On this week’s show, Matt Ballantine and Julia Bellis meet Sian Basker, co-founder of Data Orchard, to discuss what data maturity actually means for organisations — and what a decade of benchmarking data reveals about who’s doing it well and why.
Sian explains how Data Orchard‘s assessment tool, now used by nearly 20,000 people across 56 countries, measures organisations against seven dimensions — from the practicalities of data collection and tooling through to purpose, skills, culture and leadership. Crucially, the tool is designed to be completed by whole organisations rather than data specialists, on the basis that the people closest to the work are best placed to assess it. The conversation explores some striking findings from Data Orchard’s most recent annual report: that there’s no correlation between organisational size and data maturity; that Scotland’s public sector is outperforming the rest of the UK; and that only around 5% of organisations reach the top “mastering” level. The group also dig into the human side of data: siloes, the gap between what leaders need and what frontline staff experience, and why confident leaders are often the most important ingredient of all.
Show transcript autogenerated by Descript:
Matt: Hello and welcome to episode 345 of WB 40, the bus like podcast. You wait for ages and then all of them come along at the same time. This week with me, Matt Ballantine, Julia Bellis, and Sian Basker
Bus. Like he says, it’s the 200th anniversary of the very first bus. I found that out today. That’s the sort of facts that you don’t get anywhere else other than WB 40. Welcome back to the show, Julia. How has your week been?
Julia: Well, am I allowed to go back as far as International Women’s Day?
Matt: You can,
Julia: yes.
That was quite an exciting thing that I did that I thought I could talk about that’s relevant. It seems to go from strength to strength year by year. And this year I went to two International Women’s Day events, so it’s pleasingly morphed from a day when women have to do loads of extra work to prepare stuff to a day when women get invited to go to really cool, fun things.
And this year I went to an energy auditing lunch in Manchester, which was really interesting and it was the opportunity to really think carefully about what aspects of your work drain your energy and what aspects boost your energy. And then the homework, which I have not yet done, but must, is to go away and seek opportunities to incorporate all of the energy boosting stuff in.
And they can be tiny little opportunities. You know, the difference is cumulative and as I, so I was very grateful actually. I thought that was a genuinely useful and fun way to spend a lunch time.
Matt: That’s very good. I’m also slightly never seeing that. We’re working together quite a lot at the moment that I’m on one of those lists and that’d be quite.
Julia: No interactions with you are always joyful.
Matt: Oh, that’s very nice of you to say. Um, anything else that’s happened in the last week or is that has happened like,
Julia: well, actually, I mean all about me was Mother’s Day at the weekend and so I seized the opportunity to compel my family members to join me on a mystery solving history walk around London.
And it was so much fun actually. We did a, it was called the Holy Grail Walk, and we started in the Knights Templar Church around the inns of court. And we had to follow all of these cl clues and went around Lincoln in fields and just these really cool, beautiful, peaceful back streets of London. And had a marvelous time and ended up in the Chesa cheese Pub, which was good fun.
How about you? What have you been up to?
Matt: Uh, I have been doing relentless plugging of book work because we are getting closer and closer. Apparently the book has been printed. , When I last heard from the publisher it was being cut. They’re using special machines to cut the book, which is very exciting.
, I was at the London Book Fair on Wednesday last week originally to see around it with my publisher, but he was ill, so I was there on my own and spent quite frankly, four hours wondering what on earth you’re supposed to do at the London Book Fair. It’s the busiest trade event I have. I’ve been to in forever, the whole of Olympia.
And it was, I mean, there’s incredible energy there, but I felt like a massive outsider. But I did have one meeting lined up, which was with somebody who is potentially interested in representing us for international translation rights, which is very exciting. And I had a meeting with our publisher’s other half who joined, and she’s actually a, a translator professionally.
So, um, she has a sort of vested interest in that and also obviously with her partner running a small publishing house. So that was very interesting on for, and
Julia: you should enjoy this. This is the cue.
Matt: The Well, yeah, the thing was at Olympia though, because, because basically it’s publishers selling to retailers and it’s publishers selling to rights holders.
So actually authors don’t appear to be really be there other than sort of occasional intellectual candy for the stands. And you can’t buy a book there as well, which is a really bizarre thing. ’cause it’s, I mean, you can buy the rights to books, but you can’t actually go and buy a book. I have a thing.
I know.
Julia: You feel robbed.
Matt: I know. If you’re
Julia: going to the book fair.
Matt: Yeah. Sorry. I No, no, no. And um, so the other thing was on Friday I got introduced to, um, a chap who is a professor at Imperial College, who’s an economist whose specialism is luck, which given the theme of the book about randomness is we had a related, fantastic conversation.
And, um, there might be an opportunity for me to do some guest lectures at Imperial College. It should be quite fun. So that’s been good. There’s been lots of work stuff. And then, uh, we actually had 12 people four of us, and then. Eight other people that we really didn’t know at all come for lunch on Sunday.
