Wardley Maps: Can Maps do Good?
MapCamp 2020
Can maps do good? We discuss whether maps are an effective tool in the effort to make change for good. We look at approaches to determining what good looks like, how maps can help us understand where we really are, to find paths to better outcomes, and how they can be used in combination with other tools and data to do better.
Transcript
Gen Ashley: Hi everyone. Good morning and welcome to the Square Window. The first block of the Square Window. And this morning we're gonna be looking at and talking about the theme "Can maps do good?" And we have three excellent speakers lined up for today. We have Matthew, Andrew, and Liz, and yeah, without further ado, we can get started with the first speaker Matthew.
Over to you, Matthew.
Matthew Adams: Hi there. Is everybody okay? Can you hear me? Am I still audible? This is good. I apologize. Some builders have just decided to start up work outside at 9:30. If that interrupts things, then I shall speak up. So I'm gonna be talking on, as you say, on the subject of "Do maps do good?" and I'm gonna start out quite abstract and then get a little bit more concrete if we can.
And I'll just share a screen. I promise that there are no slides full of dense text. So to answer the question to start with, I'll say "No, they can't." Why do I say no they can't? Well, a map is just a model and a model isn't an actor in the sense of being able to do good. "Doing good" is an activity that people imbue with some kind of moral value.
And so they can't actually do good in and of themselves. They're only a model and they're as useful as you make them useful. And they are as accurate as you make them accurate. It's the thing I like to bang on about all the time. They're a tool and not a sort of end in themselves.
So no, they can't. There we are. Let's stop there. Or we could take a slightly less reductive view and say perhaps maybe yes, they can in some sense of helping us to do good. And that's what I'd like to explore a little bit more in this talk.
It might seem a sort of a glib point to ask can they do good? Yes or no. But I think actually that is the essence of this really and what I'm trying to get at is a framework for how we can do good and what tools we can best use to do that. And not only do good, but be seen to be doing good. And maybe we should change the question a little bit.
Not so much, can they do good, but how can they be used to do good and how do we know what good is? And what I want to do is use a practical example for that. I'm gonna use the EU taxonomy for environmental sustainability, and the reason I'm gonna use that is, it's very difficult to say. And I have to say it all the time in my daily life and I get it wrong every single time.
So you'll notice I look down at my notes to make sure I was saying the words. It's a really good tongue twister, have a go at it. What is the EU taxonomy for environmental sustainability? It's an EU-wide framework for assessing activities and determining whether they are environmentally sustainable.
It feeds into ESG reporting. Environmental sustainability reporting affects asset managers and investors and governments and regulators, other interested parties like that. And it exists because people realize that governments and others were mandating this kind of sustainability reporting.
But it was essentially useless because nobody had any idea what any of the things people were reporting actually meant. There was no framework in which the data was being evaluated and it was all very ad hoc and descriptive, and actually it's being used. The work is being used beyond the EU and is actually having a global impact.
The EU is taking a lead on this. Now where this gets interesting for this conversation, I think, is that they have a mechanism for defining what good is in their context. For something to be defined as actively sustainable, which is the good they're trying to achieve, it has to meet four criteria.
And those four criteria are that it must substantially contribute to the objectives. So one of the objectives they set out, they've actually set out four or five sort of core objectives. Things like move to a circular economy, recycling and waste management, maintenance of a healthy ecosystem.
They're their core objectives and whatever an activity does, for it to be actively sustainable, it has to directly contribute to the benefit of one of those. And secondly, it must do no significant harm to the other objectives. Okay. It's a bit like the Hippocratic oath. You can't improve your recycling by turning the whole of the Cotswolds into one massive recycling center.
And third, it must have compliance with what they call minimum social safeguards. What that means basically is this is actually a set of baseline safeguards that are published by the International Labor Organization. And they're about things like modern slavery and working practices, those kinds of things.
And what you must do to be actively compliant is do no harm in this domain, beyond the objectives. So you're not required to actively improve modern slavery conditions for example, but you're required to do no further harm. You can't develop your sustainability program by employing an army of slave labor, for example.
And finally it has to do what they call "comply with the technical screening criteria." Basically, what that means is that you have to provide evidence for the three things above, and I think there's a really good way of thinking about doing good in general terms. And these four principles I think are very generalizable: define your objectives and the surrounding landscape.
Show how an activity contributes to those objectives. Do no significant harm either to those objectives or the landscape beyond and provide evidence for the above. And how can mapping help with that? I think it can actually help with all four of those principles, because doing good as we've defined it here, and there are plenty of other ways of defining it, but I think this is a good and narrow way of doing that.
Doing good means making change. It means actionable, something that's actionable and something that's measurable from the effort of that action. And I don't think that I really need to rehearse in this context that Wardley maps are a great way of helping us to describe a status quo and then to look at the impact of change on that status quo.
So I've got one here. This is a classic make, use, dispose, pollute value chain that humans have been doing since at least the late Mesolithic, when we introduced farming. And this is the, somebody makes something. We use it, we dispose of it and that naturally causes pollution.
And we can also define our future state. The future state that the EU taxonomy is looking for is one of those is a move to a more cyclic economy. So here's an example where we've introduced a couple of cycles. We've got a make, use, recycle cycle here. We've got a use, reuse up in the sort of consumer space up here cycle.
We've got a use, remake, reuse cycle so that we've introduced with a couple of extra sort of activities we've introduced three cycles there. The other thing I think is worth pointing out here is that for these kinds of doing good maps, I think it's really useful to put your negative value on there as well, outcomes products that are actually undesirable.
And highlighting those undesirable outcomes is really useful. Now we can produce a family of these maps for all of the objectives that we've got within our framework for doing good and for our surrounding landscape. And that gives us a framework, which we can use to model any activity when we decide to determine whether it's going to do good or not.
So when we consider the activity, we can take one of those existing models, annotate it, and see whether it contributes to this new desired state. Very popular thing, curbside recycling. It seems generally a good thing. So let's add curbside recycling to this chart. There we go. You can see we've got an hour make, use, recycle but it doesn't actually seem to have done any good.
When we add that recycling in we are actually, we're actually causing more pollution because our curbside recycling mechanism is a net contributor, not carbon neutral with the way we're collecting things with lorries and things, and actually processing the output. There's no downstream consumer of that recycling.
It just goes, it just adds an extra complication into the make, use, dispose, pollute cycle. And that's a really interesting point. If we look at our desired outcomes here, it's only when we introduce that loop, that cyclical part of the process, that we drive towards our desired outcome.
The curbside recycling is only a positive contributor to that loop if it's performed in conjunction with another action and then it does become a good. Then it becomes, if we, for example, add modeling for our aluminum recycling supply chain into this, we can see that is a way of enabling curbside recycling is a... Now all the maps are really good for showing these kinds of dependencies. And they're often the kind of unintended consequences from what we perceive as a good. It can highlight those and bring them out and show us where our assumptions are falling down because we're not actually mapping that action into our overall set of objectives.
