"
Greg Arthur (00:01)
Hi, welcome to the Product Design for Learning podcast. We've got Beau with us and Beau's topic today is data or as it should be called data, data, data. So good, we named it three times. Beau, could you tell us a little bit about yourself, why you're here and why you chose data times three?
Bo (00:23)
Yes, hi Greg, it's nice to be here so thank you for inviting me. I'm Bo, I'm currently impact lead at a company called La Paia. We run design and run programs for our clients, mainly soft skill programs and we actually strategize with them across their entire portfolio.
And yeah, my background is a bit varied. started off, well, I studied chemical engineering, started off as an operational improvement consultant. So targeting performance in different types of companies, worked at a scale up, HelloFresh here in Amsterdam, and then rolled into the learning, the world of learning. And actually, when I was a consultant, we were driving performance. But I always insisted that we do some training as part of our program.
That's where I really learned the value of bringing people along during training and how powerful that can be. And now I'm on the other side. I'm in a training company and I'm in the learning and development world and driving for the fact that performance is so important and that it's important to performance, track data to know whether we're actually having an effect. that's, I've kind of come full circle and absolutely loving the L &D space.
Greg Arthur (01:41)
Nice, that's a varied background and varied route to get to where you are. I imagine data probably played a part even if it wasn't obvious at every single sort of stop that you made before you got here. if you had to summarize data as a point in a process, when we're talking about the product design process, it's kind of the second one in. So we've thought about the problem, we've kind of got an idea of what's making that up.
still not thinking about a solution at this point, or we shouldn't be, how would you summarise the data section and also why it's important to you?
Bo (02:21)
Good question. it's, let's see, the data is the investigation, the questions that you have.
Greg Arthur (02:29)
Mm-hmm.
Bo (02:30)
It's also the critical thinking about your reason for doing what you're doing. It's joining up with stakeholders. It's making things concrete. It's gathering evidence and it's distilling insights. Those are all the things that I think data can be, which actually from such a small word, there's a lot of concepts that I think are important at this phase.
Greg Arthur (02:50)
Yeah.
Bo (02:56)
And I think it's absolutely critical. So for me, I would not start a project if I didn't have a solid basis to start from. So I think data means like the reason why it's so important is that you will actually find out whether what you're doing, it's the basis to find out whether what you're doing is actually working, whether you're achieving your goals and whether you can, how you can share it with others. And usually that's important because you have to get funding for your projects by sharing it with others.
Greg Arthur (03:05)
Hmm.
Mm-hmm.
Bo (03:26)
you need to get your stakeholders on board and that's also completely crucial for what you're doing.
Greg Arthur (03:32)
Absolutely, and I completely agree with that. I think it's definitely something that as a as an industry and as a function we talk about data all the time it seemed to be like maybe Maybe AI has taken over at the moment as the main topic But it seemed to be data and and then before that was storytelling seemed to be at any conference Learning conference for like maybe three or four years. I was like our data is the thing that we've got to go after Prior to that was storytelling now. It seems to be AI is the thing that everyone's going on about
So my glasses keep steaming up, so it's super hot today, weirdly for October. But it's definitely something that whilst we talk about it a lot, I don't feel like, and I could be wrong, and I'd love to be proven wrong with data, it feels like as an LND function, we don't do enough of it. don't talk, well, we talk about it a lot, but we don't do it in the right way as in.
data analysis, data capture, how we find out if we're going down the right path or not, even in terms of data collection. When you're working on a project, how do you even know where to begin when it comes to that kind of data phase?
Bo (04:42)
Yeah. And reflecting just on what you said, I think what I've noticed coming in from a bit of a technical background and, having a lot of data, I notice a little bit of underlying fear almost towards data. and, and, and also traditionally it was never part of the LND function. So a little bit of like, but that's not us or you know, it's too difficult. So there are a few reasons I think why it's, it's like this. and then.
Greg Arthur (04:56)
Mm-hmm.
Bo (05:12)
to your question where to begin. was your question, right? So how do we get started with data if we haven't before and if it's never been part of our function or it's never been something we thought about and we're like, yes, but now I'm really going to go for it.
Where I begin for, I cannot begin unless I have a really good, bigger picture of where we're going. And this bigger picture I describe in three levels. There's the top level. It's the goal. Why are we doing this? And for, work with organizational clients and that's like an organizational strategy or a department strategy or a team strategy. Like a reason, a goal, the bigger picture, the bigger why of why we're doing this. That's the top level.