As part of my eldest getting ready for the Scout jamboree in 2027, we met the families of the other two kids who are going to the Scout Jamboree in Poland. It’s quite nerve wracking when you’ve got complete strength. It’s coming for, for lunch, but it was all great and they were ev all, everybody’s lovely.
But, um, that was quite an odd way to spend Mother’s Day.
Julia: I think that’s a brilliant comfort zone, expanding activity, you know, created by your children actually.
Matt: Yes, and it was, it was, uh, it was a stretch, but it wasn’t as much of a stretch as Olympia, which was stretched too far possibly. So yeah, keeping in and outta comfort zones is an important thing.
Anyway, so yes, that was, that was the, the sort of week that’s been. Um, Anne welcome back to the show. You were with us some years ago now, I think. Mm. Um,
Sian: yeah.
Matt: But, uh, what you’ve been up to? Well, I mean, if you wanna go through the entire last four years or something, that’s fine. But last week,
Sian: last week, well actually I also was celebrating International Women’s Day last week.
I celebrated it with my choir singing, protest songs on a beach in Pembrokeshire, which was a hoot. And I can’t, it’s quite a kind of, we’re sort of coming out of winter. I mean, I, I still, I, some people would say argue already in Spring, but I have spent quite a lot of time feeling like I’m only just edging out of winter into spring.
And so it’s sort of cooking up a little bit. And we’ve had we’ve had our Data Folk club this week, data Orchards, data Folk Club, where we get lots and lots of people in the nonprofit sector together for a free hour. Once a month. Some of it’s just like every, every third one’s like coffee. Uh, this, this week’s one was on data culture.
Um, we’ve been talking to the Canadians and we’re, uh, we’ve got some exciting All of
Matt: them.
Sian: Yes. The Canadian partners, the ca Canadian Data maturity people. Ah, okay. Yeah. Um, are, uh, they’re flying the flag over there. Uh, so we’re working with them a bit this week. Uh, the other thing that was really significant was, it was pie day.
It was actually pie day on Saturday. Yes. But because because it was a work day. We celebrated on Friday, so Friday people had to make pies. The, the link there. I can see you frowning.
Matt: No, I, I know. This is the, this is the, the lack of adherence to international standards for date formats that you’re going on about here, isn’t it?
Sian: It really is. But we have got some US people on our team, and there are lots of people that in solidarity for the, for what’s going on in their country. We, we are not gonna be too. Precious because we also haven’t got our own date format, plane pie day. I think even if we switched to a different calendar system, I think we’d struggle.
So anyway, yes. For a bit of fun. Can we two extra months? Yeah. Yeah. I made a, I made a frenchie panty pie with a pie sign on it, pie symbol on it. And, um, other people use their own creative approaches as well.
Julia: Amazing. I think
Sian: you win pie day half down. Actually, I didn’t, uh, I didn’t do anything quite as educational or challenging as either of you.
For Mother’s Day, I just allowed my children to cook me delicious brunch and smoothies and kind of, indulge me with s spas and then I watched little women. Nothing wrong with any of that. No. Fantastic.
Matt: Side note to my, to my children who I know occasionally. Now listen to this show, Oscar Milo, if you’re listening, that’s how you do it.
Alright, next year, take notes.
Sian: Chocolate Foot Spa.
Matt: Oh, it was a, it was a, it was a drawn out experience for those two this year, but there we go. Right, we are here to talk data. We are here to talk data maturity. We are here to talk about the state of data maturity. So I think we should probably crack on.
So back in November of 2025, that distant past before 2026 started there was a report that Data Orchard, which is the company that you co-founded, cn. Published, which was one of a, a number that you’ve done now annually, looking at the state of data and the state of data maturity within organizations that you both work with and also who have used the, the data maturity assessment tool that, um, you’ve, you’ve been working on for many years as well.
And you did a good presentation about it, which is why I suggested you do be a guest on the, and then Christmas and New Year and stuff. But we’re gonna sort of talk about that over the course of the show, but I think it’s probably a good starting point for us to just think about what, what data maturity is.
’cause it’s a term that’s banded around a fair bit, either because it’s needed or because it’s absent mostly. But you’ve done quite a lot of work to be able to help, uh, codify it into a series of. I guess dimensions. And so for, for you and for the work that you do with your clients, what, what is it we’re talking about when it comes to whether you are mature or not in the data?
Sian: Well, I suppose, uh, I mean on, on the quick realms of definitions, when we are talking about data, we’re like talking about all of the data an organization collects and users, uh, we have a pretty holistic definition of that. And when we talk about data maturity, we’re talking about an organization’s journey towards improvement and increased capability in using data.
And we’ve, we’ve been researching it really, we started back in 2015 kind of wondering how, how would, how could you measure and compare how well an organization is doing with another organization and what are the important factors that you should consider? And we went and looked at as many. Really different diverse organizations as we could, like big international ones, tiny local ones in transport, housing, like as, as diverse as we could.