Now, of course, this is very high level, but even at this very high level, it's a really useful tool. And this comes back to my point, right back at the beginning. You don't need the nth degree of detail to reveal important parts of your value chain, whether that's doing good or any other desirable outcome. It's actually better to start at a very high level and then drill in if you need more information to understand what's going on.
And actually that's where we might come into the next bit. Because one of the hardest areas to deal with when doing good is providing the evidence that we are doing good and doing no harm. The model is all very well. It shows you what you assume to be the case and you're intelligent people doing research and you've probably got a good model.
That fits the expectations of all those who are involved in modeling it. But how do you evidence that the model is meeting your requirements? Wardley maps, I think, provide a very interesting tool for providing that evidence because they help you to find the dependencies in the system.
But we found that the big challenge, when you do that, is that actually many systems and perhaps even most don't actually have information flows within them that enable you to gather the evidence that you need. And when we look back at the Wardley and say, right back at the beginning, we're trying to bias towards evidence.
That's a bit of a fundamental flaw that we need to sort out before any of this is useful. And it's actually one of the big challenges for the EU taxonomy right now is that they've got a lot of really great structure. But we are not yet at the point where we understand the evidence. Now, this is a model on the left here that we produced in a workshop with a client that as part of a sustainable finance initiative that I'm involved with and that left hand side is a very simple model of primary production in agriculture.
And what we realized was that while there were financial information flows throughout that system, as there very often are, the actuaries have got, are very good at financial risk over the years. And most systems have that financial risk deeply embedded within them, but there wasn't a lot of information about anything else.
If the goods we are looking at are trying to mitigate soil starvation or pollution or environmental destruction, we need some way of measuring those things. And we need some way of measuring those things consistently. And where we also discovered that there are very few incentives for driving that information through the system and another view of this world.
Another way that's, another map that is useful in this context, particularly in the terms of doing good is what is the value flow. We know where we want to get to, but we don't understand what the value exchanges are throughout that process that encourage people to participate in that process of good.
And another thing that we then went to do, we then moved. So when we were trying to explore this further we produced the diagram on the right, which is what, how is the reporting done right now? How is the, how does the taxonomy encourage reporting? And what we discovered was that almost all of the elements in that are unconnected.
They have no path of connection in that reporting chain, there's no chain of evidence. There's no way of demonstrating the reliability of the evidence being passed from one point in the chain to another. And then there are very few ways of defining what the way in which that evidence should be interpreted.
And very few feedback loops to validate and process at any level in the system. And as we look to other systems, we realize that this was pretty common, too. And it's only, the people participating in the workshop had never done any Wardley mapping before. It was only when we produced a map like this, that they understood how deeply disconnected everything was.
And the scale of the problem that we were trying to solve. What do we do? I do think it's, I do think it's actually quite simple and it does come back to those four principles that I outlined at the surface, the conversation here. To do good, we need to define our objectives.
What's in scope and we need to define our surrounding landscape, what's out of scope, but which we, in which the area in which we must do no harm. When we are looking at an activity, we need to show how it actively contributes to those objectives and how it actively does no harm. And we need a model for driving the evidence, the chain of evidence through that system to ensure that we are doing it because we need to be able to provide the evidence for that. Otherwise we have no idea whether we're actually achieving our goal. Sometimes that's often the most visible part, but the doing no significant harm elsewhere, that's obviously often the most invisible part of the process.
And we use maps to define what the less good to good transition looks like and make sure that we include those negative outcomes as value on the map. And that's my conversation for today. I hope that's a good conversation starter and we can take it from there.
Gen Ashley: Great. Thank you, Matthew. That was a very good talk. We're just gonna wait for Andrew to come back with video.
Matthew Adams: Not too massively over time either.
Gen Ashley: No, you're good. And we're just gonna wait for Andrea to come back with video.
Okay. I think I'm gonna get started with the first question. So in the context of the EU taxonomy, is this approach more about persuading people that you are doing good rather than actually doing good?
Matthew Adams: That is an interesting question. In the context of EU taxonomy, I think there are all, this is, this gets into the reasons why we choose to do good, right?
Sometimes we choose to do good because we actively want to do something good. Sometimes we choose to do good because someone is standing with a big stick behind us telling us that we need to do good. And sometimes we do good because we are worried about how other people perceive us and all those reasons for doing good are good reasons for doing good.
I don't, there's obviously there's some room for philosophical wiggle here. But I think that actually when we do good it's for a combination of all those reasons. We like to feel good about doing good. We actually want to improve things.
We fear what happens if we don't do good whether that's intrinsic or extrinsic. And the only way in which as a society we can do good is if we can bring other people along with us. And so this evidence and persuasion part is an important part of any process of doing good. There's then actually the practical implementation and one of the nice things about applying this kind of process, these kinds of those four principles is that they go hand in hand. The persuasion and the implementation become a part of the process and a connected part of the process.
So I hope that answers that question.
Gen Ashley: Yeah. Thanks Matthew. I'm gonna ask Andrea or Liz, if you've got any questions for Matthew.
Andra Sonea: Yes, I was wondering what did you perceive was the motivation of your clients engaged in the Wardley mapping? Cause I understand you are the mind of this and you see where it goes. You have the framework, that it goes towards doing good, but from the clients, I rarely, meaning organizations of sort, you rarely perceive this explicitly. We want to do good, maybe better, but good. It's...
Matthew Adams: Yeah, I think that's really interesting because what we tend to find is that, so I work for a company called endjin and our mantra is helping small teams do big things, right?
And in this context, those small teams that we find are often the nucleus of a set of people who want to do good within an organization. And what we are trying to do is amplify their ability to do that within the organization. And that nucleus is set up for a number of different reasons. So for example, in ESG in particular and corporate finance it's becoming important because it's becoming important to the regulator.
It's becoming important to governments. And so it's becoming important to those organizations at a corporate level. And that's why people started doing their ESG reporting. And there's a great chunk in their annual report every year. That's being carefully drafted and contains lots of lovely meaningless words.
And but part of that is that they got some budget and they gave the budget to people to be the ESG team and the people who took that budget, all the people who were not interested in that ran away from that budget as fast as they could, because they did not want to be associated with that. So you end up with a core team who have a small budget, but are really motivated to do some good.
And then this helps them to understand the true enormity of the change that they've taken on and start to identify ways in which we can focus that in, on specific objectives and understand how that can be, how they can run a path through their organization and out into their clients.
Because one of the key things this, that this kind of mapping exercise shows us is that it's not isolated within a single, none of these value chains are isolated within a single organization. It's almost impossible to do good at this scale even inside a global corporation because the interconnectedness of that value chain is too great now.
