - data and the impact of a data-driven workplace culture. Joining Aoife to explore this topic further is special guest Natalie Cramp. Natalie is the proud CEO of London-based data agency, Profusion, which specialises in helping businesses become more efficient, effective and profitable by embracing data science.

In this episode, Natalie discusses the path to transforming organisations with data, how transparency is a confidence builder and how data is a key ingredient to creating happier work environments. Key points throughout the episode include:

- An introduction to Natalie Cramp.

- Decision-making processes in the workplace.

- Employee performance management: Identifying your organisation’s top talent.

- The starting point of organisational transformation.

- Using data to drive your business strategy.

- The power within aligning HR and Marketing teams.

- Why it’s time to embrace data-driven decisions.

- The importance of asking the right questions.

- Employee retention: securing your top talent.

- The benefits of checking in with your staff and listening to your employees.

- Understanding your company data and business trends.

- How data can support employee engagement during a hybrid and remote period.

- Using data to track inclusion in the workplace.

- Insights and success stories of data implementation.

- What Happier at Work means to Natalie.

THE LISTENERS SAY:

Do you have any feedback or thoughts on this discussion? If so, please connect with Aoife via the links below and let her know. Aoife would love to hear from you!

Connect with Natalie Cramp:

https://www.linkedin.com/in/nataliescramp

https://profusion.com/

Connect with Happier at Work host Aoife O’Brien:

https://www.happieratwork.ie

https://www.linkedin.com/in/aoifemobrien

https://www.twitter.com/HappierAtWorkHQ

https://www.instagram.com/happieratwork.ie

https://www.facebook.com/groups/happieratworkpodcast

Are you looking to improve employee engagement and retention? Do you struggle with decisions on who to hire or who to promote? I have an amazing opportunity for forward thinking, purpose led, people first organisation to work with me on the first pilot Happier at Work program for corporates. The program is entirely science backed and you will have tangible outcomes in relation to employee engagement, retention, performance and productivity. The program is aimed at people leaders with responsibility for hiring and promotion decisions. If this sounds like you, please get in touch at Aoife @ happieratwork.ie. That's A O I F E @ happieratwork.ie. You're listening to the Happier Work podcast. I'm your host Aoife O'Brien. This is the podcast for leaders who put people first, the podcast covers four broad themes; engagement and belonging, performance and productivity, leadership equity, and the future of work. Everything to do with the happier at work podcast relates to employee retention, you can find out more at Happieratwork.ie.

You can actually use data to explain right these are decisions we're making and these are the reasons why. All of a sudden people feel more content because they understand it, they can see what's happening. They don't feel that something's being hidden from them, and then they feel more secure. When you feel secure, you are happier at work.

Hello, and welcome to this week's episode of the Happier at Work podcast. I'm so delighted you could join me today. My guest today on this interview based podcast is the CEO of Profusion, Natalie cramp. Natalie is a digital marketing and startup operations expert. She has more than a decade of experience in leading private, public and third sector organisations to significant periods of innovation and change. This includes creating and scaling tech solutions for government organisations, and developing the digital capability of third sector organisations as well. Currently, Natalie is the CEO of data science consultancy Profusion. At Profusion, she leads a team of 60 consultants, data scientists, data architects, developers and digital marketing experts. She is responsible for a Profusion strategic direction, the expansion of its product offering and the growth of its blue chip client base. Now, as you can imagine, myself and Natalie have a really interesting discussion all around the importance of data in organisations and how we can use data to make better people decisions to be more transparent and to be more fair or fair at work. I thoroughly enjoyed this conversation with Natalie, with my own background in market research and data analytics and a real passion for bringing more data into how we make people decisions at work. You know, we had such an interesting conversation around how we can use data, what are the important things to look out for? And what are the kinds of problems that we can solve at work using data. As always, I do a synopsis at the end of the podcast today. So if you want to stay tuned, listening for that, and we'd love for you to get involved in the conversation as well. So you can do that through social media, the two main places that you'll find me on LinkedIn, Aoife O'Brien - A O I F E O apostrophe, B R I E N. I'm spelling that out for the non native Irish speaking or people who are not familiar with how to say and pronounce Irish names. You can also connect with me through my website, happieratwork.ie. And I'm also on Instagram @happieratwork.ie and I hope to connect with you there. I hope you enjoy today's episode. Natalie, You are so welcome to the Happier at Work podcast. I'm absolutely delighted to have you as my guest today. And I'm excited for what we're going to be covering today. Would you like to introduce yourself to listeners give a bit of a flavor of what you do, how you got to where you are things like that?

Of course, it's really lovely to be here. Thank you for having me. So yes, I'm Natalie, I have had a little bit of a squiggly career path, as I think they're now calling it.

I think everyone has. And I love that term.

