Podcast Ep. 69: Jorgen Hesselberg on Data-Driven Continuous Improvement

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Episode Description:

In today’s episode of the Agile Coaches’ Corner podcast, Dan Neumann is joined by Jorgen Hesselberg. Jorgen is the author of the new book, “Unlocking Agility: An Insider’s Guide to Agile Enterprise Transformation,” as well as the co-founder of Comparative Agility — a leading agile assessment and continuous improvement program.

This episode will be focused on data-driven continuous improvement. Jorgen shares the main reasons to use data to drive continuous improvement, highlights ways to gather data (and why these methods are used), and discusses important pieces to keep in mind when implementing changes to your team and organization through the data you collect.

Jorgen has a lot to say about this topic as a co-founder of a leading agile assessment and continuous improvement program, so you don’t want to miss his insights and key takeaways.

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Key Takeaways:

  • Why use data for continuous improvement?
    • Data can help guide you and your teams to ask better questions, and it shines a light where there otherwise would be darkness
    • Helps you reflect on what you’re doing and what you can do better; data helps guide these conversations
    • Optimizes workflow by making the feedback loop faster so you can take action more quickly and therefore see results faster
    • As a change leader, data can help you find out where you can be of most use to help your teams
  • What are some ways to gather data for continuous improvement? And why are these methods used?
    • Objective data (defects in production, trends, etc.)
    • Surveys, despite being subjective, can also be very useful because they can hit some important patterns of ways of working (i.e. psychological safety was discovered through a survey), and they highlight other points that wouldn’t naturally come up in conversations because they create anonymity and give everyone an equal voice
    • Structured interviews
    • Gathering data — whether it’s through structured interviews, subjective data or collecting data electronically — helps shorten feedback loops
  • What is important to keep in mind when using data for continuous improvement?
    • Subjective, objective and quantitative data are all great — as long as the data helps you and your team ask better questions, that is the main goal
    • As a coach or change leader it is important to ask meaningful questions that highlight the issues and challenges your teams are facing and to give them a voice
    • Don’t implement changes that you have gathered from the data all at once; otherwise, you and your teams will become overwhelmed and end up making no changes (i.e. because you are diluting the focus and creating confusion; people don’t have time to adjust too many different things at once)
    • An important facet to making change based off data is to change at a rate where you can see it ripple through the organization
    • Combine subjective data with objective data
    • Measure technical debt simply by asking your developers
    • Listen to data early on and refresh it periodically to stay ahead of the curve
    • Don’t continually ask your developers how they’re doing—they’ll get annoyed
    • Understand what “normal” benchmarks are for your niche
    • Data isn’t going to give you answers, but it is going to help you ask better questions
    • Use data for information, not evaluation



Mentioned in this Episode


Jorgen Hesselberg’s Book Pick:

Transcript [This transcript is auto-generated and not completely accurate in its depiction of the English language or rules of grammar]

Intro [00:03]: Welcome to Agile Coaches’ Corner by AgileThought, the podcast for practitioners and leaders seeking advice to refine the way they work and pave the path to better outcomes. Now here’s your host, coach and agile expert, Dan Neumann.

Dan Neumann [00:16]: Welcome to the Agile Coaches’ Corner. I’m Dan Neumann and I’m excited today to be joined by Jorgen Hesselberg. He is the author of the book, Unlock Agility, an Insider’s Guide to Enterprise Agile Transformation and the cofounder of comparative agility, which is a leading agile assessment and continuous improvement program. Thanks for joining you today, Jorgen.

Jorgen Hasselberg [00:36]: Thank you so much for having me, Dan. Really appreciate it.

Dan Neumann [00:38]: Yeah, we had the pleasure of working together in the past when I was, uh, an independent consultant and it’s, I’ve really enjoyed kind of tracking and following your journey of agility through lots of big companies as well as now as an author and the cofounder of comparative agility, really adding some value to the industry.

Jorgen Hasselberg [00:57]: Oh, thanks for saying that. And man, I’ve been following you too. It’s like, uh, the alumni club from, from, from, from the Navteq. It’s been pretty amazing. If you think about all the great people that came out of that company, it’s a, it’s really an honor to have been part of that, uh, that journey. So yeah, thank you for saying that, man. It means a lot.