The motivation for these people is that they are intrinsically motivated to do good and extrinsically enabled to do it by the organization. And then these, this gives us, this is a really useful tool for them to be able to communicate what they're trying to do, what needs to be done and to focus on the sort of step by step process, particularly when you, then, the, I've not talked about it in here, but when you go back to the back to the Wardley doctrine, that's a, again, as always with transformation, we are really at the sort of phase one, really getting the basics, the real basics in place in most doing good, because most doing good has not yet got to the point where it's biasing towards the evidence of communicating well with all the parties, even understanding who the parties are, who the stakeholders are.
We're really poor at that because doing good is traditionally so low budget, even in large organizations, big charities, any individual thing is so low budget. And so constrained by the needs to try and get as much of that budget into the activity of delivery. That often it doesn't have the time or space or brain power devoted to it to understand the longer part of that chain.
And I think that's bringing that out and helping people to understand and then manage the enormity, cause that's the other part of it. It's not that this is an unfathomably challenging problem. It's that you don't realize what a mess it is and the map helps you see the mess and you can start sorting it out.
And that's, that I think is really powerful way of looking at the world.
Andra Sonea: Yeah. Great.
Gen Ashley: Great conversation so far. Liz, do you have any questions for Matthew?
Liz Keogh: I wanted to know what the most surprising thing you've seen emerge on those maps was.
Matthew Adams: The hundred percent, the most surprising thing was that nobody had any idea what they were talking about. As in there was no, there was, in a project that's about taxonomy, the realization that nobody really knew exactly what anything meant.
And that there were no, that there was no mechanism for explaining, validating and feeding back. The fact that people had understood what was going on was the most surprising thing. The analogy that I use, it's on that slide, is there's no GAAP. For ESG data gen, the accounting after, after the last, but one major financial crisis went through a whole process of saying, hang on a minute, we realize we don't understand what any of these measures are in the system that we've got.
We need to get some generally accepted practices together to on how we interpret this data and what it actually means. Having taxonomized the objectives, the taxonomy is doing a great job of taxonomizing the objectives and mapping industries into those objectives. And what activities impact on those objectives?
But they have not thought about the evidence. They've not thought about the process of evidence at all that gives meaning to those definitions. And that's fair, right? This, it's not like, it's not, this is by no means of criticism. It's just, I think they assumed that everybody understood what everything meant.
They've not yet got to the stage of realizing that they didn't. And so this was the first opportunity to bring that out and reveal it as the next big challenge.
Liz Keogh: Okay.
Gen Ashley: We actually have a few questions coming in. I'm just gonna read them out. The first one is how do you know if your map is too little or too much information? In the context of the talk, several of the maps seem relatively simplified. So were they simply a good conversation starter?
Matthew Adams: See, so I think that maps should be too simple. If you reach the point at which you think the map is now just about complex enough, it's too complex. And if you need to, if it explains your concept clearly, and without extraneous detail, then that's enough.
And if you then need to do, if you then realize you need some more information, I tend to draw another map. And then build up a family of sets of maps that you can use together. And it means you have to do a little bit more work to stay on top of keeping them in sync.
If they're all still alive. But that brings me to another point. If a map isn't still alive, it's, if it's archival, then get rid of it because, put it in the, get rid of it. Put it in the archive because if it's not alive and it's not delivering value, it's just taking attention.
And every map should be very simple because either it's very focused or very high level and you need to be able understand how you navigate from one to another, and you can build a map for that too. So I, that, I hope that's, I'm being highly opinionated, feel free to knock me back.
But that's the way I use them.
Gen Ashley: Okay. Thanks Matthew. So our next question is this, what do you think the impact has been of economic models around negative externalities concepts when it comes to harm in value chains?
Matthew Adams: What do I think the impact has been? I think that in this context, I think that the, there has been an important shift over the last year or so to understand that these negative impacts are probably as important, if not more important in the risk profile of an organization as the positive outcomes. And there's been a clear shift in desire for people to be able to model this kind of risk. So as we move purely from the sort of financial actuarial risk to this kind of ESG actuarial risk and there are, but people are struggling to understand how to do it.
And that's quite, that's quite legitimate. We're also really in the only in the very early stages of this as well, because we are looking at the moment really. We are focused on sort of investment risk. We're not look, we're looking at direct, actionable risk.
We're not looking at a sort of structural consequential operational risk within the system, and that's where I'd like to get to, because I think actually my instinct is, and we need to drill into this some more, but my instinct is that's actually a greater contributor to the financial risk, the investment risk down the line than the pure am I investing in activities which are seen to be actively sustainable. I think actually we need to look at the operational activities of the businesses concerned and see how that impacts on that. And actually they build the chain up from there, but that's, that's another order of magnitude away from where we are right now.
Gen Ashley: Okay. Thank you very much for that, Matthew. And thanks for the questions, Andrew from the audience as well. I think now we're gonna get with the next speaker who is Andra. So over to you.
Andra Sonea: Hello. Okay. I'm a solution architect and researcher at the University of Warwick researching things related to urban space and economic phenomena in space and time. Today I would like to tell you a story about using mapping for basic banking services. So why? I think, oops.
Okay. How I think this works. So when we talk about basic banking services we usually refer to being able to receive payments or make payments. And for probably all of us here in this conference it also means that we have a smartphone and on that smartphone, we have, I don't know how many applications which allow us to make payments in many different ways.
So you would ask yourself, okay, what's the problem? Is this a problem for today's age in the UK? And I would say that it is because actually not only in the UK, but also in many other European countries basic banking services are considered services of general economic interest which means that they are usually not satisfied by pure market forces.
And usually a state intervention of some sort is required. So in the UK, for whatever reason, the government decided that Post Office is the provider of basic banking services. And when they did this, they were also allowed by European Union to provide state aid to the Post Office under the condition that they maintained this number, 11,500 Post Office locations across the UK.
And you will see this number on websites, HSBC, all the banks in the UK, but also smaller banks. So if we look at Starling, which is a challenger bank, they also state you can access us through Post Office. And they are considered somehow a replacement for bank branches.
You see here on Starling, they also provide a percentage of coverage, which is like 99% of the population live within three miles of a Post Office. What are the basic banking services actually? So they're very basic indeed. You can check your balance. You can withdraw cash, you can deposit cash, you can pay utilities.
So just a bit more than an ATM. I would say. I would draw the attention here that you can pay utilities, but you cannot pay a friend. You cannot pay a member of a family. You cannot pay a company which is quite restrictive. I would say that percentage of coverage of the UK territory comes from the official access criteria in the UK.
And here I choose to show just two of them. And to give you an example, how this works. So the government says that 99% of the UK population shall live within three miles of the nearest Post Office. So if this blue thing here is a Post Office, the very, the darker shade of blue around it is a distance.