But it really there's sort of common denominator has been about how do you scale big movements of people and what's the digital and data, sort of underpinning that you need to do that, in order to solve problems, whether therefore, a business or whether that's for society. And so I sort of started life as many people do as a management consultant with Deloitte, trying to tackle sort of people people related transformation challenges, and then moved to work for London 2012 for four years to mobilise the movement of 200,000 people that we needed to make the London 2012 Olympic and Paralympic Games a reality. Just obviously an amazing experience and and learned a huge amount there. But and that was really where I was doing so much more rudimentary data work to really understand the makeup of that. 200,000? And would we even be able to get those people? Would we have shortages? And how would you address that ahead of time? And how would you not drain London have the resources that you need, because they also needed hospitality people, with everybody sort of coming to see it. And then built the Mayor of London sort of legacy program from that called Team London, which was a movement of volunteers across the city to try and make our city a better place. And how do you support charities to be able to work more effectively with businesses? And how would you inspire the next generation of volunteers to be able to use it as a route to get to work and build the skills they needed to get to work? And also, how do you make volunteering sexy, again, to be honest, and connect with the community that aren't currently volunteering? So, you know, the, the sort of traditional volunteer workforce was actually dying out and you can't say to somebody, yes, we need you to come three days a week and spend lots and lots of time with us when they've got busy jobs and demanding, you know, demanding personal lives or they're a student. And so how do you connect with that younger generation and get them to volunteer and technology and creating the right opportunities, and this sort of speed networking speed volunteering app, which enabled people to sort of gamify it a bit and check in became, became the way to do it. And then moving on to work for a startup, which was trying to fix problems between business and schools connecting to be able to improve careers education. And of course, if you're going to do that across the whole country, and work with different schools, you need to have the right digital and data underpinning. So as a huge movement of volunteers to support schools, it was a huge movement of people to inspire the next generation. But in order to make that a reality, you had to understand the state of the nation, you had to understand what was happening and data tools were really key to that. And that led me to then take up my role as CEO of Profusion, which is a data agency in consultancy, and I sort of, I love solving problems. I'm a fixer, like, my friends will tell you, I'm a total fixer. And I really believe data as a way to solve our problems now and in the future. And we work with all sorts of businesses, large and small. So from footsie 100 companies, to charities, to SMEs to government. And it's all about how can data really be at the heart of every decision your organisation makes. So how do we put data in the right hands at the right time to create the value that you need? And that can be about increasing your customer engagement? It can be about improving your innovation, or really critically, it can be about how do you drive a more inclusive organisation. And as somebody who's always been involved in big movements of people, I'm really, really passionate about how you bring the two areas together, we've seen the lack of progress we have made as a nation as businesses in a really critical area and diversity and inclusion. And you know, we've been trying to do this for 1015 years, and we're making very, very slow progress. And it's still supposedly at the top of the boardroom agenda. Because we've not made enough progress, we've now had to adapt to the way of working to be more global, but also to deal with hybrid working after the pandemic, and change the ways that we engage with our staff. And I think there's lots of ways that data can help to enable people to ultimately be happier at work, because workplaces are running in a more inclusive way to enable HR teams to have an easier life and be happier at work. And so it's sort of really brings my two passions together, in terms of the work that we do with people in HR teams and supporting line managers to really use data to support their their people, because our people are our greatest asset.

Yeah, yeah, you're speaking my language. Now, Natalie, when you say things like using data to solve problems, I'm a total problem solver as well. It's what I've done my entire career, it's something I can you know, and it's, for me, it's about finding something I really care about, that is a problem that I do actually want to solve. So, you know, going beyond just helping big companies to sell more. It's about how do we create happier working environments exactly, like you said, but using data so it's not just like, sometimes I feel that people think this concept of being happier at work is airy fairy, it's a little bit fluffy. And I'm sure listeners of the podcast realise that it's not but there are other people out there who maybe don't realise the full extent of you know, it's it's actually real, it's scientifically backed and how can we actually do it? And so maybe, you know, let's kind of jump a little bit back and say, Okay, so from me and from from what I can see out there in the market, there's not a lot of people who are currently using data at the moment to make decisions.

No CIPD are saying only 6% of HR professionals are using advanced AI analytical techniques to make business decisions. And 3 0% collected news very basic, basic HR data. But I mean, that's been the case for years.

It hasn't actually changed over to time.

You know, the 6% that has grown. But 6% is so, so small. And what's really interesting is, by the time this podcast comes out, we will have some research that will be out. And it's really the the key themes from it is that employees are demanding an era of transparency, they're demanding more transparency, and how decisions are made. And they want to feel secure, they want to feel that they've been treated fairly. And I know there's some nervousness about using data to make decisions. And we see things in the press, right, we see the Amazon algorithm that only hired white men, and people really panic. But there are ways that you can avoid that happening. And I certainly don't advocate for data to be used instead of people. And we shouldn't be automating our decisions about people. So you know, data can be used to predict your top talent. What I'm not suggesting is that should automatically fire out emails and say you're promoted, you're not, right, yeah, that's not the right answer. But we do see that people often get overlooked in a promotion process, particularly cultural factors affect that. So I might not be prepared in a large company to jump up and down and tell everyone how marvelous I am. And other people might be. And other people might be able to go to the drinks events to network, and I might not be able to. But that doesn't mean that I'm not doing a great job. But actually, if you are less aware of my work, it's, you know, through no fault of anybody's I may get overlooked. And so what data can do is to inform the people who are making the decision. So it's not just a subjective decision, but it's a data driven decision to understand actually, who is sharing the characteristics of real top talent? What success are they having, and try and make that a more balanced and inclusive conversation. So it's the sort of partnership between the human intelligence because of course, this is about people and how they connect with people, how they work in the environment, and there's things that data will never be able to show you. But also using the data to make sure that the decisions you're making are grounded in reality, and not in some subjective opinions. And that's what people are telling us they want. That's what employees are saying they want. They're saying, even though feels a bit nervy having people look at my data, because it all feels very personal. And obviously, you see a lot of things about data privacy in the news. They say they would rather that data was used to make decisions and the current current way it's done, because it feels like it's just someone's opinion. I think that's really interesting.