Dan Neumann [01:15]: I met some wonderful people and I’ll have to stock some more of them for podcasts. Now that I think about it.

Jorgen Hasselberg [01:20]: Yeah, it’s really amazing. If you look at where people have gone since Navteq, you know, which, which really was kind of a unique journey. Uh, you’ve seen people now become leaders of other companies. You’ve seen them become leaders in their own space. Uh, and I think all of it is, is because that particular transformation was, it was really, you know, that’s where a lot with my book was based on too, it kind of created something that was bigger than the sum of its parts. Like all of us, I think, grew, uh, as being part of that transformation. And ultimately that’s what we’re trying to do with these other companies as well. It’s the power agile transformation when it’s done right.

Dan Neumann [01:56]: That’s fantastic. And our focus today is going to be on data-driven, continuous improvement. The Agile Manifesto has, you know, one of the last principles, not in importance. It just happens to be at the bottom of the list and it says at regular intervals, the team reflects on how to become more effective and then tunes and adjusts its behavior accordingly. So what I love it says, okay, this is, this is the what you have to do, all right, or what we are doing to find more success and then leaves the how up to the practitioner, you know, the different frameworks you might or might not be following. Um, and so let’s talk about using data for continuous improvement.

Jorgen Hasselberg [02:32]: Yeah. I think that’s, that’s super important. And, and honestly, I, I’m not sure if we’ve done a great job of that. I think very often we sort of go with gut feel and we just kind of do what feels right. Um, but I do think data can be really, really important for us. And, uh, and I think it’s data in all kind of flavors. It could be subjective data, it could be objective data. It can be data that’s qualitative, something that’s quantitative. I, I’m not sure if it matters so much. I think most important thing is that the data helps us ask better questions because they don’t think the data in itself is going to give you the answers. Especially not in a complex environment like, like a team or an organization. But I think having data that can help guide us, it can help us ask questions and sort of shine a light where there otherwise would it be darkness. I think that can be extremely important. So I’m a big proponent of that and I’m really glad that uh, you know, frameworks like Scrum would put things like retrospectives into their framework, sort of saying, Hey, you know, at least reflect on how you’re doing now and at least look at some of the processes and things you’ve done so far and see what you can do to do better and no better way to do this than to have some data to guide those conversations.

Dan Neumann [03:44]: Yes. And you know, when you talk about subjective data, I think of exercises like the four L’s liked, learned, lacked and longed for, um, and there’s, there’s some other versions that also call themselves four ELLs, but that’s the one for me that comes to mind. The other ones aren’t wrong. Um, and then something like a good, a bad, a do different, but they’re really a feeling based or intuition based types of approaches. Um, they’re valid, they just don’t lack or I’m sorry, they just don’t contain quantitative or objective measures of things like defect counts, system outages, uh, those types of data.

Jorgen Hasselberg [04:21]: Yeah, that’s right. And then, you know, the, the objective data, you know, which is the things you just mentioned, you know, defects in production, trends like that or, or you know, you can look at the MTTR, you know, meantime between, you know, recovery, you can look at fluid efficiency, those kinds of metrics. Those are wonderful. I think that can be really useful. And they’re automated so it doesn’t take a lot of time for teams to collect them. So I think those are great. But I also think that, you know, surveys, which, you know, they can be very subjective, but I think surveys, especially if they’re statistically validated and made by domain experts who actually know what they’re talking about, I think they can be really useful too because they can kinda hit some, some really important patterns or, or, or sort of ways of working that you may not have thought about before. I mean, good example is, um, psychological safety. I mean, there’s been a lot of talk about that lately. Right? And that was dr Amy Edmondson, who, uh, who, who did that research back in the 90s. Uh, and, and the way she was able to define that was, was through a survey and she has a survey that’s, uh, that she would have asked her, uh, recipients to, to answer a couple of questions. And it went down to things like, you know, do you feel, do you feel safe saying no to, to your team member? And I’ll do you feel safe, uh, that if you, if you make a mistake, you won’t be used against you. Like questions like that. And, and you know, the answers to those questions can be really revealing cause it kind of shows you all right. What kind of team are we here? Are we able to be comfortable with each other? I mean I’ll be comfortable speaking up when something isn’t making sense in all these kinds of things are things that may not naturally come up in conversation. And I think uh, surveys can be really useful in that context.