And we identified seven. We got it down to like seven key areas. And, um, one of them is quite obvious, like the practicalities, like the data itself and the tools that you’re using to collect it and store it and manage and analyze and report it. Another dimension of it is about purpose. And so, you know, what are you using the data?
Do, are you, you know, how are you using it to drive your, um, your services or, or your outward facing engagement with whatever client sector you’re, you’re working with? How are you using it to strengthen your core capabilities as an organization and your internal capabilities? And then. The, the, the key bit was about people.
So the leadership, the culture, and the skills were the other three themes. And, and actually without people, none of the other stuff works at all. So, um, so those were the kind of the seven key themes that we, uh, defined. You know, we had lots of thinking, you know, in the early days, uh, when we kicked off, it was around the time of GDPR and the New Data Protection Act, and we resisted some people’s suggestion that we should go down that route as a, as a specific key thing.
And instead what we did was we embedded quite a lot around security and governance into those existing themes. So those, you know, probably about 20% of our assessment covers data governance and data security. And it’s embedded in culture and skills and tools and of course the data, but it doesn’t necessarily touch on the purpose aspects of things.
Julia: I bet you I, like many of our listeners right now, I’m doing this mental evaluation of everywhere I’ve worked over the years and trying to tie up where it fits with, uh, what you’re talking about on your framework. And one observation that I can make from my experience is that culture around data, I’ve been working in tech for about 20 years is completely different now to what it was 20 years ago.
Have, have you found that, that there is more respect for being able to handle data responsibly?
Sian: Mm, I think so. I think so. I think it’s be, I think it’s because it’s everybody’s job. It’s so, integral to just about every aspect of, I mean, in our personal lives as well. I mean, you know, we’ve, on all of our apps and everything we do, data’s very much more in our lives and more, more accessible than it ever was.
I mean, it used to be so horrible to, to collect and, you know, extract and get anything interesting or beauti or beautiful out of it. I mean, I’d say data visualization has been, uh, transformational in terms of the appetite and interest that people have in data. ’cause nobody’s gonna read a spreadsheet.
Well, very few people gonna have a spreadsheet.
Matt: There are some people who like that there are, but um, yes, and I said I, I, interesting though, Julie. So the, the last role that I had before I started my current job when I landed at the organization, my observation there, this is back in 2019, my observation there was that data was regarded as simply the stuff that sat in systems.
Speaker 4: Yeah.
Matt: So there was a, there was a an awareness of the fact that data was a thing, but there, there wasn’t anything that was, that it had a life and could be abstracted outside of the systems in which it sat. And as a result of that, there was massive inconsistency in the way in which data was represented from system to system.
And data integration was a complete nightmare because although. Sort of first step had, had been taken in terms of the recognition of what this stuff was about the next level up. They, they’d not made that exist. That step at all didn’t
Julia: really exist. No, this is what I was thinking at the start of my career.
Every idiot, junior developer, of which I was one, had full production access, you know, full sequel ability to drop tables, you name it, everything. Anybody could do all of that because data wasn’t valued in the same way that code was. Um, and now I think data is valued so much more. I mean, obviously it’s the most valuable asset for an organization, isn’t it?
Yeah.
Matt: So, so, um, that’s the, the, the structure in which you’re working. How do you then go about actually. Understanding where an organization sits. ’cause it’s one thing to have a set of measuring tools, but how do you go in and Yeah, actually measure it
Julia: well and make it beautiful and make it beautiful as well?
Sian: Well, we have worked, I can talk about the beautiful, in our latest iteration of our tools, but I mean the, how I think is probably one of the things that was most unique about our tool in that we realized very early on, it’s entirely possible for a group of experts to go into an organization and do an external assessment of them.
But it’s very time consuming. It’s very expensive and you won’t get round to ev you know, certainly in large organizations you’re not gonna get round to talk to everyone about it. And and so what we felt was more useful is actually the people in the organization are, are the best people. They understand the context, they under the understand the work.
They’re the best people to answer the assessment rather than us. I mean, you know, we could do it, but take us a very long time given the millions of businesses and companies around. So we designed it really as a tool for whole organizations to take and complete. I think certainly in in smaller organizations, organizations with less than 250 staff, we get a huge response rate.
Some of them get a hundred percent response rate and they get, and the way that we do it is we do it as a live event. And so we launch it, we introduce what we mean by data, by data and data maturity, and then we get everyone to take the assessment at the same time, drop the link in. They all get their own personal report on how they scored the organization, and all of their data goes into a whole organization report.
The bit that’s more fun is the qualitative discussions that follow immediately after. So the tool itself is about engaging people and educating people, and they get a sense of what good and great looks like. But the, the main thing is it gets ’em thinking about what are the problems and challenges we have in our organization?
What ideas have we got for getting better and that, you know, so we can do it in an hour and a half workshop and get like, you know, a hundred people in the organization as a kickoff to have those conversations and really mix ’em up. You know, we’ve got CEOs talking to, to drivers and trades people. We’ve got like, you know, the head of finance talking to the creatives.