But you're absolutely right. Like there's there's a few things to unpack there Natalie. One of them is this idea of it being subjective. And I would absolutely agree. I've been at the other end of that process where we're discussing people, and it's very much people's opinions of how people perform at work. The other thing that springs to my mind is, you know, if someone is an introvert, so I'm an introvert, but I'm a social introvert. So I would go to those social occasions. And I And over the course of my career, I've learned to tell people, what I'm doing, what successes I've had. But I think there's a lot of people out there who who just don't know how to do that. And it's so it's so important to be able to get recognised that it's not, it's not about politics, and it's not about who knows you it's about actually the job that you're doing, or at the very least educating people on on how this works. And exactly like you're saying, actually this idea of transparency, and how are those decisions actually made? I would have loved to be transparent with my team and say, well, this is what happened, actually, we all gathered in a room and we looked at the ratings that we gave you, and we compared you to each other. And then we decided, you know, well, that person compared to that person? Well, actually, no, I think you need to bring that other person down, because they're not quite as good. And you know, and, oh, we have to have this split, where it's only 10% can get the top rating, and we have to earn whatever it was okay. Yeah, exactly, you know, and the nine box grid and not everyone can be in the top right corner where they, they, you know, they get the top rankings and what they do and how they do it. But I think bringing a lot more transparency around that. I mean, that's that entire conversation in itself is probably a conversation for another day, you know, this whole performance management and how do we do it properly? And how do we move away from the nine box grid? And how do we support people and how do we not tie up our pay with what people say about us and all of those kinds of things, but um, I suppose for me, it will be great to understand if someone's starting on this journey, and I'm sure there's a lot of people out there listening who are who are. Maybe they're not even just doing it yet, like you said 6% Like that's, it's a pretty shocking number to be honest. As someone who knows the power and the value that He can bring to an organisation like that 6% Or only 6% are using advanced analytics is, is pretty, pretty dire number and, you know, that's a UK number. It's probably even lower in Ireland to be honest. And this is what I'm seeing, like when I talk about things like people analytics, people that I used to really glaze over, they don't really no. And I think traditionally the people who would be using that data for the likes of page or desktop, their strong area of skill, you know, they're they're good at relating to people not necessarily looking at numbers. So back to the kind of the question I have about this is if someone is looking to get started, like, where did they even start?

Yeah, for me, it all starts with education. And I think I'm really passionate about how we support HR professionals to get this knowledge, because if there's one thing HR professionals know how to do is transform organisations and transformations work because of people and culture. Any technology transformation, you know, so many technology transformations fail, and they fail, not because the tech is right or wrong, they fail because of the culture. And people and people being prepared to change and the processes put around it. And HR are the ones who knew how to make that happen. And I really want to see a partnership between HR teams and data teams to transform organisations with data because the data teams are great at doing the stuff, but they're not the ones who are going to create the culture change. And they need the partnership of somebody who understands the people in the business who understands the levers that they have, who understands how to make people tick, we saw it with diversity and inclusion, by you don't become a diverse and inclusive organisation. Because you hire a head of diversity, you become a diverse and inclusive organisation, because every line manager understands responsibilities and puts it into action, their day to day role. Every single person in the organisation recognises the things that they need to do the way that they need to behave to people to make to make it an inclusive place. And all those different things are happening across the business. And so you can't suddenly become a data driven organisation by hiring a data team. It's sort of how do you transform the organisation and the processes in it? How does the person be they in HR doing the payroll, or be they you know, in the sales team? Or be they in the logistics warehouse? How do they understand the information they're being fed and interpret it and take the next best action in their day to day role that's going to support the business. And so for me, education is the start point, because actually, we really need our HR teams to help us drive this across the business. But actually, they're not even doing it for themselves at the moment, let alone supporting the transformation across the business. And transformation starts with education. So we did some really interesting research on data literacy. So we've created this data literacy analysis, which we make everybody do before they go on our data for Leaders Course, which is all about supporting leaders to understand how to ask the right questions of data, how to what the art of the possible is with data, and basically how to avoid things going wrong and spending a lot of money on stuff that's not going to work. So we make them all do this assessment. They of course, get very competitive when we benchmarked it against 300 leaders across across the country. And actually, HR didn't score very well on it as a as a function.

That doesn't surprise me.

They're all quite rubbish! CEOs, by the way, they are the worst. We're only 30% data literate, but the average is only 34% of leadership. And you sort of think, well, if that's what your leadership understands, then how can you possibly use data to drive your business strategy? And I think it's the same for HR, right? How can you possibly use it to drive your HR strategy, and actually HR have a lot of data through ATS systems through the information they gather in application processes, through the employee engagement surveys that they send out? There's loads of information and Intel in there. But it's just the currently not being put together and used to answer the key questions that they're trying to tackle. And so starting by just doing some data literacy training, by understanding what the art of the possible is by understanding how do I, how do I go from I've got this HR strategy, how do I translate that into a series of questions to ask my data talent, whether that's a data team, whether that's an external partner, whoever it is, and that's got to be your grounding. The other sort of tip I would give HR teams in terms of getting going is to talk to your marketing team. Bizarrely, the two teams don't seem to talk in the organisation very much, but they are both trying to attract, engage and retain people. Yes, your HR team are doing it for your own people and marketing are doing it for your customers. But it's the same process you're trying to achieve the same things. And marketing teams have typically been faster to use more modern techniques in this. There are of course, some ethical things you need to consider when you're then transferring marketing practices to HR. Obviously, with customers you want look alikes of who spends the most. If you predict if you looked at your top talent. You don't necessarily just want To replicate that, because as we saw with the Amazon algorithm that creates bias based on who your talent has been, so yeah, you may not achieve, but there are ways to manage that. And so I think having a conversation to see how marketing, you're using these techniques and how it's helped them, and how they started out, there's probably lots of shared lessons that can be learned. And so it's another really simple step, just to start with, and see, there's probably quite a lot of stuff in your organisation that you can actually already apply and use and just start playing with it.

It's so funny you say that Natalie, just because that is my background is market research, and, you know, taking that data and exactly that. So kind of isolating specific types of behavior and looking at, you know, tracking that over time and, and things like that. So, it's so interesting. And it's, it's so true, as well, that the two teams are so interlinked, and yet they don't necessarily talk to each other, or see how they can maybe help each other. There's a few things kind of coming up for me in relation to what you've said. So I suppose the first thing is that this idea of data literacy, and I don't know are people afraid of, of what it is, but if you think of the likes of a, like, say, a marketing director, or a finance director, surely they know their numbers, and they know how to show whether or not something they're doing is working, you know, whether that is I never worked in finance, but I'm trying to think of some examples. But like, you know, even things like revenue or things like, you know, collections or something like that, surely they know how to translate what it is they're doing, to show the impact that that's having on the business. And from a marketing perspective. You know, this is how to show when we ran this campaign, and this is the impact that it had, these are the impressions that have these are the, you know, all of these different kinds of things. So maybe people shy away from this idea of old data. I'm not I'm not data literate, but actually, you're using it all the time, but you just maybe don't use that language.