Greg Arthur (05:39)
Mm-hmm.
Mm-hmm.
Bo (06:01)
The middle level is usually to do with the target audience. So who are the target audience? What is their situation? Why are they not achieving that goal? Because that goal is also what's their link to that goal and why are they maybe not achieving it? And then somehow that they're also behavior change starts creeping in. Like there's usually a behavior that needs to be changed or something that a skill that they need to learn because they need to do something differently. So what is the change on an individual level that you need that you
Greg Arthur (06:15)
Mm-hmm.
Bo (06:30)
want to achieve. So that's the middle level. And then the bottom level is the program. You've probably thought about a program. There's a learning, there are learning elements involved. I also like thinking about non-traditional programs. So it doesn't always need to be a program can be different initiatives that you're doing, but what, that's what you're going to do about it. So that's that picture. And I like to have it in one slide, because then it's, and very summarized. just what is that? That, and that's where you can start. And I think from there,
the data will flow.
Greg Arthur (07:02)
Yeah, absolutely. And I think there's a couple of things in there. So we use a product canvas throughout our process where it will change. I guess it's project to project, but it will change fairly rapidly in the beginning whilst we're kind of saying we've only half filled in some of this because we don't know the answers and then more towards the back end of the process. It's probably not changing that much. We should pretty much know what we're doing and why we're doing it. But in the first half,
Bo (07:27)
Mm-hmm.
Greg Arthur (07:31)
You could look at it week to week and it could be like, yeah, and it can almost look like two different projects. You're like, no, same one. But then when it comes to data and you talked about having the shared goal or the main goal, it's trying to make sure that is locked in in your definition phase or your understanding phase. When you get to data, as in second part, you're talking about data.
Bo (07:33)
second pencil. Yeah. Yeah.
Greg Arthur (08:02)
That's the bit where I wonder how people start. So when you were talking about you wanted to map these things out, when you were talking about people and skills and what's happening for them right now, that's the bit where I think people struggle. So having that goal is usually like, yep, someone's told me that 80 % of people don't do this and we needed it to be 90%. And that feels like a data point for them. And they've got that kind of like end state goal, which is pretty handy.
Bo (08:17)
Mm-hmm.
Greg Arthur (08:30)
When we say to them, how are going to do that? And how do you know what's happening to these people day to day? That's the bit where I think it's okay to come into the data phase without any data because this is where you're to go and get it. So how do you even know where to begin to say, let's find that to your point, you were saying, let's find out what's happening for these people. How do you, how do you know where to begin? Cause it could be loads of people. Yeah.
Bo (08:56)
Go there.
Go there. I think it's as simple as that. And that's actually going back to my performance consultant days. We used to spend time with the people that were doing the things to be done. we were having clipboards and making notes, but I even spent time in a hospital, in an operating theater, in a hospital. I watched the surgery on a diabetic toe. I would not recommend it to anyone.
I was not looking at the operation so much, but I was noticing everything that was going on around. And some things that we noticed were the trouble of communicating between the operating teams. Because there's people in all different parts of the hospital that need to come together at the right time, at the right place. The patient needs to be anesthetized. There needs to be all these things happening at the right time. But you would hear, of course, all these kind of complaints like,
Greg Arthur (09:41)
Mm-hmm.
Mm.
Bo (09:58)
One party would complain that the other wasn't on time and the other would complain that the other wasn't on time. But unless you were really there, you didn't know what was actually happening. So I think that's really the place to start is to be there to talk to people, to shadow them, to understand what's going on. And clearly there are now tools, there are tools in place that are tracking this. There's data being input. So once you understand what's going on, you also start to...
Greg Arthur (10:02)
Thank
Bo (10:28)
understand what systems they're using, how the data is logged, how good the data perhaps is, which data there is. think, but I think just looking at the data is not always, yeah, can be a bit dangerous because you actually need to go what's going on. Yeah.