We’ve got, you know, and, and, and that diverse that, just getting that conversation going about how even people see data, what they, how they’re using it in their jobs, what challenges they see. Is a really crucial part and being one of the great successes of our approach is about, and then we get as many other people to take the assessment after that.
So, it’s, it’s a, it’s a really collective everyone job to assess the data maturity.
Matt: That’s really interesting is the sociotechnical thinking stuff that I’ve become a bit obsessed with that was kind of fashionable ish, sixties and seventies, and they’re kind of dropped out of fashion entirely.
But within those ideas with works of people at Albert Churns who was at the Tavistock Institute, I think it was one of the, one of the principles that churns talks about was, I can’t remember the exact language he used, but essentially it was, give data to people who can act upon it. And that within the last 30 years of data warehousing and uh, kind of the ability to be able to retrieve data within organizations, an awful lot of it has been focused on management information, giving information to people who manage, not giving, people who do things, information on which they can act better.
And a lot of that I think then stems from those methods of being able to try to work out what maturity looks like, which is what do you send in a bunch of crack data experts and they go and assess it and they start by talking to the managers and then they have a look at some systems and they give a report and they never actually talk to the people who might actually be able to do something useful with the
Sian: dates.
Well, I think, I mean everybody, I mean I think a key thing is about getting different perspectives. So one of the things that’s very common is the sort of internal benchmarking where you are captur. Like how different teams perhaps see data maturity across an organization. How people at different levels of seniority see data.
For a lot of people who are working frontline actually data’s working quite well for them, is what we’re seeing in a lot of organizations. You know, from an operational point of view, it’s doing fine. Meanwhile, you might have a, a senior team who’s going, it’s just not doing it for me. I’m not getting the value that I need.
And it’s those conversations about different perspectives, uh, within an organization that are the most exciting. And we’ve had, um, like in, in Scotland, we’ve been working with public sector. We’ve just finished our sixth cohort of organizations who are taking the assessment at the same time. And they’ve been quite interesting because they’ve used persona types about like, what kind of persona do you have?
Is it, you know, are you a sort of data collector processor? Are you an analyst? Are you an IT person? Are you a strategy? Are you a policy person? And one of their personas is, I don’t believe I use data in my job. And we’ve gone, wow, I think it’s maximum like 2%, maybe 3% of respondents said that they didn’t think they used data in their job.
And it was quite an early on question in the assessment. And then right at the end of the question, at, so they go through the whole assessment, uh, and right at the end we ask them how much of their job, how much of their time in their job is spent working with data. And those people, on average, say they spend 20% of their time working with data by the end of the assessment.
So
Julia: the. The ones who say they don’t use data at all. Yeah. I
Sian: don’t, I don’t believe
Julia: you use data
Sian: in my job at the end of the assessment,
Julia: then learn through the course of the assessment that actually they do spend a day a week on data. Yeah. Yeah. Wow. That’s fantastic.
Matt: I’m reminded a bit of a survey that I ran in an organization once, which was about collaboration, and I had about 10% of people saying they didn’t collaborate with anybody.
And I thought, why are you, why are you here? Exactly. It was quite terrifying. And then you could see who they were and think, oh no, you might be right. Do, do you find that the, the organizational structures have an impact on the ability for organizations to use data effectively? Do we silo around data in similar ways that we silo around processes or other sorts of interaction?
Definitely
Sian: and I think, in larger organizations well combination of larger organizations and kind of very, what’s the word? It does kind of depend a bit on the context in which organizations work. So organizations that have, do one thing basically and are very systematized about it it’s kind of easier because everybody can get behind that in organizations that have got really, really diverse products and services and operate in lots of different places with lots of different kinds of customers or clients or whatever you want to call it, service users.
It gets really complex and, and, and certainly you do get those kind of functional kind of differences where, and, and it’s sometimes, exacerbated by the tools that they use that have been kind of developed and customized for specific kinds of functions and roles. And so you can end up with very siloed, working both kind of people, cultural, structural, and technological.
Speaker 4: Mm.
Julia: I’ve got, you know, examples of data appearing to tell an impossible story until you dig into it and understand the context. The famous example is the Amy Edmonds son. The teams in hospitals that have the best outcomes, reported the most mistakes. And that is where the data doesn’t seem to match reality until you dig into it and you understand it’s ’cause they’re talking about the mistakes not hiding them.
And, and I’ve, I’ve had many examples in my career of. People trying to manipulate data or, you know, the data is accurate, but it’s somehow you, you, you do need to understand the human interactions and what is going on behind the data before it makes sense.
Sian: Mm. And I that’s, well, I love Amy Sen. We talk about her a lot in our data culture chorus.
Um, and yeah, I, I think that whole the calling it out and the, and the conversation and the confidence of people to be able to ask about data and explore data and understand the nuance of it, the complexity of it, the lack of certainty in it.
Julia: Yes. Or, or the, you know, the certainty is there. The data is unemotional, isn’t it?
But humans have emotions around the data.