If you go on the real street network for one mile within the Post Office. And the other one, the lighter blue is three miles. So it means that if you live outside these blue shades, for example, in this part, you are part of the 1%, not the, the other 1%, not the wealthy 1%.
I was wondering is this really true? Because everybody takes these numbers for granted, but there is no methodology for this calculation. There are no granular results and it didn't ring true to me at all. So let's see who are those people who live outside those blue areas and how their Wardley maps look like. It looks a bit different simply because for whatever reason, there are categories of population which can be identified, which do not use online banking or mobile banking either because they don't have a smartphone or they cannot afford one, or they cannot use one.
Or in their area, there is no broadband or a combination of the two. And this restricts their choices considerably. So even more, if you live outside those three miles. Or would you walk for three miles actually to go to a Post Office to pay something? It's completely out of our realm of experience actually, because we have the phone in our pocket all the time.
Almost all this map here is in the commodity section, in the utility section, at least in our mind. And usually when I used Wardley maps with companies, my focus is on finding things which are in Genesis and in custom built, what should we build next? What should we build next?
So things that don't exist, but on this time, almost everything was going out on the, off the map on the right hand side. So let me, so with for example, if I look here at the option, somebody wants to pay a personal company. Okay. They need to have an account. They need to go to a Post Office.
Probably they need a car as well. Probably they need a driver if they don't drive or they're too old to drive and they need petrol and so on. So all of a sudden the transaction is considerably more expensive for them than it is for us. So I will take it out to a completely different type of map.
Can you see this? I hope so. So this is the map of Wales, and I thought, okay. If we are told that 99% of the population lives within three miles of the Post Office, let's calculate this. And I web scraped the Post Office website. I extracted the point, the Post Offices.
I put them on the map. I also extracted their opening hours. So if we look at Wales what we see is okay, it has lots of national parks, that's a lot of national parks. And probably people don't live there. Yeah. We also know that Wales is not, is sparsely populated. So that probably is not a problem.
There are not many people living in the national park, but let's zoom in and see. Let's zoom in and see, I don't know, a detail. Let's look here. So this seems to be a little town. It's a university town. It's a place where people also go for holidays as well. So what's happening here? So they have two Post Offices here, two Post Offices here. What are these red ones? So these red ones, if we look at the capacity, which means opening hours, it shows 0.01 capacity's half an hour.
So we see that they're open for two hours per week, maybe three hours per week and so on. So what we observe when we do this type of map, we see that this point contradicts the government calculations that people live, 99% of the people in the UK live close to a Post Office. Hence we can close branches, banking branches, because they have a Post Office nearby.
But in reality, it's a very temporal structure, let's say, and it's not something you would start driving three miles towards, maybe that mobile Post Office doesn't open today. Maybe the guy is ill or he simply didn't turn up. So if, and if we, these are driving distances by the way.
So if we look at Wales and the one mile, what they're called isochrone areas, where you can drive for one mile to a Post Office, it looks really bare. So what do we learn out of it? Coming back to the, coming back to our results. So I said, okay, let's replicate or try to replicate or build a better measure for the construction of such indicators of coverage of the UK territory.
And my results were completely different from the government. I would say completely different. In Wales, if we look at Wales. So if we change the scale of the map, which is important, not the whole UK, but we look at Wales, it's not true that 99% of the people live within three miles. It's 93. If you go by car and it's 95, if you walk and for one mile, the percentages are considerably less.
If we take into consideration Post Offices of a more permanent nature, the percentages go down considerably. Why this is important? It's important in the context of simply because it's 10% of the population of Wales, it's actually quite a lot. It's bigger than the whole population of Cardiff.
So what do we learn? So I use different types of maps. I use the map, the Wardley map to make explicit, what do we need in order to provide that basic service and then a sub map or a geographical map to explore if the indicators which are given to us are true or not, can we rely on them or not?
So what I learned is that, or what we learned in general is that in a way it's nothing new. I don't discover these things. It's more obvious when I apply it to a particular case, that if you don't make the methodology explicit for constructing a map, you can come up with anything really. How you make the distances, how you measure distances.
It makes a big difference. The government measures the differences in a straight line. But this is not how people move. Yeah. When you do a map you have to choose a propagation method for measuring distance, which is appropriate for the phenomena that you observe.
So people move on roads, so we should measure distances on roads. And also if you play with the scale of the phenomenon, you observe different things. So you have to also choose the scale for your maps. And this is true in maps and in geographical maps. So coming back to this, my worry is that, my worry, so the sub map that I explained, or I explored, it here in the commodity section and it's falling off the map and it's falling off the map for reasons which are bigger than us.
It's a reality but I'm trying to figure out, okay how do we fix that and this, or what can we put in place? And I personally don't see it. Don't see it yet. So we are, so you are aware of this S curve, which is the innovation curve, which Simon also uses. So normally when you see this map, you have this S-curve here and then you have another S-curve starting from somewhere underneath the commodity and going up towards the right hand corner, let's say, so we are in this place here where we see the end of commodity and probably something has already appeared.
But people fall through this disruption. This disruption is not without pain. So what I map here is not the product. It's actually an infrastructure. And it's interesting to see the end of a type of infrastructure because we don't get to see them very often in our lifetime. Infrastructures respect or live by a different timescale, a historical timescale, not human timescale.
So to witness in your lifetime, the end of one and starting of another one, it's quite interesting. So yes. If you have any idea how to deal with that, or if you have seen examples of other types of infrastructures and commodities disappearing, I would be very interested to hear from you and I stop here.
Gen Ashley: Okay. Thanks, Andra, that was very good. So we're just gonna wait for Matthew and Liz to come on video. But I'll get the first question started. So why did you choose to focus on this problem? And also why maps and why the Post Office?
Andra Sonea: So it, I think it comes at the intersection of two things I'm interested about. So on one side, in my daily job, I work with systems. I do systems architecture, I build systems and I usually use Wardley maps to figure out what to do next. I spent probably the last 10 years on identifying the next cool thing. And at some point I realized, in all honesty, let's see who uses these cool things because they didn't transform the world as we imagine it, or we, it didn't democratize anything, what the hell is going on.
And this is when I started to do real maps to, and I do this in my academic work. Let's say I use a lot of spatial analysis to look at phenomena and I started in the search for people too. So if I want somebody to have access to my mobile app, first, he needs to have broadband of some sort, the infrastructure needs to be there.
Then he needs to have money to pay for that. Then he needs to be able cognitively to work with money in an abstract form and not everybody can do that. And then I realized that actually we live in our focus towards the next shiny thing and disruption. We leave behind entire categories of people through no fault of their own have the infrastructure taken away from them.