Greg Arthur (10:39)
Yeah, absolutely. And I think you made a really good point there about going there and observing people. again, a common misconception I've seen from people is, we've got some numbers. I'm like, okay, I've got some numbers as well. But if you don't have any context for those numbers, they're kind of, either guessing or they're almost irrelevant. So if you can say this person was late three days a week,
you're almost getting a bit of quantitative and qualitative data. Then if you start to find out why they're late, what's happening for them on those days, you start to build out a bigger story and a bigger picture. And that helps you with your analysis to be able to say, we're putting some meaning behind these data, meaning behind these numbers. And we can start to figure out all the way back to your original point, what's happening for these people, what's stopping them doing this thing. And I think I can have seen people maybe getting too hung up on numbers or
percentages or really kind of vague statements of these people really liked it these people didn't and it's like is that it is that the only options were allowed you either do or don't like so I think I'm trying to trying to cut through that is definitely useful I think definitely turning up and and just watching people and spending time with them is definitely going to help you and when we're talking about just data in like gathering data how do you know how do you know you've got enough
how much is too much data to get.
Bo (12:12)
didn't know you have enough. So when you understand what's going on...
I that's, that's the one, you know, have enough, I think with, and then it depends which type of data. So there's some data sources where you'll get lots and lots of data and it's cheap to capture or it's already being captured. And then it's okay that it's a huge amount. You need to understand what it means and what it, what it can tell you and how you can interrogate it. but there's other sources of data. So qualitative data, for example, interviews that you're running or a day spent shadowing those
Greg Arthur (12:36)
Hmm.
Bo (12:49)
super expensive, right? They're taking up a lot of your time. doing that, doing 10 days of that is too much probably for your project. Well, it depends on the size of the project. Again, maybe it's a three-year long program and so maybe 10 days is actually the perfect amount. So it's looking at the balance in total investment. So looking at what is your total budget days, also money, resources that you have and
Greg Arthur (12:52)
Hmm.
Yeah. Yeah.
Bo (13:19)
Deciding this is how long how much I want to spend on it and to understand and how well do you understand it already if you worked in that environment you probably already understand it quite well, so perhaps you don't need as much data or you don't need as many interviews and but you need you want to be able to To have enough that you can
Greg Arthur (13:23)
Hmm.
Bo (13:43)
understand what's going on. And I think it's also important to concisely share what's going on and have the data to back that up. So I think a combination of quantum qual is always good for that. enough that you feel comfortable to say, okay, this is situation and this is why I think that. And then it goes a bit into your gut instinct of what is enough data. when you feel almost, do you feel comfortable presenting to your CEO why they should invest
Greg Arthur (13:49)
Yes.
Yeah? Yeah?
Bo (14:13)
this. If you feel comfortable with that amount of data, that amount of insights, then it's enough. And maybe you'll learn that was not enough because maybe you end up presenting and you and then he's like, that's, that's, you know, I don't feel like that's strong enough, yeah, but then that's well how you'll learn. but I do think you can start building up that gut instinct about what is enough for you to make a decision on that and for others to make a decision on that.
Greg Arthur (14:16)
Yeah.
Yeah.
Yeah, absolutely. And I think there's definitely, there's a point in there about how do you know you've got enough is, so we run a hypothesis section within our data right at the end actually. So once we've gathered what we know already, so like we're just kind of just collecting more to validate that. We'll make a little plan around what don't we know the answers to yet. So where are they? Why are they there? What's happening for them? How do they feel? How do they think?
We'll go and grab that, do a bit of analysis. We'll talk about analysis in a moment. then we'll do like a hypothesis, say we thought this. So now that we've got all of the data, before we go and present to our fictitious CEO for lots of money, does that hypothesis stand up? And it seems to be a bit of a, I was gonna say 50-50, that's probably wrong. It's kind of.
More often than not you're kind of on the right track if you've been following the data but then there are things where you kind of go, unless we had this bit of data, this has blown the whole picture wide open and we didn't know anything before. So you have to almost like kind of retract your original thinking. But I do like that kind of gut reaction of would I present this to somebody who's quite senior to say you can have some investment to move this on? How confident are you in what you've done?
But then, I guess for me it goes back to...
Is is I've got enough is everyone's gut feeling the same basically, so how do you how do you almost use data to cut through emotion? If that's if that's a valid question Yeah
Bo (16:19)
Mm-hmm, because then it's otherwise it's back to gut feeling again, right? And then you're like what? But maybe I mean if you have a team working together then that's even more valuable because one person will
gut feeling will require more data and other less. And perhaps by having that discussion, that's a really healthy discussion to have with each other to say, do we have enough? Are we confident? Are we not yet confident? I think maybe it's the best if you can have a team together or use your stakeholders to touch base with on whether that is enough data. Yeah.