Matt: But, but there’s also a, it’s a, a ties to a thing. I’ve been working on some of the book publicity recently, a story about, um, general Eisenhower making the decision to launch the D-Day, uh, landings on the 6th of June in 1944. And they depended on, you know, there’s a whole load of planning behind it all.
150,000 troops, thousands of boats and airplanes, and they needed the tides to be right, which are obviously very predictable. The right level of moonlight so people could see what they were doing, which is predictable to a level, but depends on the third factor, which is the weather. And the weather, even today, down to more than about 48 hours.
Is essentially a chaotic system that you don’t really know and being able to make decisions in the context of the known and the knowable, but also being able to understand the stuff that you can’t know. And funnily, there was a, a, I dunno if you saw the news story last week, I think it was, which was about theme park operators, the, the biggest theme park operator in the UK complaining about the use by the BBC of long range weather forecasts because it directly impacts their bookings.
But if you’ve not really got accurate weather forecasting for more than about two days, that’s a real problem. ’cause if, if next Saturday is currently forecast to rain, I’ll be getting less bookings at the moment if I’m running a theme park, because people are looking at the thing going, there’s no point in going on Saturday, is there ’cause it’s gonna rain.
And so the reliance on data and the impact of data and the way in which it shapes behaviors and now you’ve got this, you know, theme park operator saying, don’t publish that because it’s not good enough. And it’s having a d you know, a devastating impact on our bookings.
Julia: There’s a financial product you can buy, isn’t there a weather bond or something?
And you have some businesses that do well when it rains and others that do badly when it rains. And it’s all about transfers of cash so that you can cover yourself. If you’re a theme park that needs sunshine, then you, you get some cash flow from Oh,
Matt: interesting.
Julia: Yeah, a different, I can’t remember what it’s called, a weather bond or something like that.
Matt: So you have been doing this surveying, um, this assessment tool for, well, some years now. 11 years, 10 years?
Sian: Well, we started the research in 2015, but we launched the tool in 2019. Okay. So that’s what, six
Matt: years? Seven years? Seven. Seven years. Seven years. Time flies. I know. And you now are able to start to be able to gain insight across multiple organizations, and that was the report that you published back in November.
So some headlines, first of all, about what, what was the, the November’s report telling us?
Sian: Well, I suppose the first thing I should say is it’s the first report that we have done where we included the commercial sector. So we had actually had the, originally the tool was designed for the non-profit sector.
And predominantly that excluded initially the public sector, but we quite quickly found that we had public sector users, we had people from commercial organizations using it. And so in 2021, we actually adapted it to make it completely sector agnostic. But because our work is primarily aimed at the nonprofit sector, we only ever really reported on the nonprofit sector.
And then over time we go, actually we’ve got quite a lot of growing data on commercial organizations and we were getting more and more, interest in sales of these, uh, assessments to commercial organizations. So, that was the first time that we aided three sectors, private sector, public sector, and not-for-profit sector.
And it was also the first time we’d done com done global. I mean, I feel a little bit I mean it has been used by people in 56 countries I think now. Most of them are UK so our dominant market by a long way is the uk. So I would feel more confident talking about the uk, but I did notice that other benchmarking data maturity people were claiming global.
And I was thinking, well, I think we’ve got quite a lot of global data actually. So we’ll do a global. Cross sector report. Um, so we just start to compare, you know, how different is it and are there, you know, and I know over a long time it’s, um, it’s been quite interesting to watch the, the trends in different sectors and to kind of get, be able to get, because we’ve got more data.
I think we’ve had now it’s almost 20,000 people who have used the assessment. And we are quite picky about the validation and the, and the benchmarking. So we only include definite registered organizations, at least for the uk. So we do check them off against the company’s house and against charity commission registers.
And yeah, so. Yeah, I think we, we were kind of curious to look a little bit more into what kinds of organizations. We’ve always asked a question, are bigger organizations better? And I think there’s quite a lot of myth that are out there around some views on what kinds of organizations are great data organizations.
Julia: Well, okay. Can I dive in with an assumption and then you can maybe disprove it or dig into it. So when you said that bigger organizations are better, I thought bigger organizations have more access to more data and there is something good about quality, about quantity. Is that true or I completely wrong?
Sian: Well, uh, we have seen no evidence. Ever. It was the first thing we looked at. We just thought, well, bigger organizations are banned to have massive data teams and, and they’ll be really on it. And so their organizations will be brilliant at this stuff. And we have never, and we do it every, every year we do analysis.
We’ve never found any correlation between data maturity and size of the organization. We’ve looked at it by age, you know, is it like old organizations? Is it new organizations? We haven’t found any evidence that size is a factor in data maturity.
Julia: Say size does not matter when it comes to data maturity.
Mm-hmm. Wow. You know, first surprise.
Matt: So is public sector or not-for-profit better than commercial?
Sian: Well. It’s very, they’re very close. So if you, if we, I mean we score them on a five stage journey from like unaware through to, um, emerging learning, developing and mastering. Only about 5% of organizations are at the mastering level.