So with this question, or basically it was a step by step realization. This is when I realized, oh, Post Office. Maybe I don't use them, but for some people they're very important. And by the way, they're vanishing. Yeah.
Gen Ashley: Thank you for that. Liz or Matthew? Any questions for Andra? Matthew? Go ahead.
Matthew Adams: Yeah. I'm really interested in space and time. Not just cause I was a physicist once upon a time, but because I think one of the things that people lose track of in these kinds of almost all GIS applications actually, is that the time dimension is as important, if not more important than the space dimension and the, but one of the challenges is that our screens and our paper and our maps are two dimensional.
Yeah. So how do we, oh, now, we have techniques for layering that kind of time transition into the mapping, but how do you think we can best deal with that. What do you think we, do you think we need a technology? Do we need a conceptualization leap to get a better, to help people to understand the time dimension better?
That's what I'm getting at.
Andra Sonea: Oh yeah. It's not easy. So where I see this done more successfully, so we probably know there's a lot of, or the visualization of the information has evolved massively. So you can now visualize phenomena, which happened in the brain or things like that.
Unfortunately, you cannot use this type of visualization in the academic papers, which are printed and so on. But actually what I've showed you, it's a temporal phenomena because I can create that map and visualize it during the week. So it happens that in Wales you have coverage Tuesday at lunch, but if I run the same map Friday morning, you have nowhere to go.
It's something like that. So it's a dynamic, it's a dynamic map which now can be built. Not necessarily, I don't use pure GIS, I use Python. So it gives you a lot of freedom to build the type of visualization that kind of you have in your head. And you think you can build, and I find the visualization, the right visualization, equally powerful as a method behind it.
I'm not a master of that. I'm but I'm trying. Yeah. Yeah. Thanks.
Gen Ashley: Great. Liz, you have a question for Andra.
Liz Keogh: I thought that was absolutely magical. I was just watching this going. This is so amazing. Thank you. What next?
Andra Sonea: Actually, oh, I'm ready to talk about this for one hour. So the, this mapping, it's a multi-layer, it's a multi-layer phenomenon. So what I showed the mapping of, the map of UK of Wales where people live and we know at granular level of detail, so the statistical data, in UK it's amazing.
We know a lot about the socioeconomic status. And so on. That's one thing. The infrastructure they have access to, the infrastructure of access being Post Offices, banks, hospitals, whatever. It's another thing. But if I focus on financial services, basically there are other layers above that, which I intend to map.
So what I intend to map are cascading failures in financial services. And because it links my knowledge of how banks work and how the financial industry works to how this is experienced on the ground. So in nowadays the cascading failures in financial services are modeled only from the point of view of financial flows and people focus on the places where money are, from this bank to this bank, there are a lot of money moving and so on.
So this is mapped somehow. But the mundane life of people, retail banking, nobody cares about this. So how this is cascading. You can leave an entire area in the UK without any possibility of doing any banking at all, paying anything just by failing two systems. If you know who those systems are, where those systems are and the thing is they are identifiable.
And in theory, this is a national critical infrastructure of the UK, but we don't care to map it to say to. So this is what I intend to map: the critical infrastructure of financial services and the cascading failures that would happen through it. Yeah. Okay.
Gen Ashley: We've got one question actually from the audience. This is from Harry. How do you hope to influence government with your maps?
Andra Sonea: You can influence them if they want to be influenced. So I believe in the power of maps. So as Matthew said, can you do good with maps? If this information is public and I think it should be public. It's not. So if you want to obtain the points, you cannot, you have to web scrape the Post Office website.
If this information is public and people have information and they can act locally to advocate for themselves to put in front of their MP. This is our area. We have in total 20 hours of service per month, per week. Whatever. Is this okay? No, it's not okay. So who cares that they're 10 points, basically I believe in empowerment through information.
What I can do, I can publish a paper, will be published. It'll stay there in a journal that you have to pay for. Nobody will read it, that's the life of the academic, but making these maps public. I think it's powerful. Yeah. Great.
Gen Ashley: Matthew, do you have another question?
Matthew Adams: Yeah. Do you think, one of the things I like about the maps is that they're, anybody can understand them, they're really easily understood. At least at a superficial level, do you think we could do better at using them to inform people, as you're talking about influencing government there and the locality of it, do you think we could use it out of the academic sphere and into the local activist kind of space and find a way of presenting it?
That is, that is comprehensible at that level for the individual MP for the constituency, do you think, and is that as is, or do we think we need to put another layer of explanation around it so that people can understand, people understand it in their own idiom, yeah.
Andra Sonea: Probably it would need a bit of explanation instead of capacity to use minutes per week instead of to explain. So that concept of an isochrone, even if it's basic, people think in this way, but they don't, when they see it, they don't recognize it as such. So from this point, if I go one mile on any possible road around me, and then I draw a polygon around those end points, that is the polygon around you, where you can go for one mile.
So those are those polygons. People don't recognize them immediately when they see them. So probably, I'm still exploring to be honest, what works in terms of explaining what the real situation is. The reason I created it like that is because the official measure or the official access criteria are expressed in this way.
So I use the latest methods for calculating what they say is true and it's not true. Yeah.
Gen Ashley: Okay. I believe Liz has another question.
Liz Keogh: And I noticed. So it was further to what Matthew said about getting involved with local activists and other people responding to this. I was wondering if having, have you spoken to any of these local people on the ground who don't have the access and have you heard any stories, any ways that they're handling this stuff coming into that custom Genesis space at all? Yeah.
Andra Sonea: So because we cannot travel too much or why couldn't travel, because of lockdown in the past six weeks or six months. So basically, maybe it's artificial too, but, and Twitter is not the real world, but I extracted Twitter data and I extracted complaints. And when somebody writes a thread it's what happens to them and how when they cannot pay and how they deal with this.
I think what strikes me, it's a dramatic situation when something critical happens in somebody's life and they cannot do that basic thing to pay, to take a cab, to go to the hospital. They cannot check out of a hotel or they're stranded somewhere where they're not familiar with. Those are the most dramatic situations. As for the usual ones, they're even documented by the regulator by the FCA. And I think this is what started me on this path. The most representative population caught up in this situation are the older age population which need to take a cab to go to the, for, I don't know, 16 pounds, one way, 16 pounds, the other way to be able to do a financial transaction. So the cost of a financial transaction for that category of population, it's massive. It's 32 pounds, for some of them. And some of them, they don't, they're not independent.
They are not independent. They need help. So they're, it becomes extremely expensive. So there are cases documented by the regulator. And in my examples, looking what happens, my examples come from Twitter, but I plan to go locally and interview people when this will be possible. Yeah.
Liz Keogh: My mum lives in a little village in Dorset, miles away from the local town and they closed her Post Office. So this is really dear to my heart. Thank you. Oh. Oh, thank you.