Greg Arthur (16:34)
Hmm.
Yeah.
100 % and I think there's definitely a point around trying to go to not disparate groups but like different enough groups they don't have to be like kind of polar opposites but being able to say this is what we found out based on data and you're almost removing everyone's opinion so I've done some projects before there's been points where probably probably slightly further on where I've got to MVP where the data has told me it's this direction
made an MVP, taken it to markets. They've stripped it apart and said, this is terrible, why are you doing this? Which has given me some more data to say, don't do this, do something different. But then I usually find that kind of end part of the data process helps to kind of put everyone's opinion and biases to one side. You have to say, it doesn't matter what you think, what I think, what they think. The data's told us this.
Do we proceed or do we not? Or do we change our direction? And are you basing it on data or gut feeling or a little bit of both or because that person's really senior and that's what they've told us to do. So it's trying to figure out where you... So in my mind, you would always follow the data. But I can see where some people maybe get sidetracked with, I just got this feeling. It could be something else. How do you... Sorry, okay.
Bo (18:18)
Mm.
Yeah, interesting. So I think that feeling, that feeling is important because often it's based on experience as well, especially if you have got senior stakeholders that will not have time to look in detail at data or will not do interviews, but they have a lot of experience, so they have a feeling. So I think...
Greg Arthur (18:26)
Hmm.
Bo (18:42)
get, well, getting them involved early. think that's one thing, understanding what is their gut feeling, what is their pet idea that they would love to try. And making sure that you touch on that in your interviews with your data, because bringing them along, you know, even if it is dis, if it's disproving their gut feeling, then that's a change process that you will need to manage with them. Hopefully they're open for that. They may not be, but it's kind of, I think there are ways to convince, influence them.
Greg Arthur (18:46)
Mm-hmm.
Yeah.
Bo (19:12)
let's say into the direction that the project is going and you will need to spend time on that. But if it's actually confirming something that they've known or felt for a long time or had in the back of their mind, then that's amazing because that's a great way to bring them on board.
Greg Arthur (19:17)
Mm.
Bo (19:29)
But it's about bringing their hypothesis in early and then, and validating it with, or invalidating it with data. and perhaps one other thing I wanted to say about how much is too much, how much is enough for, for interviews. I remember when, we were doing lots of interviews when you start hearing the same things coming up again and again, and you've kind of managed to categorize different personas. And, and then you, the moment you start speaking with someone in the first few sentences, you're like, yeah, that's
this persona and I've heard that before. I think that's also where you're getting a good idea, understanding of what's happening at the moment and you're probably starting to have enough data from that source.
Greg Arthur (20:13)
Yeah, yeah, I think 100 % and I think that leads into like analysis. So when you're doing like, as you're talking about at the moment, like a, me, almost doing like an affinity map where you're starting to group certain bits of information, whether it's personas or information or even starting to look for themes and patterns, that kind of stuff. If you're, if you're kind of affinity map or similar version of that is really sparse or everything's still just kind of scattered all over the wall.
Nothing's in groups probably means you either haven't got enough or you've been asking the wrong questions We've been looking in the wrong places I think if you can turn up with a group of people and we always try and do it in silence for the first The first round of it is usually all the information goes on the wall and then there's no talking because you don't somebody say I'm gonna do this you know why you gonna do that like no no no kind of Encouragement just to see what people naturally do and then
if you can't get to that in a group of three, four people in 10 minutes, you can't start to see patterns emerging already where you either have a little bit of healthy debate, but largely saying this obviously goes over there, then I would then suggest you need to go and get some different or more data. But I think to your point, if you're already spotting things like super, super quick, then yeah, that probably means you've got enough to start to make a decision on what to do.
One of the questions that I've spoken to some people about, especially when we're talking about doing data analysis or data capture, or actually anything in the data process, is people I've seen that say, we've asked some people. You go, great, how many people have you asked? And they say, five. I've asked about five people. We go, okay, I'm sure this program's for about 10,000 people or even 100 people, five doesn't feel that many.
How do you, or sorry not how do you, what kind of advice would you give to somebody that maybe struggles with the data phase as a whole, even if they don't know they're struggling? And just to clarify, if you are that person that that last five people for a hundred person project, you're this person and you should listen to Bo's advice.