Yeah, I mean, we, we use it every day. I mean, AI is just looking at, you know, some a device that can perceive its environment and take action based on that, right. So actually, as humans, we execute on that all the time, you know, you go and stand at the traffic lights, you take in to account what's going on and whether you cross the road or not. And then you sort of cross the road, and you make a judgment call whether you move faster. And we we as humans process huge amounts of data on a daily basis, which we learn from as we get older, you know, you start at the baby, you don't understand it all and you sort of build it in like, we have our own algorithms running essentially in our bodies, right? We see what's going on we process it, we use the knowledge we've already got, and then we take action based on it. And that's sort of what data does as well. But I think the problem with data and AI particularly is it's got this sort of mystique around it. What is artificial intelligence? Is it the robots taking over the world? I was no good Terminator. Yeah, I was no good at science at school. I couldn't possibly understand that. And actually, and the problem is the people who do practice it are geeks who like to talk in geeky language, right? And love them all dearly. They work for me, but and it's it's that translation piece. It's really difficult. But actually, it's not that it's not that scary. It's not that complicated. And actually, it's where we see it going wrong, that people aren't applying normal business logic and normal business processes to it. And so, actually, that's why it's not always creating value for people in their organisations. And a lot of the stuff we have we teach on our data for Leaders Course, and our data Fundamentals course, is essentially basic business planning. You know, get your question, right, ask the right question. Don't send people off sort of down rabbit holes with vague briefs, right? We've always known that. That all sorts of other things that we do, you wouldn't just put a job spec out saying, Oh, we're quite interested in young talent to come and help us with some marketing. And think, oh, yeah, now we're going to be able to hire a marketing analyst, would you right? But that's what we do to our data. We basically say, Oh, we want some personalisation. Or we're sort of need help to help me with performance management. Right? What what question are you actually trying to answer? Yeah. And so and that's where we sort of see things go wrong. And so I think it's about sort of, as you say, not being scared of it. And recognising it's something that's part of our daily lives that by wearing an Apple Watch, you're contributing so much data to someone else, you know, you're looking at your data every day we are by opening an email. We're constantly having these value exchanges of data in our lives, where you're saying, okay, I will allow people to know more about me. So I can have a more personalised email because I don't want to be sent men's shirts, because I only buy them once a year when I'm buying it for a present for my brother in law, and I don't really want to be bombarded by you the rest of the year, because I'm not that generous and only buy for him once a year, right? Like, you know, so will will happily give our data to do those things. Some people will. And so we're actually using it all the time. And we need to get a little bit more confident about not being scared to learn some more and realise how it can help us in our daily jobs. Because it can remove a lot of the manual stuff we do, it can make us a lot more informed about what we're doing. And it also back to the transparency piece can really help us to help people be happier. Because, you know, you look at the equation to sort of get people to be happy. And it's not about them always having their own way. It's about fairness and respect. People are happy to accept something, even if they don't like it, as long as they think it's fair, and as long as they understand it. And I think that's what data can go a long way to helping with, because it can really help for us to be more transparent. For us to show that we are being fair and to explain. The decisions may not have been unfair before, but they were hidden. And so you can actually sort of use data to explain, right? These are decisions we're making. These are the reasons why this is the trajectory you're on. These are the things that you need to do. These are the things that will change things, and all of a sudden, people feel more conten t, because they understand that they can see what's happening. They don't feel that something's being hidden from them. And then they feel more secure. When you feel secure, you are happier at work and you get the best out of people.

Yeah, yeah. I mean, this, the idea of asking the right questions to begin with, I think is really important. And something you brought up earlier in the conversation that I wanted to drill into a little bit more. So thank you for bringing that up again. And, you know, I've seen it countless times being on the receiving end being an agency who is working with clients. And they will provide a very either a very prescriptive brief saying, I want this, this and this type of analysis, which kind of belittles our own ability to decipher what they're trying to get to. And, you know, when you're telling someone exactly what to do and how to do it, it takes away that sense of autonomy, which is something I talk about a lot on the podcast. But then these really wide open vague briefs, where you don't really know what it is that they're trying to get to. And it's like, we just want you to look at all of the data and pull out a story that you see. And I kind of sometimes think it's like, and I didn't get this about, say if you're going for a blood test that they can just analyse it, they can just test for everything. And if you go for a blood test, and they'll pick up any sort of thing that you have, and if that's not how it works, you have to be very specific and say I want this blood test for a specific type of illness. And it's the same when it comes to data, like it's really important to ask the right questions. And what I see out there with a lot of companies when they are starting on this journey, they're like, we just want to have these reports on its app, what's the purpose of the reports? And really, it's not about just creating a suite of reports that you can check in and be like, oh, let's have a look at our app center. Let's have a look at our overall engagement scores. It's about thinking, right? From the information that we already have, what are the big questions that we have. So if there is an issue around diversity and inclusion, and let's take the hot topic of empowering more female leaders into senior positions in organisations, so if you know that that's an issue in your organisation, what kind of initiatives can you run to close that gap for them to help more women to succeed? What kind of things can you do? How can you measure whether or not that is effective? And that's where the data comes in, in my mind, like, that's something I suppose on a personal level that I'm really passionate about is helping women leaders to get to those more senior positions, but how can we actually facilitate that and, you know, this idea of transparency and fairness and respect, I absolutely love that. And I totally buy into that as well, like if you if we can be more transparent about how decisions are made, you know, someone retires or someone leaves the business, and there's a gap in a in a leadership position. How are you making that decision of who, who is going to fill that gap, you know, and being transparent about that, you know, even if, even if it's filled by a man rather than a woman, if you can say how you determined who got opposition, then I think, yeah, it was, you know, I feel like I'm on my soapbox.