So it’s like one in 20 organizations are in the mastering stage. The majority are in that learning, early developing stages, and it’s very similar. So on a, on a scale of north to five, we’ve got the commercial sector, global commercial sector, and non-profit, not-for-profit sector are both 2.8 and the public sector’s a bit ahead at three.
But there are nuances within, within sectors and yeah, sub sectors is more, more of an interesting place to look and geographies seems to be an interesting place to look.
Julia: Oh,
Matt: so UK geography, uh, which of which of the areas of the uk, the devolved
Sian: nation,
Matt: Scotland doing
Sian: absolutely brilliant. Scotland’s doing great. The, the devolved nations are doing be doing better overall. Um, the areas which were doing less well, were like east of England, I think it was east of England and the East Midlands were doing less well.
London and the middle.
So when you look at Subsectors, and we’ve seen this across all sectors actually, that there are some types of industry that are more data-driven industries, inherently more data-driven.
If you’re a regulator, if you are in finance, do do really well because it’s inherently a very quantitative insurance, finance, insurance, if you’re an insurer. See it gonna be, um, we see it in the, in the nonprofit sector. We see it a lot in like obviously research organizations. But you’d hope they were really good, wouldn’t you?
Matt: So you’ve got these exemplars of good organizations. You’ve got an ability for people to be able to benchmark. You’ve build up a whole load of data around how they able are, are able to be able to benchmark themselves and um, and and improve. As a result, you’re now starting to be able to offer them services, them being your clients, offer them services where they can actually start to build capability.
So it’s not selling data warehouses that you’re doing more skills and capabilities to help people do it better.
Sian: Yeah, I mean there, there are lots of great organizations that already sell data warehouses, actually. So, I think the world is fine for a lot of that technology thing, and I think a lot of the tech skills are, there’s amazing training and opportunity to learn if you wanna learn coding and engineering and data architecture, all of that plenty of it is really well covered.
What we realized was that there was a real gap for leaders, for CEOs of organizations who have got this mixture of a tremendous opportunity, always been told about the amazing opportunity around data, while still also carrying really alarming risks. You know this so and so as a, as a leader in your portfolio of skills and responsibilities, this whole data thing has crept up over the last, you know, particularly over the last decade.
And, and I think it’s quite scary. It’s quite uncomfortable ter territory for a lot of CEOs. And they’re having to make, when they’re thinking about the future of their organizations, they’re having to think about that, you know, those assets and that opportunity, but also they’re having to think about their workforce and like, what kind of skills and roles and responsibilities do they need?
And many of them just aren’t that familiar with it and are, you know, it’s just like, oh my gosh, what, what do I do? So we’ve been working at like, what’s. And of course lots of, lots of people really hate data as well. Lots of CEOs really hate it, you know, especially if it’s the kind of thing that, you know, they have to keep reporting to regulators and they have to you know, uh, maybe they have to report on their contracts or certainly in the nonprofit sector, there’s lots of reporting to funders and commissioners and so it’s, it’s not all that popular as a, as a topic as well.
So what we’ve been working at is we’re like, what is the least amount of time that could be the most fun and interesting and engaging and effective for leaders to build up their confidence to lead in data in their organizations? So we’ve got it down to our five week course, and, uh, we run them. Both as open courses and in-house courses, and we’ve done courses with networks of organizations that are operating in the same field.
And I think that peer bit is really interesting. So we’ve, we’ve had the joy of working with the law centers, networks across the UK and the wildlife trusts across the uk where, you know, you’ve got like a dozen leaders from organizations are working in quite similar contexts. Mm-hmm. So there’s quite a lot of peer learning in that journey, and they cover those seven themes.
You know, we cover the purpose bit to begin with. Uh, we talk about the practicalities of the tools and the data and data quality and data assets. We talk about skills, we talk about leadership you know, board responsibilities, that sort of thing. Uh, and of course we talk about culture, which of course is actually the key to it all.
So, yeah, I think that, that kind of group of leaders what we’ve, the feedback we’re getting now, so we started these courses in 2023 and we’ve been following up with people who did the course to find out, have you been able to affect change in your organization? Where have you got to? And it’s been really encouraging hearing the, the stories on what these organizations have, have gone on to do and what those people as individuals, you know, they’re saying things like, oh, I feel so much more confident talking to technology companies.
Like, I know what I’m talking about. Um, or I know what questions to ask. And you know, a lot of them have made changes and are doing really, uh, innovative stuff like in Scotland that they’ve been doing Scottish Citizens Advice. Scotland have been doing a lot of work around AI and fuel poverty, for example.
And it’s that sort of confidence to, to understand the whole big picture about it and then kind of be pragmatic about your next steps, because you’re not gonna be able to change anything. You’re not gonna be change everything at the same time and you, you, and it’s probably not gonna be very quick.
So taking kind of baby steps, uh, has been really effective. So that’s how, yeah, that’s been our Data for Leaders course.