Dan Neumann [06:05]: Yeah. You, uh, before I clicked record, we were talking about structured interviews, but I wanted to touch on this the survey piece. Cause you could imagine a company that’s gone through a leadership change, maybe it’s even been a couple of years and if the previous regime was one that was not promoting psychological safety and the new one is advocating for it. Well people have long memories and if you’ve been reprimanded or it hasn’t been safe to say no or point out the emperor has no clothes to use that analogy and the new leaders saying, well trust me, it’s okay. It takes a while for that to permeate the culture and to actually become part of the way people think and behave. And you can ask people, do you feel safe saying no? And they’ll probably say yes, but then you can have anonymous surveys, you can look at the, what the data says about it and um, go across the different levels of the hierarchy. You can go broadly across the different verticals that might roll up to this new leadership team. And to get a really nice perspective for, for uh, uh, for low effort relative to interviewing people.

Jorgen Hasselberg [07:17]: I, I think you’re totally right. And the thing that I love about surveys in this sense is that they, they are anonymous in the sense that you, you can feel completely safe saying, you know, how you feel. And what I think could be really revealing here is that it will show how people feel differently about the same thing in the same cohort. Um, and I, I did a, a leadership workshop, this is not more than about six months ago, and I did a psychological safety survey with, with 25 sort of hardened leaders from a energy company. And, uh, these were all VPs and above, you know, there was a little bit of a mushy smell going on and a little bit of a sort of hardcore executive speak and, and you know, we were talking about the science behind high performing teams and we understand that, yeah, it makes sense if things are not working out, it’s important to speak up and all that. And they said, yeah, we’re high-performing. We lead to speak up. And I said, we don’t, why don’t we, why don’t we take this survey just completely anonymous. We took the survey and then what we found is that they did actually score it relatively high. Uh, from an average perspective, their average score was relatively high, but then we looked at the level of coherence inside the data set. So that’s essentially the, the degree to which these team members were agreeing with each other. And there were large differences there. There were several outliers. So imagine out of a data set of about 25 people they have at least four or five people here who did not feel safe at all. Whereas maybe the majority did. There was quite a few people who did not at all. And then there was many people who kind of felt in the middle. And, and what was interesting about that is that we say, well you know, as a team, you know, we, we can kind of talk about, cause that’s a block and say, yeah we’re, we’re high-performing and we feel safe. But then when you really look into the data and you look at the nuance inside of that dataset, there’s a good 10 to 20% of those people who don’t feel safe at all. And they’re not the ones who speak up in the interviews. But now it’s right there in front of you and you look at that data and then you say, Whoa, this is not what we thought. Now what are we going to do? And I think that’s the beauty of data like this. It brings things out to the forefront. It’s just like, I think part of the reason I think Scrum and agile in general is so powerful is that it doesn’t tell you how to solve a particular problem, but it brings it up into the surface and says, you know what I am going to, and I think it’s Schwaber who says I’m going to put a mirror towards the organization and so that you can actually see the truth and you might not like the truth, but at least it’s there now and now you can start dealing with it and I think that’s what this data can help you do.

Dan Neumann [09:56]: Yeah, and I think the anonymity, like you said, creates an opportunity for that to happen in a way that can’t happen in a group. I know as a facilitator sometimes, you know, you try to make sure that all the voices are heard through maybe activities like silent writing where people start the activity by writing on a sticky note. So we don’t get anchored on a thing, but people still have to feel safe to put the thing down and know that at some point it’s not terribly hard to figure out where it came from. And um, people who are either feeling unsafe or simply are, um, not as expressive for whatever reason, uh, still aren’t on a level playing field with the loudest, fastest first talker that is in that room.