Bo (22:30)
Yeah, I mean it's a good start I think let acknowledge that I think it is a good start to have talked to people and Ask them their opinion. I think how to get started. I think there's Yeah, I think asking yourself the question Would I
feel comfortable presenting to my CEO that this is a data-driven approach.
I think that's a good question to ask yourself. If the answer is no, or I never asked myself that question, I don't really know. Well, think one valid way is to ask for help. I think there are probably teams in your organization or there are communities of L &D more and more of these communities that actually have a lot of experience in this. So asking for help and getting help from a peer in another organization
Greg Arthur (23:05)
Mm-hmm.
Bo (23:32)
I think often L &D'ers are working alone in their organization or only in small groups. So I think asking for help and getting some examples of what others have done can be a good approach. I do think start small. think there are a few different concepts here. So get curious. That's one that I often use. So it's more like a mindset. So ask yourself questions.
Greg Arthur (23:36)
Hmm.
Bo (24:02)
questions like, okay, so these five people think this, I wonder if the other 95 think something similar. Start by asking questions, how might I find out more? And I need to present by the end of next week.
Greg Arthur (24:17)
Hmm.
Bo (24:22)
And there are probably limitations to what you can do. And I have in total four hours to work on this. So that's quite constrained. But what might I be able to do in that time to, to find out if those 95 are thinking the same thing? So then you might start thinking and then get your creativity flowing, I think, because you could send a poll out in the company internet or, or on Slack, or you could message another
Greg Arthur (24:29)
Mm-hmm.
Mm-hmm.
Bo (24:52)
people and not have to speak to them but you message them with a poll or a quick question you could there might be a gathering that's happening on Tuesday that you think hey there I can talk to a lot of people at the same time if you do like that personal contact but that I think there are millions of options here but perhaps by thinking by starting small by asking questions and by also knowing your constraints you could actually do a lot
Greg Arthur (25:18)
Mm-hmm.
Yeah, I think so. I think the worst thing you can do is skipping the whole data phase just because you're either scared about it or nervous about it or or if you've had like kind of not not great results before because it's not something you're used to doing or or particularly enjoy doing or particularly good at doing Everyone's got to start at some point. I think I think that's that's incredible advice is just is just do something can ask someone and
that's capturing data. I think the one thing I would add to that is just is try and have a plan but don't feel restricted by your plan. So like trying to think about what kind of things do you want to know? But especially if you're talking to somebody and they go off on a tangent or they start talking about something else that for them is related but it's not on your list, don't stop them and say, no, no, we're talking about...
we're talking about these two things only. Let them talk, take some notes. If they start talking about football or something, then maybe bring them back on track if that's not what you're there to talk about. But otherwise, think, yeah, just getting out there and trying to get used to building that skill of gathering information, because it feels like quant information is probably easier to gather. You can largely do that with surveys and...
Reports and spreadsheets all these kind of things, but I think qualitative is It's almost an art form in itself to be able to say I'm gonna speak to lots of people or they're gonna tell me lots of things how do I make sense of 300 conversations 300 statements or however many it's gonna be I think that's them And I would love to meet someone that says they're perfect at it because I think that person exists But I would love to someone that's really that's really into that
Bo (26:55)
Mm-hmm.
Greg Arthur (27:18)
it's time consuming but it's it's worthwhile.
Bo (27:21)
Yeah, I think that's very, very true.
And I think there are also real experts out there. So that would be good to get their view indeed. I think for me, what works really well is writing a lot of notes just after a conversation like that. So even if it's a short conversation, immediately jotting down what resonated for me or what I remembered from that conversation. Perhaps even emailing it to them and asking them if that's correct or if they have anything to add.
Greg Arthur (27:50)
Yeah.
Bo (27:52)
Yeah. And, and yeah, the picture will start building up, but it, yeah, it is, it is also an art form to, and I like a science actually to, do this properly and to get the themes out of it. Yeah. ChachiPT can help us a lot actually with that as well. So if we record a conversation, we could, yeah, use generative AI to transcribe it and to analyze it.
Greg Arthur (28:06)
Yeah.
Yes.
Absolutely. Absolutely. And I think definitely that it's probably a whole other episode, there's a definite, you can just Google like open and closed questioning. I'm sure people know what that is, but when you're getting into those conversations, knowing when to use those open and closed questions, almost having like a question funnel where you kind of start super open and start to bring it down. That's...