Gen Ashley: Thanks for that, Andra. And thanks for the questions, Liz and Matthew and the audience. There's a commentary from Matt. You're saying he's shocked to hear that you suggest that Twitter isn't the real world. Cause it can be too real.
I'm on Twitter a lot of times. Most of the day probably but yeah I get it. But thanks again, Andra. And we're gonna move on to Liz. Who's the last speaker. Last, but not least so over to you Liz and then we'll get on with the questions again after your talk.
Liz Keogh: Thank you very much. We're at MapCamp, right? Do maps do good? We wouldn't be here if we didn't think they did. And we've already heard some stuff from Matthew. We've heard some stuff from Andra around maps do good. I think maps do good when they're not doing bad. So as long as we can eliminate the times when maps are doing bad, we'll be doing good with them.
I think there are two times when tools of any kind, particularly things like maps can do bad. One of them, the first one is when the bad people have better maps than the good people. And Matthew's already talked a little bit about what he sees as bad and good. So I'm gonna clarify what I mean. And it's around this concept of privilege, which I know gets thrown around a lot.
So I'm gonna explain what I mean really clearly. When people who have lots and lots of choice in the world and can do lots of different things when they use tools to accrue more choices to themselves at the expense of people with less choices and their choices, that's bad, right? So you are creating choice imbalance. So somebody who has lots and lots of money, never has to worry about how to get somewhere or where to get healthcare, et cetera. You can, you can actually map this as well, right? So you can look at where is choice a really new thing or a thing that's ephemeral and it's not stable yet.
So you, I think that the people with fewest choices are people on low lying Pacific islands and tribes in the Amazon rainforest who don't even know that they should be campaigning with their MP. They don't know what's about to hit them and they've got no choice. We've got all the choices about that.
They don't have any choices. I think that then you start going down into places where choice and opportunity is a relatively new thing. Disabilities, people of color, and then women, we haven't had the vote all that long. Black women have fewer choices than white women do.
And then of course you get down to the commodities and the people who've always had lots and lots of choices. Your middle class, middle-aged white male, they're the commoditized owners of choices. You can also look at corporations. Oil companies, because they are rich, have lots and lots of choice available to them.
And they have been using that ferociously over the last few decades to try and keep hold of their choices. If we're gonna give maps to anyone, I would like to see, I would like to see us making sure that we help people to map when they have fewer choices, when they live less than three miles from your local Post Office, more than three miles from local Post Office, those people, helping those people have more choices.
So that's the first one, and this is true of any tool. It's not just true of Wardley maps, true of any tool that offers choices to people and helps them see what choices are available and negotiate better choices themselves. The second time when maps do bad is when they are misleading. And this is something I think we can be a little bit more direct about and have a little bit more fun with.
And it's a bit of a devil's advocate position, cause we all, we know all maps are wrong when Simon talks about this a lot. They're just a model. But I think there is something very misleading in Wardley maps. And I wanna talk about it and explore it and see if it leads us to a little bit of a different way of thinking of these things.
Now I'm still, even now more familiar with Cynefin than I am with Wardley maps. So for those of you, I'm just going to type that just in case anybody hasn't come across it. How do I get to everyone? All panelists and attendees, right? I'm just gonna type that. Cause it's not spelled like it's pronounced. You can go have a look at that.
It's a framework that's related. It's a framework for making sense of different situations, depending on how predictable and unpredictable they are, has a huge amount in common with Wardley maps. Dave Snowden who created the Cynefin framework, Simon Wardley hang out an awful lot. I have the privilege to hang out with them occasionally on a Friday.
It's really fun. I wanna share something from the Cynefin, the world of Cynefin. So let me just share this with you. It's only a couple of slides I've got for this talk. Where's it gone? There we are. Okay. Don't know if you can see that, right. So this is a fitness landscape, this fitness landscape, and this concept comes from the complexity thinkers, the Cynefin world and particularly Dave Snowden's company, Cognitive Edge creates these. So what they do is they send out a little survey and the survey has a prompt that is either positive or negative.
So it might be imagine that your best friend is taking a job with your organization. What story do you tell them? Or it might be tell us a story about your life in your country, or tell us a story about trying to bank at the Post Office, or banking at the Post Office. And then you can, they have these little triads, so here's one of the triads and they ask, okay, in your story, it's your, in your story was handling shaped by emotions, insight or politics, right?
And they asked you to move a counter to signify where your story sits in that little triad. And you can see this triad. You've got these emotions there, the emotions have fewer dots there than there are in the others. Okay. So you can start seeing these patterns emerge. And when you've got enough of these triads and one of them, they've got kind of six of these triangles.
So that's 18 different taxes. You can come up with, you can start seeing patterns. And so they create these fitness landscapes. So where you get a little tight cluster of dots, that little tight cluster of dots is a bunch of people all telling the similar stories. If you decide that one of the directions you wanna move in is better.
You wanna move that little cluster of dots. So they're telling better stories, right? It's really hard to move a little cluster of dots if there's no other people telling stories near them. You can see these. This is like a, it's like a landscape. It's three dimensional and there's energy that's needed to move to a different place.
So if you've got another group of people telling similar stories, but a bit different, a bit better, it's what they call an adjacent possible. So you can move people from where their little settlement sits. They're over to this other place. That's a bit better if there's nothing around them.
If they're just surrounded by mountain ranges, it's really hard to get them to move. And it's much easier to get them to move around a mountain than over a mountain. Okay. So you can think of these energy gradients that you need to move people. That's inertia. That's these little dotted lines, right?
That's what prevents people from moving. And I think the fact that they are represented by little dotted lines is misleading. So I want, I've what I've done. I've redrawn the Wardley map with a little bit of a template that you're welcome to think about. And it is a devil's advocate position, and I hope it's thought provoking.
But I redrew the map, the Wardley map, and this is what it looks like. So between each thing you've got these dragons, these mountain ranges, you've got a yawning chasm of despair, right? This is what happens every time. I have a discussion with somebody about their choice of architecture in technology space or their choice of technology.
And we have a really great discussion and it just disappears into the abyss and we never ever see it again. I think getting past this inertia is the quest. This is the quest. Okay. I'll put this out on the slack channel. I'll tweet it. My tweets are protected, but please just ask to follow me.
Unless I see things on your Twitter I don't like, I will let you follow me. I'll put this on Twitter as well. So I'm gonna stop sharing for a moment. Okay. How to map inertia a little bit better? There's one thing which I think does, and Cognitive Edge and people complexity do with Cynefin that we don't tend to do with Wardley mapping, but we could. In Cynefin, they say the data precedes the framework.
We tend to use Wardley mapping more like a categorization tool. So we'll draw the map and then put the stuff in it. I, what I suggest trying is put all your bits on the map and make them relative to each other. So how stable, how mature are they? How visible are they? And then after that work out where the boundaries are.