Jorgen Hasselberg [10:38]: Exactly. I think you’re right and there is a tendency for some people to dominate. We’ve all seen that. Uh, so, so I think, um, surveys are a really, it’s a really democratic, a very, very nice way to get everyone a voice quickly. Uh, and then of course the secret sauce is then in, in the action and it’s about doing something with those results. I, I don’t, I think we’ve all been through pulse surveys and you know, those kinds of surveys that HR sometimes do for us and send them out and you spend, you know, half an hour answering it and then nothing happens. And then it’s the most frustrating experience. I think a lot of people when they think of surveys, they have a very negative connotation because they’ve gone through that exercise where they actually took some time and try to answer these surveys in an honest manner and then nothing happens. So it feels like a waste, uh, that’s of course on us as coaches and has change leaders to, to look at the results and actually do something about it. Uh, so, so I think, uh, you know, just like the agile manifesto, we’ll say individuals and interactions over processes and tools. This is a tool that gets you going, but it’s not going to solve your problem. You as a coach and as a change leader is still need to do the hard work.

Dan Neumann [11:50]: You’ve got the collecting the data is the first part of it. And then figuring out, interpreting it and coming up with action plannings and, and you had a nice clean way of, of saying that, that that resonated well. So maybe I’ll, I’ll let you say it.

Jorgen Hasselberg [12:04]: Yeah, no and I think it’s very legitimate and it goes down to this, I mean any type of tool or survey or whatever, it’s not going to give you the answers, but it is going to help you ask better questions. And as, as coaches, I think that’s ultimately what we’re looking for. We’re looking to ask those questions that are meaningful that that sort of highlights those, those issues that the teams are struggling with and you know, give those people the voice that they usually don’t have that. So yeah, that, that, that’s what I think is the, is really great about these surveys now. Just like any other change effort, you have to be careful not to do all the things at once. You know, it’s just, it’s like WIP limits. They, they matter for change efforts too. So part of our job as changed leaders is to look at all of these concerns and say, all right, here’s across the board and across the teams I see a lot of different concerns in certain areas. Where do we focus first? What, what, what have you, what have you do first and how can we make a meaningful difference on this area? And then once that’s been tackled and once we’ve, we’ve made some progress there, then let’s move on. And that’s, I’ll do all that once cause I think it’s easier for us to get, uh, you know, seduced by all the data and try to do everything at once. And then of course we end up doing nothing. Then we’re back again to square one.

Dan Neumann [13:16]: Yeah. You, you dilute the focus, you create confusion. Uh, people don’t have time to address too many different things at once. And in the spirit of complex adaptive systems, you may believe that a change is going to get a particular result, but by the time it bakes in, you may either get or not get that result and it may have an unintended consequence. So change at a rate where you can see the effects of it ripple through I think is a really important facet to organizational agility as well.

Jorgen Hasselberg [13:47]: Totally agree. And, and always kinda combined it, you know, combined subjective data with objective data. You know, so let’s say you’re looking at a, a challenge with a team. The team is indicating they’re not doing enough. Um, you know, automated testing for instance, they’re saying too, too many manual tests. You look at this and you say, okay, what’s the impact of that? The team will talk to you about it and then we’ll say, well, you’re not getting a lot of stories to done. We were having challenges with our velocity, you know, also should things, symptoms will essentially come to the fore. And then you talk about alright, well, what, what’s some of the impacts of that? Well, you know, we’re not delivering as much as we could. We were getting frustrated. We know we could do better. Uh, and then what are we gonna do about it? Well, how about maybe an, uh, an automated test framework? Uh, how about we do some, some TDD or you know, maybe more automation. Uh, and then you start doing that. Then you said, okay, great, we’re changing our practices. Uh, well what’s the impact on that objectively in terms of lead time? Uh, what’s the impact on that in terms of quality, you know, and those things can be measured, uh, quite simply frankly. And then you can start to see correlations between how the teams feel about things and then how the objective results in the organization are materializing. I mean there’s, there’s a lot of great sort of predictive data that happens at the gut level. And I mean, teams, you know, one thing that I think is really great is it’s for instance, when it comes to, you know, technical debt, how do you measure technical debt? And I think there are tools to try to do that, but I think the best way to measure technical debt is literally to ask your developers, how do you feel about the level of technical debt right now? And I think you’ll get a really honest answer that I think is actually quite accurate because they know what they’re called basis looking like and and no tool is going to do a better job of, of, of measuring that. Than how they feel about that code base cause they know.