Bo (28:29)
Mm.
Greg Arthur (28:42)
For me personally, I always find that's much more in the planning than the execution. As in, if someone gives me a really good answer at the top, we could just stay open for like 40 minutes. We're going, yeah, great, and tell me more about that, and that sounds really interesting. And I've got absolutely no credible information from them. But we've had a really nice time. So I think being able to plan ahead for those kind of things. But you're right, yeah, generative AI, co-pilot teams, all the things that can basically transcribe your conversation.
Bo (28:47)
Yeah.
Greg Arthur (29:11)
You can start to search for things like keywords, phrases, that kind of things, and then you get back to the pattern finding and grouping of information. Definitely makes a lot more sense.
Bo (29:19)
Yeah. Yeah. It speeds up the analysis so you can, when you've gotten curious, you've got all these questions in your mind and you've got all these interviews you've done. You can actually just ask Chat2PT for the questions you've got and it'll start spitting out answers and, yeah. Yeah. Yeah. It'll, we are.
Greg Arthur (29:26)
Hmm.
That's We're living in the future. I feel like Terminator is going to be real soon, I'm going to keep an eye out. So when we talk about the data phase, and this is very specific to your career rather than what you see others doing, what mistakes have you made in the past, knowingly or unknowingly? What have you learned from?
Bo (29:42)
You
Yeah, so I think, yeah, at the start, I mean, with my kind of technical background.
I think my first, so it was about five years ago that I dived into L &D and I came in a bit with the mentality of this can't be so different than, you know, the work that I've done previously, working in factories, working in hospitals. Let's, you know, let's see what, you know, why this is such a tough nut to crack. And I actually went into it wanting to be too rigorous and wanting to get
it completely right first time. and then I had to pull myself back and say, okay, I actually don't understand enough of what, of what learning is. So I actually enrolled myself on a course of learning about learning on Coursera, which is for free actually. It's, it's fantastic how the brain works and how it's the basics on, how we learn.
Greg Arthur (30:51)
Hmm.
Wow.
Bo (31:04)
and how it causes behavior change. there is, I mean, there is so much more. So I've dug a little bit deeper into that, but it's, it's, it's a science in itself, behavior science. So I gathered a lot of respect for those topics and that, that they are, yeah, that they are actually their own domains. And there's actually a lot that we don't know yet as well. There's a lot, but there's also a lot that we do know and that we should, should be mindful of that. And, and, and that from the
Greg Arthur (31:15)
Hmm.
Bo (31:34)
there realized, okay, it's not an exact science and how do we go from, I guess, diving into all those scientific theories into, okay, we've got now a company that wants to drive behavior change, but...
Greg Arthur (31:48)
Hmm.
Bo (31:49)
Obviously we're never going to get to experiment as if it's a scientific experiment. It's not an exact science. We have a limited budget. have, what are the absolute most important things that we need to take from all that scientific research into a company setting? And again, there are experts on this in this field. There are people like Ina Weinbauer-Heidel, who has done the research, looked into what are the blockers to behavior change and how can you overcome those.
Greg Arthur (32:08)
Hmm.
Mm-hmm.
Bo (32:20)
So it took me a little while to locate all of this and to understand, okay, it's that complicated, but it has also been simplified by people already. And these are the resources that I can now use to simplify this complicated science. And to be honest, it also puts people off if it is too big, if you make it a big scientific experiment or a big...
Greg Arthur (32:45)
Hmm.
Bo (32:46)
It has to be completely proven or it's like that can put really put people off and say okay I'm not even going to get started then so and that was again another mistake that I made making it too big and then that that it wasn't picked up because it was just seen as too as too big and not chunked down into smaller steps or not even
Greg Arthur (32:52)
Yeah.
Bo (33:13)
Yeah, chunk down to the right size for that version, that loop of the product that we were building.
Greg Arthur (33:17)
Yes, yeah and I think that's an incredible point so I think it's worth people doing that and making those mistakes because everyone's version of big could be different in terms of how big their project is or how big it is for them. So like if someone's earlier on in their career it could be someone a bit later in their career could say well that's you know like that's a week for me where someone else could be a month. But also to the the scale of your data collection
This is something again we talk to clients about which is we need to get in and get this as quickly as possible so we capture what's happening right now because that's that we want to capture this moment in time and that moment in time could be a week's period or a month period but no really longer than that so we can move on because we still got the rest of the process that we still haven't decided what we're going to make and how we're going to make it. If we spend too long doing this and arguing amongst ourselves and just collecting lots and lots of things time is moving on.