And your boundaries will be constraints. They will be constraints that prevent them from moving to the right. Okay. When you start looking at those constraints after you've already got the stuff, I think you are, you're more likely to see really interesting constraints and you're more likely to go, oh wow.
There's a thing there that we never ever talk about. So being able to map those constraints, I think that will make it less misleading. I think you'll end up with boundaries in better places. I think you'll discover weird things about the boundaries. They won't just be straight lines. They will be twisted and that will be okay.
You'll find that boundaries, which are completely unnecessary. I think that will make the map less misleading. There's one other thing that I wanted to talk about inertia, and I think I've got a few minutes. So you can probably hear I care passionately about climate change.
One of the things that Simon talked about, it was when I was looking at technology and how it was moving and how industrial revolutions happened. I was asked by one conference talk about industrial revolutions. Simon put me onto this awesome book by Carlota Perez, Technological Revolutions and Financial Capital.
And it's all about, it says above dynamics and bubbles of golden ages. So it's about revolutions and how they happen. And one of the things that she talks about is this concept Kondratiev wave. So I'm just gonna share again really quickly show you a picture of a Kondratiev wave.
Okay. Oh, sorry. It's gone all the way. There we are. So this is a Kondratiev wave. This up, down, up down, and you can see she reckons it's been responsible for every single one of these. This is on from Wikipedia, but it matches what Carlota Perez says. There've been all these waves before and they go through this prosperity, recession, depression, improvement cycle.
So what usually happens is you get, you get a bunch of people come in. There's some other ways as well, but I particularly like the Kondratiev waves. You get a bunch of investors come in and there's some enabling technology. So the first one was, they have basic steam engine. So we're able to automate looms and weaving.
So this great weaving thing starts taking off. And then you need coal to power the steam engine until you make canals and you have horses dragging things along canals. So a bunch of other tech rises up at the same time. And people who are investing in this, they're seeing their money grow. It's so super exciting.
It's really good. Your money is growing, your money is growing and then it tops out at the top of the market and your market's capped. And nowadays it's your global market that becomes capped. And we are seeing the spread of technology get quicker and quicker. So I think these waves are happening more and more quickly as well.
And now the investors they're looking at this stuff and they're going well, hold on. The stuff I was putting the money into, it's not making as much money anymore. How do I make my money make money again? Cause it's all just still, and it's not really moving. And so there's a recession that's starting to happen cause their money isn't making money. As it goes down, they start looking at, can we fund some more risky stuff? Can we put the money into things that we would never have touched before, cause they weren't guaranteed to provide any kind of benefit to us, but now it's worth doing now it's worth putting money into that stuff.
And so you see another bunch of enabling technology emerges from that new investment: steel, railways, et cetera. And off we go again, and the information technology boom was the last one. We're due to enter into another one of these revolutions. And I thought to myself okay, everybody talks about this.
And some people say it's a myth. I'm just gonna skip past those. How would we know if we were entering one of those revolutions? I took these before. Like well before COVID I took these last year. Okay. So this is Google. This is Alphabet. So you can see it was climbing. And then for the last two years, it's got this little up down cycle.
The market has a little cycle of its own, right? This was Facebook, similar climbing and then static. Apple climbing static. And this was in 2019. It's only gone down cause of COVID so climbing static. Okay. Uber wasn't doing very well at all. Lyft. Not doing so well at all. Even though they are a little bit more ethical than Uber, even Spotify, the technology darling, not doing so well.
Microsoft were doing well. Good on Microsoft. They were doing well. They were the only ones I could find that were doing well. If we are entering into this next phase we should start seeing the next enabling technology start coming up and I hope, think, dream, wish. Please let it be that it's green tech. We're seeing battery stuff coming on the market. I saw Tesla had their battery day. They had a really powerful message for the suppliers cause they make batteries, but they are also supplied with batteries.
Their message to their suppliers was we can't fund our own demand. We can't meet our own demand with our own batteries. You will always have a place with us. The world has so much demand for batteries right now to store all of the renewable, more fluctuating power sources. Those power sources themselves, all the ways and means of get there's so many, they found, things where you can put a rusty piece of metal in water.
And as long as it's thin enough, it will generate electricity, all these new things that they're finding right now around how to generate power in renewable, non-polluting ways. New ways of breaking down plastic, new ways of recycling, new substances that we can use instead of plastic that break down more nicely.
There's so much tech coming on the market. And I think that one of the things we can do is to help connect people to that Genesis, that custom built stuff that's coming in right now and help get it into stable products as quickly as possible because that will make the world a better place and then we'll be doing good.
That's it? That's all I got. Thank you.
Gen Ashley: Thank you, Liz. And yeah, Matthew and Andra, hopefully you come back on video. We have, actually, I have a question for you. So what's your, sorry. What's your favorite and least favorite characteristics of human beings?
Liz Keogh: What's my favorite? So my favorite characteristics of human beings is their ability to move forward in uncertainty. The fact that we have these heuristics that let us make shortcuts through the masses of data, we can never hope to understand. And so we can move forward in uncertainty which is astonishing. And my least favorite things about human beings is the fact that we shortcut the data, make all these heuristics and we tend to move forward in uncertainty and then we get it wrong.
It's simultaneously the thing which is magical and also just usually trips us. Okay.
Gen Ashley: Before questions from Andra and Matthew, I'll just take this first question from one of the attendees. What is your relationship between SenseMaker and the other diagram with green, blue, red, and arrows. I think you showed some diagram.
Liz Keogh: Really? Yeah. The, basically those patterns, they can measure the distance from each of the corners of the triad, and then that's an axis. So that's one of the axes. So there's one, two, and then the depth is a third axis and they play with them and see if they can make patterns emerge.
And you can sometimes see, all the people telling positive stories have one pattern, all the people telling negative stories have another pattern. So can we move those negative stories in a positive direction?
Yeah, there's a, if you look for Cognitive Edge SenseMaker, you'll find there's demos that they've got. They've got more information about it on their site. I do not work for Cognitive Edge. They get a lot of free advertising from me as it is, go do the homework.
Matthew Adams: Do they have tooling for it? Sorry. Do they have tooling?
Liz Keogh: Yes. There's tooling for it. Yeah. Yeah. So they, one of the things they're most proud about is they've got it working on kind of iPads so they can put iPads in the street, on a building or they can give iPads to communities and they get thousands on thousands of these stories, large populations.
So it's a way of getting, when I talk to Andra about, are you hearing any stories coming in, in that custom Genesis space? It's way of seeing those weak signals with mass. So they call it mass sense making. I'm blown away by it. I'm absolutely blown away by it. Yeah, I've had, I'm going straight away afterwards to go and find out about it.
Matthew Adams: My main takeaway. Thank you so much for that.