Dan Neumann [15:50]: Yeah. Those, those feelings. I think if I understand what you’re describing is uh, taking something that’s a gut feel and then trying to quantify it. So how do you feel about the level of tech debt? You can get a good, uh, well you can get an indicator. It may, it may be good. It feels like it’s probably good. And then what might be available to try to quantify that. So there are tools that we’ll do static code analysis for the complexity of your, your functions that you’re writing. It will, um, look at how much code has been copied and pasted around, you know, so you have duplication of code. And so I think those, those feelings can then point to, like you said, what’s the better question? Okay, we feel like tech debt’s getting out of control. There’s a good chance they’re right. And now how do we quantify that and measure an impact from addressing that particular challenge?

Jorgen Hasselberg [16:40]: Exactly. And then, and then look at the objective results of it. Because ultimately the reason, you know, yeah, we deal with tech debt because it’s the right thing to do, but also because it makes us work faster. We get more stuff done, or the quality of our product goes up. So, so these things, that’s the, that’s the end result of it. We also want to measure those, those outcomes. So, so look at looking at how we feel about these things, the practices, the patterns of our behaviors, looking at their objective data that can support and sometimes correlate with, with those behaviors. And then looking at the outcomes, uh, that, that’s sort of the Holy grail. And then when you see that there are certain, you know, the, the, there are, there are certain proven behaviors that we know lead to certain outcomes. You know, that, I think this is a, an often quoted statistic, but we know that if your employee engagement goes down, for instance, we know that’s a pretty reliable predictor of attrition. Like, you know, people are gonna lose, leave the company if, if their engagement is down. I mean that just happens. And it’s the same thing I think with, with things like technical debt, technical debt is at a pretty high level, you know, quality is going to go down. That just happens. So there’s many of these things we can do, we kind of be ahead of the curve and be proactive if we listen to that data early on.

Dan Neumann [17:54]: Yeah, listen to it early on. Refresh it periodically. I think that’s an important part to say and not, not so early. I know one of the cautionary tales there was an interest in engagement or developer happiness and there’s a JIRA plugin that can ask the developers, how happy are you today? I don’t, we don’t have to point fingers or, or you know, identify where that was used. But you know, one, one way to irritate your developers is to ask them how happy they are every day.

Jorgen Hasselberg [18:24]: You know, I know what that is called. I think it’s like Nico, Nico or something. You know actually, I saw on there as I talked to someone today and had a really, we had a good discussion about this very topic actually. And yeah, I was telling uh, that person exactly what you said that yeah, it was a great intention, but it became very annoying and as you kept asking, how are you doing? You’re like, Hey, how do you think I’m doing? You’ll keep knowing me with these answers or these questions all the time. Like what she talked about, was really cool. I’d never heard of this before. She, um, she, she talked about something called mood marbles. Have you heard of this before?

Dan Neumann [18:59]: I have never heard of moon marbles, but I’m going to Google it as soon as we’re done.

Jorgen Hasselberg [19:02]: It was so cool. So basically this is how it works. It was, you know, mostly for teams that are co-located. But the way it works is that, uh, no, I haven’t tried it myself, but this is what she explained it. She said that you had these marbles that, uh, I guess essentially indicates certain moods that you have. And then what, what happened is that when you go into the office, you would basically place your, your mood or your marble in, uh, in, in, in this jar. And then every, every morning people would come in and they could see how the team felt overall. So you can see a lot of marbles that were showing like really bad, you know, vibes. You could say, Oh wow, this is going to be a rough day.

Dan Neumann [19:40]: Oh, I’m sorry. I misheard when you first said, I heard moon like the sun and the moon, but mood.

Jorgen Hasselberg [19:49]: Yeah, that’s my bad. You should no, that’s all good. Mood marbles, mood.

Dan Neumann [19:53]: No, I totally get it. Yeah. Right. So it’s not people opt into it. It’s not, um, impeding your getting to work, which I think might have been one of the factors with like an automated plug in to the tool where you manage your backlog. It’s like get out of my way. And so this marble thing, it’s, it’s low tech, it’s not impeding work. Uh, and you still get some quantitative data about the qualitative aspects of how people feel.