Matt: Interesting that idea of learning from peers as well from my work doing management leadership development in, gosh, nearly 20 years ago now, which is terrifying. But I always used to find that open courses was so much more interesting than single organization courses because it was, it was a combination of things.
It was first having a broader pool of stories to draw from. The second thing was that you didn’t have any of the internal hierarchy and power struggles or the, you know, that the normal arguments. That would emerge in within any organization. And the final thing is actually just the, the reassurance for people.
It’s not just you.
Sian: Yeah. Yeah. And we hear that a lot actually. Yeah. We hear from people who sort of say, well, we talk about the roles of responsibilities. People going, God, no wonder I’ve been carrying all this myself. I’ve got like, you know, 20 jobs I’m doing. No wonder, I feel so stressed. And so it does open up that kind of collective understanding about roles and responsibilities and what, you know, who needs to be involved.
And sharing and working it
Julia: out or making it up, which lots of these people are doing. Yeah. You can just imagine the pressure, the relief to learn about this.
Sian: Yeah. I mean I do, I’m really curious, I dunno what your experience is of working with organizations of what, what the ratio of data people to everybody else is in an organization.
But I mean, I find it really to be really diverse. So if you’ve got, like, if you work in a 10 person organization and you’ve got one data person and you’ve got a 10% ratio compared to, I meet people all the time who have like started in one job, it’s morphed into another job. Now they’re head of data, they’ve got 200 staff and, and nobody else on the data team, you know?
Yeah. So, uh, yeah, it’s
Julia: interesting. Think demand. What do you think demand frequently outstrips capacity. For data, and then you get demand from people who aren’t experts. So you’re generating lots of data that actually can’t be useful because there is such a skill in knowing which questions to what are the right questions to ask.
Um, and you need proper trained data professionals to help you with that.
Matt: Yeah. I think as well the, the silo problem you’ve got people who don’t understand how data operates. You’ve got people who understand data within their context, but can’t appreciate the data, doesn’t span out of context unless it’s designed to do so.
And then you’ve got people who understand the bigger context version of it, which is that you need, you know, I mean, in, in old money you need things like corporate data models for any of this sort of stuff to fit together. The people in that latter category are very few and far between. Mm-hmm. And actually, of all the organizations I’ve worked in over a 32 year career the only place I thought really had it nailed for a while was the BBC when I was there in the mid nineties to the mid two thousands.
And the, the very specific reason for that was that they had a corporate data model called the Single Media Exchange Format Smith. And it was developed for a very, very simple reason. It’s because the metadata that was coming out of early digital equipment from Sony, from Panasonic and from Canon was completely proprietary.
And so they needed an intermediary middle layer. Hmm. Which enabled them to translate the metadata from system to system to system. And they had no control over the, the source systems for this. And that was the reason. In hindsight, I think that SM was good when it was, but then people started to extend it too far and they started to build systems based on that as a physical model rather than as a conceptual idea.
And then it all fell apart ’cause it all got far too complicated. But that was a, you know, very clear reason they had why they needed to do this back in the, I mean, that, that project started I think early nineties. Ever since then, even, you know, when I worked for Reuters, their data management was appalling, even though they were essentially a data company as 20 odd years ago.
And in comparison to Bebe, it was so, naive in their approach. So. I, you know, I think it’s the, the, the people who can manage it from the perspective of seeing across the whole piece.
Sian: Mm.
Matt: And that’s why it’s so important that people like CEOs have appreciation of this stuff, I think.
Sian: Yeah. Yeah. I agree.
And I think it, it’s also about being able to I suppose to create simplicity out of all that complexity and what, what I do see in a lot of those really mature organizations, is the stage mean we’ve, I’ve seen organizations do this where they’re culling the data. Like no, I mean, like, there’s danger.
I’ve read your Hedges piece Matt, so don’t, don’t kill everything. But like, actually a stripping back of like, what’s the most important and most valuable and, and a, and a focus and a clarity about what’s the, what’s the most. Important and valuable and useful and meaningful data that we should get, and what can we let go?
Because there’s a, there’s always a trade off there between the extractive nature of collecting the data and what actually becomes useful in the end. And that balance needs to be struck, stuck.
Julia: Yeah. I’ve been in the experience where tech teams get very keen on collecting loads of data and it’s just overwhelming.
And you, it’s very hard to know what it’s telling you. Um, and that’s where the skill is, knowing which to ask. Yeah.
Sian: And,
Julia: and leaders actually
Sian: mostly designer, ask really good questions. I think that’s I’ve just been, uh, talking to lots of CEOs in the, um, AVO network, the Association of Chief Executives of voluntary Organizations about ai and, um, it’s really.
It is really reassuring actually, that these are very intelligent, thoughtful people who have you know, a good balance of curiosity and caution and uh, are very thoughtful about all kinds of technology and all kinds of applications of data.
Julia: So now is the. Of WB 40 that I always find the most challenging, and that is where we think about what’s coming up in the week ahead. So Anne, what have you got planned for the next week? This is the look at your diary. Look on my diary.