Yeah, I love your blood analogy. I think I might steal that from you doing the Why are you asking? Why are you doing it? And we often see people or people being scared to start because they're like, Oh, we don't have the data. We'll hang on. You haven't even worked out what you want to know. Are, we then tell you whether you've got the data or set it up to be collected appropriately? Right, but you sort of people start with the data? And the answer is to start with the business, whether that's about we want to improve diversity in our organisation, whether that's, we want to improve employee engagement. But why do you want to improve employee engagement? What actually you want to do it? Because the current talent market is a nightmare, and you're trying to improve retention? You know, that's probably why you're trying to do it. So actually, your question is about how do we improve retention? And you probably actually have a specific cohort that you're looking at, because, you know, in all organisations, like, there are some people that we don't care if they leave, right, they've done their few years, it's the right stage in their career, we probably don't have the right next step for them. It's, you know, you stay friends, and everyone moves on, or there's people who, frankly, probably aren't suited and weren't the right hire. And actually, it's better that they move on for everybody. And so that it's not that we want to sort of hem in and keep every single employee that we have, but there are key talent that you want to keep their eye on. That will be very expensive to replace. And I think that's it's always about sort of starting with the why, in the same way as the blood test. So why why do I care about this? Am I going to be able to take an action from it? Otherwise, it's a pointless exercise. So, you know, what, if the leadership are not prepared to change the way we do? promotions and performance management? Is there any point in me running this process? Is there any point of data, if I'm asked if I asked all my employees about what they would like their training programs to look like? But I've already bought my training catalog for the next few years? Sort of what's the point, right, if I'm at on impact of interventions, and learning and development, and how that connects with progression, and how that connects with progression of different cohorts, and how that connects to employee engagement scores, and are people more connected to the business after they've had that training, that's really useful. If I'm not then going to do anything with it, and change the training that I buy or who goes on it or anything, there is no point. So I think it's really thinking about why you're doing it. And then being an again, that's a value exchange, being able to be clear with people, Oh, give me this information, it's going to be good for you, because we're going to actually work out whether you as a top talent person that we're putting on our talent leadership program is going to get the training, that's going to get you to the role that the person is just retired in and is going to be vacant. And again, if we can share as much of that as we can, it doesn't need to be hidden, it doesn't need to be a black box, we can share and take people on that journey. That also helps them to really understand right? Well, the impact of that training intervention was great for this cohort of people who were looking to do this, okay, I need that's right for me, or actually, that's not right for me, because I've already got that experience by doing this. So I should be on this pathway. And so I do think the transparency, the transparency really helps. And it builds confidence that organisations are trying to do the right thing, and that they're not collecting this information for no reason.

Yeah, I think you raise a really valuable point around listening. So don't ask the questions if you can't take action on the back of it. And I think a lot of organisations make that mistake where they ask questions, but then they either don't listen to the answer, or they've heard the answer, but they're not actually willing to put into place, you know, to take actually, to actually take action on the back of what it is that their employees have said. And the other thing I find with a lot of organisations rather than internally checking in with their own employees, they look to see what's going on in the market to try and replicate. You know, and I get this all the time from my clients saying, Oh, we're just wondering, you know, what are other people doing in this area? We want to kind of copy it? My answer always is, you need to check in with your own staff, you'd need to be looking at what's going on down the road and what your competitors are doing. You know, of course, you can take ideas from what you can see publicly about what people are doing. But really, what you need to do is check in with your own staff and see what it is that they want what's going to work for them, especially now that we're in this hybrid working situation. I think, you know, some, you know, Elon Musk is mandating people back to the office. Other people are saying that's a terrible mistake. And you know, other people are kind of struggling with bringing people back to the office or other people are totally okay with like, it's okay, if you want to work remotely, some of the time as well. So there's kind of a lot of chopping and changing going on out there from what I can see as well. Absolutely.

I think, on the sort of looking taking ideas from other people. I'm I'm a big fan of sort of learning from others, and we definitely don't all have it right but I agree with you. And again, this is where data can be really useful diagnose your problem. And first, so for example, we, as an organisation very passionate going back to your soapbox about women in leadership. And over the last couple of years, we have gone from sort of 25% to 55% women in leadership. And we've got 46%. Now across the organisation up from 20, late 20s, I think. And so we've, we've done really well on that. And we meet or beat industry benchmarks on sort of lots of diversity criteria or diversity criteria that are published from industry. But we care about also being representative of where we're working. And so if you look at our black Asian minority ethnic stats, and I won't get started on what a terrible way that is of grouping people in a terrible capture of data, but because that's what you can benchmark we do, we do look at that. We've improved, and we are above the industry, and we're above the UK, but we're based in London. So we need to do more on that. And particularly in our technical teams, we haven't got enough young black men, and we're now part of this GLA program will to share learning across different organisations to be able to tackle that problem. But if we just went into generic diversity stuff, we could be tackling the problem of like, you know, I go to other forums where people have got 10% of women in leadership in our industry, because our industry is rubbish at it. Like, I'm trying to tackle a problem I don't currently have when I have got problems and other areas that I need to sort of make progress in. And so I think that's why using the data and understanding your data and your trends, and where things are out how progression is moving, then you can say, right, so the actual problems I need to solve with these, these are the highest priority of those based on the business strategy based on the time, effort, reward, etc. And then you then you go, and you get your ideas from other people. And then you can test them out. Rather than sort of rather than

Blindly looking at whatever everyone else is doing.