Jorgen Hasselberg [20:19]: It’s just an indication and it is really sort of, yeah, like you say low tech. And I thought that was really cool. It’s a good way to sort of get to get, get to meaning without being super complicated.

Dan Neumann [20:32]: Yeah, no. Awesome. One of the questions I find myself getting asked, and it seems like it’s often, it may not be because I haven’t actually measured it and plotted it out, but when I’m at an organization they tend to ask questions like, well, but what, what’s normal for my area? What’s normal for a financial services company? Like a bank or a credit union? What’s normal for a pure software, you know, cloud hosted, um, you know, direct consumer type of service. What’s normal for my kind of niche. And I think that’s also an area where knowing what is normal, if I’m doing managed vendors and trying to be agile versus writing and deploying to the, the web, you know, multiple times a day type of, of agility.

Jorgen Hasselberg [21:20]: Yeah, I agree with you and I think that’s actually really, really important because I think one thing that we have understood is that agile or agility means different things to different people. And, and context really matters here. And it’s not fair, I don’t think. Because very often, you know, when we talk about agile companies who will be talking about the Spotify buys and the Googles and the Amazons, and they’re doing all these wonderful things, they’re releasing 300,000 times a second or whatever they’re doing. And you can imagine if you’re a utilities company or if you’re a financial services company with all sorts of strange, uh, you know, uh, constraints around, uh, regulation and things like that, it could be really hard for them and maybe even, you know, quite demoralizing to, to listen to those, those examples. That doesn’t mean that they can’t be more agile, but maybe not exactly the way Amazon does things. So I think it’s important to sort of be able to benchmark yourself to your peers. So, so that’s one of the things we do in comparative agility. We have a pretty robust, uh, benchmark now across 13 different industries. So if you are a utilities company or an energy company or you know, maybe a healthcare company, you can compare yourself to your peers in your industry and then you might find that, you know, even though you, you’re not quite where you want to be, you’re actually doing okay compared to, you know, your brothers and sisters in the same industry. And I think that can be a, you know, a comfortable way of looking at things instead of just always comparing yourself to, you know, to Amazon, which I think can be really intimidating for a lot of companies. I mean, if you think about it, I, I don’t want, you know, my kid is now, you know, my oldest son is 12 years old, plays soccer. If I had him comparing himself to Mesi all the time and it’d be very intimidating. He was just like, man, I’m not even close. But you know, he plays with other 12 year olds and, and in that context he’s pretty decent. And I think that’s fair with big companies also. I think it’s a, you know, different industries have different concerns and different constraints and I don’t think it’s fair to compare in inside, you know, apples to apples.

Dan Neumann [23:20]: Definitely and you might find that you know what you’re doing really well compared to your group and maybe there’s a story to tell there and there may be some areas where like, man, what we, we are, we’re laggards. And so maybe that’s a need to get out to a conference or reach out to your colleagues with whom you don’t directly compete and, and ask them, Hey, what, how did you guys solve this problem? I know some of some industries are more collegial with each other than, than, than certain other industries are. And some will actually help each other out. Um, you know, when they’re not going after the same, same base of users or customers.

Jorgen Hasselberg [23:55]: Yeah, you’re completely right and they’re probably dealing with the same concerns many of them. And it’s interesting how you could see certain industries are dealing with very common patterns and the one thing that I see a lot in financial services industries is that, you know, but one of the things that are actually doing quite well is, is technical practices that tends to be pretty robust. At least that’s what our data is showing us is that uh, you know, financial services tend to have a pretty decent technical base. Infrastructure seems to be doing pretty, pretty awesome. But then where they tend to struggle, at least what we are seeing overall is that a youth culture is a bit of a challenge. Um, some of it also has to do with management. Um, no, I don’t know exactly why. Uh, I think part of it could probably be some regulation. They could maybe be some, some sort of old school culture thing that sort of blends into what’s going on and maybe some top down management procedures of the past. But uh, but it’s interesting to see that certain industries tend to have certain patterns that keep repeating.

Dan Neumann [24:54]: Definitely, definitely do. And it’s interesting to try and dig in and figure out the why and like you said earlier, you know, the data isn’t going to give you answers, but it’s going to help you ask better questions.