Sian: Well, this week I have got in a week, well, a week today I’ve got a VO fest.
So I am going to be in London with lots of other CEOs, uh, working in the voluntary sector. And I absolutely love it. It’s a real it’s a really rare occasion actually to talk to other leaders about all kinds of things. We’ve also got our kicking off with our Canadian partners, so for the first time we’re going.
Multilingual, we are going to be developing the French Canadian version of the data maturity assessment. So that is super exciting. We’ve been wanting to go international. I mean, I said we would go global before, but we’ve wanted to go multilingual for some years. So, for a while we thought it was Welsh.
That would be first, but it turns out it was gonna be French Canadian. Um, so that’s, uh, that’s super exciting. And what else are we doing this week? I should have,
Julia: I’m jealous. I’m jealous of your
Sian: multi
Julia: opportunity.
Sian: Yeah. Well we’ve, we’ve had a lot of interest. We had actually, we had a lot of interest from South Americans, so there was the, the Spanish thing.
I mean,
Julia: you don’t, you don’t have to have more than three interesting things coming up. So I think you’ve
Sian: already
Julia: met,
Sian: if not exceeded
Julia: the crater.
Sian: Yes, I think I have. Oh, and Scottish government. We we’re, we’re going to be looking at our seventh cohort of organizations in Scotland, uh, in the public sector, taking a data maturity assessment.
So yeah.
Julia: Busy week. That’s, that sounds fantastic. I’ve been looking for opportunities to use French at work ev forever, and you’ve come along and you’ve got one, so I’m very envious. Matt, what about you? Have you got as many exciting things coming up as Anne
Matt: and, and possibly not, and, you know, exciting as a very subjective concept, isn’t it?
Uh, I’m going to be going to a. An event organized for suppliers from one of the major government departments tomorrow afternoon which will be, I’m coming
Julia: with you, in fact.
Matt: Yes, that will be, there’s a number of ways that could be interesting, so we’ll wait and see.
Julia: Yeah.
Matt: On Thursday there is, um, another one of the TBD Paul Armstrong’s breakfast events happening in London, and the guest speaker this month is a guy called Near Isle.
Who I know from a book he wrote about probably 15 years ago, called Hooked, which was about how to design software to be he denies, I’ve had discussions with him on Twitter about this. He, he denies, uh, it being addictive, but to my mind, if you’re gonna call something hooked and you’re talking about design principles, it’s kind of, uh, addictive design principles.
He’s gonna be talking about his new book, which I know nothing about, but I’m interested to hear. And there is a little bit of me thinking that maybe that N’S work was far too successful last time because an awful lot of organizations did follow the ways in which he thought about designing habitual patterns, uh, in a way that maybe has not benefited mankind.
Humanly human. Yeah. And then apart from that, I’m gonna be finding opportunities to be able to record more short videos to publicize. Did I mention I’ve got a book coming out to publicize my book? So the event that we’re going tomorrow is at the Royal. Institution. And I’ve been, um, digging into that.
And so I think I might be doing a short video before, uh, that session starts to talk about how the randomness of how Michael Faraday, one of the most prolific scientists of the, the, the 19th century became a prolific scientist based on the fact he randomly got some tickets to go to see another, I can’t remember the name of the, uh, the scientist he went to go and see.
But there was somebody who used to do talks at the Royal Institute and um, Faroh got some tickets randomly ’cause he worked as an apprentice Bookbinder until he found science. So, uh, Davey was the name of the, uh, the scientist that you went to see. So, um, bit of that I think. Um, how about you, Julia? What have you got in the week ahead?
Julia: Bli me? Well, so one of the things that I learned boosts my energy. Is going to new places. So I am looking forward to going to the royal institution with you tomorrow, and I’m also looking forward to some co-working days that we’ve got coming up next week in various different locations. I dunno how much energy boosting I’m gonna get out of traveling across London on a tube strike day, but I will do my best.
I’ll let you know actually if it ends up being an energy boost or an energy suck. Ah,
Matt: yes, the tube strikes. Uh, anyway, that’s it for this episode of the very pleasing 345th episode of WB 40. Um, cn thank you so much for coming and joining us again on the show.
Sian: Thank you very much for having me. It’s been lovely to, to meet you, Julia, and uh, lovely to see you again, that thank you.
And,
Matt: uh, thank you Julia for your, um, wonderful co-hosting. Yes.
Julia: It’s always a pleasure. Thank you.
Matt: And we will be back again next week. There’s three in a row unless something untoward happens, which is always a possibility. Uh, next week we’re gonna be meeting with a chap called Dan Bocher, and we’re gonna be talking about his initiative to be able to help men talk about men’s mental health.
So a complete change is seen. That’s what we do on WB 40. You see, it’s all about what you need to know to manage technology, and surprisingly, that’s often not about the technology. Until then, Anne, thank you Julia. Thank you. And we’ll be back next week.
Sian: Thank you for listening to WB 40. You can find us on the internet@wbfortypodcast.com and on all good podcasting platforms.