Yeah, it's got to be right for your context. And data, data ethics is a big area in sort of looking using data in HR and something I'm very passionate about. And that like we've created this good data guide, which helps guide organisations through that look at the KPIs they can be measuring to see whether they're progressing on it. But we do, we're not giving them the Holy Grail of how to do it. We're giving them the guidance and the questions to ask themselves because in different contexts, and you've got to take your own values as an organisation, you've got to take your own context, your environment, your employees into account, to make those decisions, because the same data model can be used in different contexts and be fine or disastrously bad. So, you know, we built a data model to predict which car someone's going to buy next, right? Most people, most customers are probably comfortable, that you're not going to hassle me when I'm not in the market, you're only going to ask me when I am. So you've used my historic data and some other information to predict that fine, you suddenly apply that to gambling. And we're now predicting, it's the same data model, but you're predicting who's going to gamble next. And then the gambling institution is going to contact them and encourage them to gamble. That's obviously, nobody would think that's okay. And so yeah, it's not really about the data models. It's about how you use them. And I think the that's why, again, having HR people educated on this, because they can really start to play that organisational context, they can start to support on assessing data ethics, because technology does move faster than the law. So no one's going to give us the holy grail of exactly how we do this and how we do it, right. We've got to make judgment calls. And I think that's, you know, there's a huge role for HR to play in this. But it really starts with them educating themselves, then getting confident about using data in their own day to day roles, to then see how that can work across the organisation. The other thing you mentioned that I really wanted to pick up on was your stuff on the hybrid working in this sort of new world of work, and how do you manage it. And we're data geeks, right? So in lockdown, I got really worried because we're quite an informal flat structure. We don't have hierarchy for you no need, but we don't operate on hierarchy. And we're not very siloed. And then suddenly, lockdown comes and really you're talking to your direct reports or your team. And I got really worried that we've sort of become more hierarchical, we've become more siloed. So being data geeks, I sort of went to my data team, and I need you to solve this problem. And they built what we call the coffee roulette algorithm, which basically matched people randomly based. So we sort of optimise say, You didn't match with your direct report. So your team, our board joined in, so they really got to know more junior staff, which was great. And it was literally just informal coffee for 20 minutes. We started every week. We sort of made it every two weeks. Can we decide that was too frequent? And then we did some group ones as well. sort of putting different groups of people together. And like 97% of employees said that helped them feel really strongly engaged in lockdown. 87% said it helped them speak to a colleague that they don't normally have something to do with. And the mandate was absolutely as as informal chats like no agenda, not about work. But 53% said, as a result of learning about people, they wouldn't always talk to their ability to do their jobs improved. And 61% learned more about what their colleagues job entailed. And it's, you know, that's a really simple data thing. It took us no time to build it was plugged into Outlook. So it automatically spotted when you had the gaps in your calendar and popped it in. And, you know, and it's it could work for, if you've brought different brands together, for example, if you've had acquisitions, and you're trying to bring company together to be one or you want your sales teams from two different sort of brands to connect, so that they're not sort of cannibalising each other's market, like, you know, that can work really well. And it doesn't matter, then if people are in the office, or they're remote, because they can connect online. And it's something really simple. But I think it just shows that you don't have to have this sort of huge data transformation of HR, to just use data to do something really small to support employee engagement sort of support the new hybrid way we're working, and to hopefully up your happiness at work, right? Well, there's

a couple of things there, Natalie, they'd love to just kind of to illustrate back to you maybe if I can, is the idea that I think with hybrid working the danger, and with remote working, especially the danger is that people will lose the visibility if they're not in the office, especially with those senior leaders. So it's great to have that option to connect with people at board level, you know, with with someone who's maybe not in the office, or someone who's more junior. And the other thing that you mentioned as well is that you weren't doing it every week. And you realise, actually, that's a little bit too frequent. So you're kind of watching over what's going on. And then you're listening back to feedback and saying, Okay, we need to change this. And I think that's something maybe that organisations struggle with as well, that they're not listening for that feedback, or they're doing something and saying, we have to do it at this frequency, when actually you just need to modify, you need to be agile and change it as it goes on and listen to what people are actually saying.

And I think there's also lessons from sort of other ways data has been used to help you with that, right? So if you look at product people, they will have customer success functions, how a customer success person does their job, is to look at the data of your usage of their product, where you're spending time where you're not, is there a dashboard you never use? Is there a sort of widget you're drilling into? Are people always like logging out after a certain point? Does that mean there's a problem? And they sort of use that data and analyse it to then be able to suggest, oh, look, actually your finance team never use it? I think we might maybe you might need to do some more training with them. Which is training package or, you know, actually, we don't think you know, you know, you said that this was really important, nobody uses it. So let's not develop that. Let's develop this one instead. Because this is the one everyone goes to. And you can do the same with your sort of your HR stuff. So you've put these interventions in. And actually, there's probably loads of ways it's really easy to track them and capture that data without even having to hassle people to ask, because particularly now we're working online a lot. Actually, there's a digital footprint of what we're doing right. So we can use to do things in a way that we couldn't before you couldn't capture it. And that helps with inclusivity. So, for example, we can you can analyse, say, Microsoft team's behavior and see who's in conversations and not because you're like super nosy. But because you can then see which of the meetings that key decisions are being made in and actually who's attending those meetings. And then who's sort of active in different networks in chats? And because the risk with being remote is actually inclusivity is getting worse, not better. And, you know, however diverse workforce you have, it doesn't matter if they're not in the room when you're making decisions in the virtual room, I should say. And so actually data can again, help you to sort of track a measure of inclusivity. By actually, are we including these people in the meetings? Are we included in the decisions? And do we have enough progress? are? Are certain networks being formed in the company and others not? And how do we like create the right nudges to change that so that maybe our new employees who would never have never worked in the office have actually got a lot less connections in the ones who have or maybe we've got a hybrid workforce. And again, we see the ones that are remote don't have the same connections or the ones that are might have better connections. So how do we then listen to that and respond to that run a focus group to agree some things to test and test things out? And it's not sort of about employee surveillance, but it's very, I mean, I wish I knew somebody who they basically realised that someone they didn't hadn't realised that this person was still working for them. They were still working for them six months later. Let's see, I said, Well, I mean, not great, it was really worth it. But like they had so little connection with anybody in the business. Like, it just wasn't happening. And obviously, they weren't getting their money's worth out of that employee because no one was connecting with them. But they were still paying and even realise, that's a really extreme example, but it's not uncommon. And so, you know, but it's also important, because you can use some of these tools to also check in people are taking the breaks that they need to.