Jorgen Hasselberg [25:06]: Yes. That, that is, that is extremely important. And I think one of those things that it’s, so, I think it’s really seductive to us as humans. We just want to have the answer. And it’s just not that simple. I mean, you know, weight loss, I think it’s a nice analogy in many ways to, to agile transformation because I think we all understand that, you know, to lose weight, it’s essentially a function of consuming less calories than the ones you burn. I mean, at the end of the day, that’s what it comes down to. It’s a simple formula. And I think, uh, we know this, I don’t think there’s any person on the planet who doesn’t get that part still weight loss. And that industry is huge, right? Everyone is struggling with weight loss, it seems like. And, and then the industry is huge where they have, you know, diets and drugs and, uh, I mean there’s all sorts of coaches. I mean, it’s, there’s a lot of money being made in helping people, uh, you know, tackle weight loss. And, and part of the reason for that, I think is that weight loss is a very complex problem is yes, it’s true that at the end of the day, if you really sort of go down to the very essence, yes, it’s about consuming less energy than you burn. But it’s also about the environment that you’re in. It’s about your psychology. It’s about your mental state. I mean, do you eat because you want to comfort yourself? Do you eat because you’re stress eating? Do you eat because you want to reward yourself? Um, you know, and what about the people around you? Does your family eat unhealthily? Do you have access to healthy foods? Um, what about, uh, your colleagues or, or people who push, uh, you know, unhealthy foods on you, you, all those kinds of things are playing into this. Uh, and, and those are things that isn’t there, isn’t so simple. And I think it’s the same thing with, with agile transformation. So if you think about the basics of what it means to be an agile organization and sort of optimize for flow, we’d understand that it’s about limiting WIP. We get that. We, we learned that from queuing theory. We know it’s about reducing cycle time. I mean, that’s no Hocus Pocus there. We know it’s about increasing resources where it’s appropriate and we understand it’s about not limiting, but at least I’m not re, no, not, not eliminating, but limiting, uh, you know, variability where it makes sense so that you have guard rails and so that you don’t have complete chaos around these areas. Even though we know those things, we still don’t do them really well. And a lot of that is because, well, there’s politics, there’s people, there’s financial concerns, there’s leaders who want a certain agenda. I mean there’s all these other things that get in the way of us doing, you know, the things that actually matter. So I think if we have data that can help us get closer to these things by listening to where the teams are saying there are challenges, I think that could be really, really useful.

Dan Neumann [27:54]: Definitely the, the data points you somewhere. And when you were talking about the, um, the different components of change, whether it’s weight loss or organizational agility, uh, Daniel Pink, I think it was the book Switch, um, had the, um, environmental component, the emotional component and the rational one. And a lot of what you’re describing is kind of the rational side. Hey, we know we should limit WIP. We know we should try to reduce cycle time. But at the same time, if you’ve got leaders who are not aligned on what’s best for the organization, they all have their own, um, their own motivations. And when those motivations clash with each other, uh, that’s when, um, some of the emotional side or like a threat to my positional authority, which could result in my personal income being affected or a relocation or what our promotion going away. And so making sure that people are aligned and there is psychological safety was one of those facets to, to move forward and really addressing the emotional side with the, the rational side and allowing that to move forward.

Jorgen Hasselberg [28:59]: Yeah, you’re exactly right. We have so many policies in our organizations that are, you know, unintentionally actually making it hard for people to do the right thing. You know, it’s not because we want to, you know, sabotage, but, but I think, uh, you know, it’s hard for people to sometimes, you know, do the things that actually is better for the organization overall as opposed to sub-optimizing for your own unit. Um, we were driven to do those things because of reward structures and politics for sure.