Gen Ashley: Matt. You're Andra, do you have a question for Liz? I'm thinking about lot of things. Liz gave, before that maybe we can pull up this other question. So someone's just thanking you for the talk Liz and his question is unnecessarily. Do Wardley maps actually give more choices to someone with few choices or just help to better understand the few choices that they have?
Liz Keogh: Yeah. Wardley maps don't actually do anything on their own. That's the problem. It's people who do it. I, while I was putting this all together, I was reflecting on the fact that I'm British and historically our relationship towards maps has been to try and work out how much of it we can own.
And that's not a really very good, that's accruing choices for ourselves at the expense of everybody that our Imperial mindset has taken over. So I apologize on behalf of every, I'm British, I'm sorry about that. I think that it's a tool for letting you see where your choices are and if you are ethically minded and sound, you will do good with them.
But do reflect. I think we all have, I talked about these heuristics, these cognitive biases we have. There's a thing called the Harvard implicit bias, Harvard implicit bias test, which helps you measure biases in your brain. And I found that I have no racism, but I do. I had ageism. I thought that all old people were sick.
And so the idea of giving old people opportunities in life never really occurred to me cause they're well, they're sick and they're gonna die. Why would I need to do that? I had to deliberately go out and find older role models to counter what I have found in my brain. So if you want to ensure that you are offering, really countering those biases that will prevent you from using maps in the right way, because you are biased.
Go seek out role models, go watch talks by people who represent things that are not it. I think that we, I've started doing that and I found it has helped a lot. So I'm ashamed of the biases in my head. I don't want them, I didn't put them there. I don't, I'm a free will skeptic. So I think that it's nature and nurture. We're either born with stuff in our heads or we develop it through our lives. It's not our fault, but if we do get the opportunity, in as much as free will is a useful illusion, it's on us to go do something about it. That's a
Gen Ashley: very good point. Especially, these times when we're talking about diversity, equity and inclusion.
Yeah. So I always say, it's a matter of educating people, educating ourselves and other people as well. That's how we get there. So Matthew or Andra. Who's gonna ask the next question for Liz?
Liz Keogh: Go ahead, Matthew.
Matthew Adams: So I want to just loop back a little bit and talk about the inertia. Ask you a question about the inertia. Cause I think you are absolutely spot on with that. The, and I've been, I've obviously I love your redrawing of the map, right? Clearly that's, it's right in my sweet spot for things. I love maps, all kinds of maps, especially that kind of map.
The, and I'm surprised my partner who loves maps even more than me, didn't detect that you brought up a map like that. But how the in a boringly business presentation context, how do you think we get the same power of expression as you got with that map with a standard template? Because the big black box doesn't, the big black bar represents a barrier rather than the jelly we are wading through. So how do you think we might visualize that jelly?
Liz Keogh: So one of the things that I've been looking at is constraints mapping. So this is again comes from Dave Snowden's world. So looking at what constraints are in place.
So they talk about two types of constraints, context-free constraints, which governing and fixed constraints. So fixed constraints is like you put a plug in the wall, there's only one way to do it. You can't get it wrong, cause okay. Governing constraints are ones which apply regardless of context.
So they are rules. They are always there. They, do not go across a red light. It's not. But a contextual constraint, an enabling constraint has some permeability. It has some ways of getting through it. So if I'm finding there is chaos with no constraints and everybody's going, oh, you can look at it and go, okay, what constraints could we put in place to enable better stuff to happen?
Cause fire burns until it meets constraints. It's what causes chaos. So putting constraints in place can sometimes help bring the chaos into place. Simultaneously you can look at, are we having problems moving because the constraints are too strict? Because there is no permeability.
Can we get an escape hatch through them? So I work in technology and I ask questions like, look, what would it take to not have to go through the CAB process before release. If we managed to get six months of monthly bug-free releases with no significant bugs, no show stoppers, would you let us start releasing more frequently without going through CAB? Can we do some minor changes if we've got rollback and we can roll back.
I was talking about this to somebody and I said, have you got good rollback? Because then if you do make a mistake, you roll back. It's not a big deal. They're like that's not, that underpins the whole of, and they don't see it as a safety net. They see it as an acknowledgement that you made an error.
So there's a cultural change that needs to shift. And those, the inertia around culture is just massive. You can start looking at where the culture is shifting or where it's stable and where there's no way it's a bank you're never gonna check, and you can start seeing those paths through the mountain ranges.
And if you see somebody's managed to make a path through the mountain range, that's what we call disposition. So that means that other similar stories are likely to land and not be rejected, cause somebody's already managed to get there. That there is a path. So you send other people along the same path and see if you can amplify the working probe in Cynefin.
Yeah, I've definitely done a lot of constraint mapping with people. I've definitely worked to make constraints permeable. I've helped people recognize where their constraints are and some of them sometimes they're self-imposed cause you know, our heads. So that's one of the things I would do.
Gen Ashley: Okay. I think we can wrap up the session with the last question maybe from Andra.
Andra Sonea: Yeah, I'm fascinated about all the ideas that Liz brought in. I was just wondering how can I hook in the sense making storytelling or structure because even if I've read about this framework, I was not aware that this is happening. It was not for me in any way, shape or form a way of collecting stories of which we try to make sense. Or...
Liz Keogh: So the poorman version is to go talk to people, go on the ground, go listen to what people are saying. They've got some, so Cognitive Edge, have some other methods, some of which are freely available, as long as you're not trying to sell them like a consultant, which what I do, things like narrative circles.
So there's ways of going around and collecting these stories. And the important thing is you don't act as a middle person. You don't act as an intermediary. You introduce the decision makers with the stories being told, always. So as much as possible, you keep that facilitative mindset. Somebody in the middle.
And just get the decision makers to see the stories and ideally get the people who are telling the stories to contribute to those decisions. Okay. I'm fairly new to this, so I'm hoping I'm getting this right. But check with Dave and some other people, cause I'm, everybody keeps telling me I'm an expert in this and I don't feel like one.
Andra Sonea: Yeah. My way, as I said, was linking to the Twitter API, putting lots of hashtags that I thought are relevant, extracting tweets, and then basically creating that type of clusters, with algorithms, what people talk about, how do they cluster and so on. So I was not aware that there is a more evolved form of that. So I would happily not rediscover the wheel. Yeah.
Gen Ashley: Great. Yeah. Okay thanks. Everyone. Thanks Matthew. Thanks Andra. Thanks Liz for joining us in this session. And obviously this is our fourth year and people come back in every year and speakers like yourselves. You are here and present, and thank you very much for that.
So folks who are watching, we're gonna move on to the networking. I think that's an hour networking after this, so we'll see you there. I'm sure our speakers are gonna be there as well mingling, well virtually with all of you. So see you at the networking session and see you back at 12 noon UK time for the next set of talks.