People are moving on, things are changing, especially now they're changing really, really quickly. So your data can go out of date really quickly if you don't act on it. people that say, I've heard people say, we've got this leadership program created really quickly. It took us six months or 12 months. I'm like, wow. So what you gathered four months ago, five months ago to get this ready, they're probably different people now. They've probably got different problems now. Or the things that you're trying to
solve for, they may have solved themselves in a roundabout way and that's not their concern anymore. So I think trying to move quickly and like you were saying, trying to chunk things down is a huge thing for people but I think people need to realise or self-realise, self-actualisation is the word maybe, something like that, around where's my limits and how quickly can I move through this. Yeah think that's incredible.
Bo (34:58)
Keep the speed.
Greg Arthur (35:16)
Incredible advice for people.
Bo (35:17)
Yeah. Yeah. Yeah. And then the other one that I would say is
asking why, think we often, or I did definitely often got very strong opinions and sort of taking them as, yeah, it's taking them for granted, taking them as the truth, but not asking why enough, why, why, why. That is also one that I've learned the hard way, because once you then get into it and you start collecting the data, that might not be, if you don't know why you then have to go back, it actually saves
Greg Arthur (35:37)
Mm-hmm.
Bo (35:55)
a lot of time to ask why and understand fully in the first place with your stakeholders, with your project team.
Greg Arthur (35:56)
100%.
100%.
I've seen this actually like in a commercial thing like on Amazon like on some products they'll be like you go down to the review section and there might be like 20 reviews and 19 of them are four or five stars and there's one person that's gone one star you know I want to read this you know what happened to this person and then it's all I see this so many times now which blows my mind that people still do it and they put one star they go really love this product you know like
You gave it one star so clearly you didn't. They go, but my delivery driver was really rude to me. And it's like, okay. So like, if you were just taking it on face value, I don't like this from their one star review, but then you read it and go, yeah, it's great. Loved it. But just don't use this delivery company. if you hadn't asked why, if you hadn't gone to look into the review, you wouldn't realise. But I think that's definitely trying to get people to also quantify their...
Bo (36:42)
Yes.
Yeah, exactly.
Greg Arthur (37:03)
or justify their answers as well by asking them why. Someone says, really love this or I really don't like this.
Bo (37:06)
Yeah. Yeah. And then you'll also come across those, yeah, those, what you said, I've asked five people. And then that's also, you know, that is also data, but then you know, okay, that's what it's based on. as opposed to someone confidently saying it and it being based on five people out of a hundred. you need to know why basically. Yeah. It's really important.
Greg Arthur (37:24)
Mm.
Yeah, yeah, 100%. 100%. And just before we go, so what tools or activities would you suggest that we can all try and adopt? What kind of, is it a thing you do? Is it a tool you use? Is it a mindset? You talked about being curious. What other kind of things could we all adopt to be even more comfortable or just genuinely better at this phase, rather than just talking about it? Because we all do that a lot.
Bo (38:00)
Yeah, yeah, so it definitely starts with the mindset, being curious, wondering why, exploring. There's often also not one right way to gather data. There are often different ways and I think being that being curious will allow you to or the mindset that can allow you to experiment and to find your way of doing things.
Greg Arthur (38:12)
Yeah. Yeah.
Bo (38:29)
So exploring and then some activities, which I definitely recommend or think are very healthy for the data phase are first of all, coffees with stakeholders, just understanding your stakeholders really well.
Greg Arthur (38:45)
Hmm.
Bo (38:47)
Yeah. Knowing what's going on with them, knowing why, again, why back to the why, knowing why they're making their decisions, why they are, what their point of view is, where they're hoping to go. And you, yeah, I say coffees with stakeholders because you won't immediate, it's not just one meeting. If you have one meeting with them, you'll have them on a certain day with a certain opinion. You'll understand that, but you won't have built a relationship with them yet. So I think it's about.
Greg Arthur (39:06)
Hmm.
Bo (39:16)
keeping in touch with them, understanding who they are, why they think what they think. And then something else that I've always found useful, always find useful, is once you're kind of getting that data in and having a look at it, analyzing it, summarizing it in...