Exactly, yeah, yeah, it's the opposite. It's not checking in on how much people are doing and what keystrokes they're taking, but but rather the opposite. And I'd love to know, Natalie, before we wrap things up, do you have any solid examples that you can share without naming specific organisations of implementing, like you're using data to make better decisions or to affect transformation in organisations?

Yeah, so we did some great work, which we really enjoyed with a financial services institution last summer, for example, who were really, really keen to look at the diversity. And so that was about analysing progression of different diverse cohorts. And what was really interesting from that was, they actually realised that despite and I mean, they put so much effort into initiatives, but actually, their picture was getting worse on women. And we were able to identify exactly where first of all that actually, there weren't enough women going into the hopper to change things, but also where they were losing them and what to. And we also identified that when women stayed, they were their most valuable employees. So it creates also the financial case to have, you know, even if the board don't care, there's a financial argument for it, which is supports the business case to invest in initiatives. But the biggest thing that women were leaving for was education. And so actually, what they were able to then do is put in place a program that enabled you to study alongside your job and made allowances for that and supported you to do that, which could quite easily tackle the biggest reason they were losing women. So it's a really small sort of snippet of an example. But just goes to show that it can lead to really, you know, really good understanding direct action, and then hopefully start to reverse those trends, which of course, they need to keep monitoring over time. It's not the only thing they're implementing, but a really clear example. So I mean, that's, that's one good example. Another really interesting example is about sort of where you overlay datasets together from different teams. And this is where one of our clients overlaid their manufacturing data with their HR data, and suddenly saw the big the reason that they were having to do rework in their manufacturing pipeline because of quality, which obviously costs them money not doing it once having to redo it was. And that was only picked up because it was overlaid with the HR data sets. And then they were able to say is that any say, well, are people unhappy? Is that why they're absent, but then you're able to say, Okay, so one, we can start to predict when absence is going to happen. So then we can make sure we've got temporary employees and etc, to make sure that the production line runs. But more importantly, let's start to look at the underlying causes for the absence. So is it because the world cups on and no one shows up? Because they're sitting at home watching TV? Is it because there's problems on the trains? And so people give up and go, Hey, or is it because it's people who aren't moving stations? And so therefore a border? It's because people are moving stations and therefore a bit lost? Or the person who did their induction is a common denominator? Actually, do we need to look at how that person delivers induction or their manager is a common denominator? And do we need to support them with some more training people and development training, we do a little bit more digging into how those relations are working. So that's another really good example of actually just a simple overlay of datasets, spotted how they could A, make sure that they saved money, more money, and got things off on time. But B, were able to then sort of dig into the root causes and tackle the real people related issues, which would lead lead no doubt to a happier workforce, because absence is also a sign of potential churn risk. So then it also then leads to retention. So yeah, just a couple of very different examples, but it's a bit of a flavor.

Yeah, really, really, really insightful. Thank you for sharing those. Now, the question I asked everyone who comes on the podcast, Natalie, what does being happier at work mean to you?

So for me, I just think we spend so much of our time at work, and you've got to enjoy what you do. And of course, there's bits of all of our jobs that are frustrating, irritating, boring, but they should be the minority and you should get joy from what you do. And they that looks different to different people. Different people thrive in different environments. But for me, you got to get up most mornings and sort of want to go to work and sort of my test? I think when someone said to me, what would you do if you won the lottery? I said, I'd buy the business and grow it not, I'd give up work. And I think, for me, that was a sign that I am in the right job, and I am happy at work. And, you know, we work very hard to try and make our team happy at work as well and create the right balance. But, you know, being happy for everyone means diferent things. So it's about finding the right fit for you. And, and that's why, you know, interviewing is so important. It's a two way process. Because it's not, you know, the same job, the same culture, the same circumstances are not the right thing for everybody. And that doesn't mean success or failure. That's just about understanding yourself. Understanding what works for you understanding where you get your energy. And what takes away your energy and finding the fit with an employer, where actually most of what you do gives you energy doesn't take it away.

Yeah, yeah. 100%. Yeah, you are speaking my language now. And if people want to connect with you, if they want to find out more about what you do, what's the best way that they can do that?

So I have a really horrible surname. But the good thing about that is there are not many nasally cramps, so you can find me really easily on LinkedIn, and also our website, which is www.profusion.com. But please do reach out to me. Also, by the time this has launched, we will have the research which is about what employees are demanding. So I'd be really happy to share that with anybody, they can reach out to me. And thank you.

So we can put a link to that in the show notes as well. So if you want to check out the show notes, we can share that directly there. Yeah, brilliant. Great. Thank you so much. I thoroughly enjoyed this conversation, right up my alley, talking all things data, and how we can use it to create happier working environments. So I really, really appreciate your time today. Thank you so much.

I love chatting to you. Thank you for having me.