Like one slide. I talked about that one slide right at the start. Get it back in there. Summarize your insights. Sometimes it gets a bit overwhelming when you're getting a lot of data in and just taking a step back and just thinking, okay, what have I found out? I liked what you mentioned with the stickies on the walls. Like just put all the data there, all your insights there.
Greg Arthur (39:42)
Hmm.
Yeah.
Bo (40:01)
and go through it, structure it, get it in a format where it starts to make sense to you. Yeah. And is it on the business goal level? Is it on the behavior level of the individuals or is it on the program level? think start to, what is it telling you about your program or about your goal that you're trying to achieve?
Greg Arthur (40:09)
Hmm.
Bo (40:27)
I think that's healthy as well. And it's okay, I think, to get a bit lost at some point when you're gathering data or when you're finding out new things and hearing things from, you know, getting some insights from one data set, ones from the other. But I think that those moments of reflection together with your team or together with your stakeholders and...
bring a sense making almost together. So have moments of that because I think if you feel, if you start to feel lost, you might lose a bit of steam. You might lose a bit of confidence. There's a risk that at that point you say, okay, whatever, this didn't work and just move on. But actually I think those are the moments where the things that you have found are very important. They might not be what you thought you would find and making sense of it together with the team.
Greg Arthur (40:53)
Hmm.
Yeah. Yeah.
Hmm.
Bo (41:21)
is really important. And also just sharing, this is tough guys, this is not easy. That's good team bonding.
Greg Arthur (41:27)
Yeah, yeah, no, I agree. don't, I don't, I think if anyone says this is super easy every time then, you know, they're just a, they're a big fat liar. So, yeah, I think, I think being really, really honest with each other in your, in your group and especially if you're working on your own, trying to, trying to have a plan, but again, like we were saying before, not kind of being too rigid to the plan. So you talked about gut feeling and kind of.
trying to almost test yourself like would you share this with anyone and I think it's okay to share it informally as you're going along to say I'm going in this direction does it feel right can I get a sense check can I get other people's gut feeling but but yeah some it sounds a bit vague but I know what you mean some of it the gut feeling thing is definitely there but I think it's also trying to go back to do I feel like I've got enough to make an informed decision because this is going to affect people that are going to work on the project it's going to affect people that are going to get the end product
how confident you feel in your approach because of the data you've gathered and analysed. But yeah, it's a minefield. I think, like you said, the most important thing I think you've said so far is you've just got to start speaking to people. You've just got to start. I think, yeah, that's my takeaway.
Bo (42:50)
Yeah, yeah, definitely. You gotta go understand. Go get in there, roll up your sleeves and just get involved. Yeah.
Greg Arthur (42:58)
Nice, nice. Thank you so much, Beau. I've really enjoyed our chat about data. We usually ask people at this point, is there anything you want to plug or anything you want to give a shout out to or anything you want to say before you go? Where can people find you? Do you want people to find you?
Bo (43:15)
Yeah.
Absolutely, absolutely. love, yeah, I'm myself definitely bit of an impact measurement nerd. So I love having those kinds of conversations. So probably LinkedIn is the easiest way. So Bo Duery on LinkedIn. And yeah, maybe there is one other person, researcher that I'd like to mention because this is Professor Robert Brinkerhoff when I read his book on improving performance through learning.
He's also written some articles and some other books, like Use Case, the Case Study Method. yeah, I've, so if you're working in organizational L &D, I think those are really, really good books to read. For me, it really opened my eyes in how to structure thinking about impact and how then to do all the data stuff after it. But really that, where do you start from? He's got that super clear and how do you drive impactful learning? He even gives examples.
So think, yeah, a lot of my own methodologies within La Paia are based on that, on him, his research. But yeah, no, apart from that, I think I'd just love to have these kind of conversations with people. And definitely, if you're feeling lost, I'd love to help out. So get in touch. I can definitely give some pointers and happy to do so.
Greg Arthur (44:17)
Amazing.
Cool, nice one. Well thank you, Beau. Everyone knows where to go and get you now. I'm sure you're have a massively full LinkedIn inbox. So we're gonna close and then let you trawl through all those messages you're gonna get. But thank you so much for your time and it looks like it's super sunny there as well. So have a lovely weekend and we'll see you soon. Cheers, thanks, Beau. Bye.
Bo (45:02)
Thanks, Greg. You too. See you soon. Bye."