There you have it, that was Natalie cramp from Profusion, talking all things, how to solve problems, using data, using data to make better decisions and have better transparency at work, which I think is really, really important. And I can't wait to dive into that research that Profusion has carried out in relation to this and what employees are actually screaming out for. So if you want to get involved in the conversation, I would love to connect over on social media, you can do that through LinkedIn, LinkedIn, Aoife O'Brien, that's A O I F E O apostrophe, B R I E N or connect through the website, happieratwork.ie or Instagram, which is also happieratwork.ie. Now we started with, you know, the fact that employees actually want more transparency in how decisions are made. And it's not about automating everything and, you know, letting machines take over. And I'm kind of having visions now of Terminator back in, you know, from 1984, or whenever that was, and but it's not about that. And, again, when we're talking about things like the characteristics of top talent that can bring some bias into things. So it's, again, it's, it's using the human intelligence, combined with the data, and how do we get started with that. So first of all, it's about education. So it's supporting HR professionals, to get that education to teach people how to use data and having a partnership between HR and data, to drive transformation in organisations. So really, it's about understanding how to use data to, you know, data literacy and training people what that actually is. The second piece on is to talk to marketing, really, it's about this, you know, connecting with other parts of the organisation who maybe have the skills to do what it is that you're looking to do. And one of the really important things that came through and this is something I say to clients all the time is to start with a question. So really, it's it's so important to start with, well, what is the question that you're trying to answer here? And do you have the data to be able to answer that question, or do you need to gather it in some way? And one of the things that Natalie mentioned as well is this idea of applying normal business logic. So you know, it's not, don't be afraid of data, don't be afraid of the terminology. It's what you do on a day to day basis is applying normal business logic. And it's not different to when it comes to looking at and analysing data. Sometimes when we have data, they're not always creating the value that we need them to have in the business. And we touched on this idea of having very vague briefs and I definitely in my history of market research have come across that multiple times. I did touch on some of the examples there for sometimes the brief is way too vague, and you're trying to get absolutely everything that you want just by looking at a whole heap of data, which is of no real benefit to anyone. I use the analogy of the blood test. So you have to be really specific about what it is that you're trying to get to. The other thing that we used to say back in the day when I was working in market research was rubbish in rubbish out when it comes to the data, or we probably use less, they're less pleasing terms than rubbish. But basically, when it's it's about the data that you have there already, and how good that data is, as well. It's not something we necessarily touched on during the podcast. But something worth bringing up that it's important to have good, solid data that actually mean something as well that it's not, you know, that there's not loads of holes in it that there is, and that it's actually meaningful, that it's that honest or authentic, that people are being their true selves. Another thing that we spoke about is the link between transparency and happiness. So the importance of people having that transparency, because it builds a sense of fairness and respect. So it's not about, you know, being upset that you didn't get it. But understanding the reasons behind the decision and knowing that it was done from a fair perspective, and having that transparency, I think is really, really important. Now, I did mention this already, but it is so important to start with a question. So what is the question? What's the burning issue that you have right now? And and if you're getting started on this journey, you should have a sense of what that underlying question is like, what are the big issues? At the moment that you want to answer? Maybe there's multiple that maybe you need to prioritise what are the ones that you really wants to focus on. So one of the things that we talked about as well is this idea of retaining, retaining the right talent, so it's not about you just want to retain everyone, it's understanding who are the most expensive to replace. So if you lose people, who are the most expensive ones that you replace, or if there's some people who are underperforming, and it's not that important that you lose them, or it's not that impactful, let's say if you lose them, then they're maybe not that expensive to replace, if they're not adding all that much value. Now, one of the important things as well is to start with why so like thinking about what action, you can actually take on the back of the questions that you're going to ask or the data that you're going to look at, we touched on the idea of listening as well, and the importance of listening to your own staff as a first port of call rather than looking to what other organisations are doing. But once you know the problem that needs to be solved, you can get those examples, or you can get those ideas from elsewhere as well. But the it's really important to first listen to your staff. Now, I do hav e some examples to share from my own experience of working, you know, I stuck my hand up, I volunteered for the peoplhe analytics team in my last corporate role, which exposed me to, you know, to have Riri great connections with global team of people doing stuff. But some of the mistakes that we made in terms of like not comparing apples with apples, because there were lots of changes going on in the organisation globally, there was a restructure, we changed how we did the rating system. And so what we were looking to do was to measure the effectiveness of a leadership development program, which I was part of. So it was interesting to see it from a couple of different perspectives from the perspective of I'm taking part in this program. And I can see now what they're looking at to determine whether or not this program was successful. But also from the perspective of a data perspective. We were not like I say, not comparing with apples with apples because of so many changes and the challenges then dashed from a data perspective that can be encountered by organisations, because of just business changes that happen. So it's really interesting, I think it's important to know that you need to compare like with like, rather than if there are any changes, it's really hard to, to run comparisons over different time periods or measure changes are the impact if there are other structural changes happening in the organisation as well. We talked about taking people on that journey and showing them how decisions are made and showing them how you're listening, and how you're implementing change as a result of that really build confidence in the organisation as well. We talked about the importance of having the right people in the room where decisions are being made. I can't reiterate that enough. It's so important. And one example that springs to mind is the Apple when they were designing their health function on their iPhone, which I use my iPhone, I love it. They didn't have a woman in the room and they didn't think to include period tracking in their health app. And, you know, as someone who talks to periods on a monthly basis, this is something that came as a bit of shock, and not something I talk about often on the podcast, but 50% of the population is experiencing this and suddenly you're kind of cutting out or annoying. 50% of people who are potentially going to use or buy your product. And so the importance of having the right people in the room. Now, the last thing I'll leave you with is having that financial argument. So being able to use data to prove the value that HR brings to an organisation, whether that is looking at retention figures or leadership development, or looking to take on additional staff based on the workload that you have, and the value that you bring to the organisation is really, really important as well. And I will leave it there for today. As I mentioned, please do get involved in the conversation. I would love to know what you thought of today's podcast. That was another episode of the Happier at Work podcast. I'm so glad you tuned in today. If you enjoyed today's podcast, I would love to get your thoughts - head on over to social media to get involved in the conversation. If you enjoy the podcast, I would love if you could rate, review it or share it with a friend. If you want to know more about what I do or how I could help your business head on over to happieratwork.ie.