Dan Neumann [29:28]: The other piece that jumped out to me related to the weight loss analogy with org change is the cause and effect has a meaningful delay in it. So if I eat a cookie today or if I eat a pile of cookies today, that’s not weight loss immediately. I’m sorry. That’s an idea. We had God love it. Oh, that’d be amazing. Chocolate chip cookie weight loss. But if you want chocolate chip cookie a day, I guess then yeah. Um, but, but there’s a delay between the extra calorie intake and when it manifests itself in extra energy stored, which is usually called fat. And so it’s not a, Oh, I ate a cookie today. I see the weight tomorrow. It’s the behavior change over a long period of time. And it’s trends and it’s looking to see how those things manifest themselves. But it’s a long cause and effect loop. And I think that’s also true with org change. You can’t say, Oh, today we’re all, we’re psychological safety today. Right? Yeah. Okay. Good luck with that.

Jorgen Hasselberg [30:20]: No, I think you nailed it. This is what we talk about when we say feedback loops. You know, that’s the thing. If you don’t have a rapid feedback loop, then you have those delays. And then it’s hard sometimes to understand that, okay, we’re making this change now what’s the impact of that? And that might take, you know, months sometimes. Uh, and then that’s really hard for you to, Oh, that’s right. We, we reduced all that technical debt and now three months down the line, we can see that we’re delivering software much faster. Uh, sometimes you forget that the action we took two months ago actually cost that. Um, yeah, you’re completely right. That feedback loop, uh, it can definitely be our, our enemy if it’s not fast enough. And, um, and that’s of course part of what we try to do by optimizing for flow, to get that feedback loop much faster so that you can take an action and, and much quicker at least see the results of it.

Dan Neumann [31:12]: Wonderful. So, uh, data-driven continuous improvement. Yeah, definitely the ability to use, um, data, you know, whether it’s, um, structured interviews. I think we’d alluded to that, whether it’s the subjective data collecting, uh, data electronically through things like surveys and being able to, um, not just look at averages but outliers and, and really then using that as a continuous improvement, shortening those feedback loops I think is the theme, the theme of the podcast.

Jorgen Hasselberg [31:43]: Yeah, that’s exactly right. And I think very often we get kind of scared of data, uh, if we look at them as weapons. Uh, and, and I think that data very often can be misused. We shouldn’t forget that part of it is that when people use data as a weapon, as a way to, you know, say, well, Hey, you’re a bad team. You know, I looked at your survey and you’re saying you’re not delivering fast enough and Hey, that’s, that’s, that’s a bad answer. So you’re, you’re bad and, and we’re going to punish you for that. Uh, when that is the consequence, uh, well, first of all, you have no psychological safety, but, but second of all that, that certainly is not the intent of, of this data. The data here is to help you as a change leader. Find out where you can be the most useful and where it can be the most helpful. Then help the teams where they need to be helped rather than punishing teams for being honest. Uh, and I’ve seen examples of that many times where we’re team sometimes will be told by their managers like, Hey, you know, you’re going to get a survey now. Make sure that you answer everything awesome. you know.

Dan Neumann [32:45]: Oh, I’ve, I’ve received those surveys and instructions. Yes, I’ve, I’ve been on that side, not an AgileThought mind you of course disclaimer this, you know, not everything’s at AgileThought. Right. And so one of the phrases that comes to mind, and I wish I knew where I first heard it from, was using that for information, not evaluation. I think Susan Defabio at a, when we were at Navteq together, I know she used that phrase, I don’t know that she created it for sure. I’m sure it came from somewhere else, but you know how our, we’re not saying good team bad team were saying, Oh, that’s interesting. Yeah. Yes. Yeah.

Jorgen Hasselberg [33:19]: Right. And, and, and here, here’s a team that’s asking for help. Why don’t we, why don’t we help them instead of punishing them? Uh, and I think that that’s, that’s what it is. And, and the, you know, it’s the same thing as a, you know, I bet you, you know, if you think about weight loss or even exercise I bet you, you have a, a fitness app or something like that that you using. And that fitness app is not about bad Dan Neumann. It’s about here’s Dan Neumann right now and here’s where he’s going. And you know, maybe if the trend is not going in the right way, that Neumann could make some changes. He can say, you know what, maybe I shouldn’t have that extra chocolate chip cookie.

Dan Neumann [33:55]: Well, and are you, and I use multiple apps and they tell me different. So, um, I use a, um, Oh gosh, what’s it called? It’s called weight guru, which ties into my, um, internet enabled scale. So I can, um, get trending data for weight. And then I’m a big fan of Strava. And one of the things A, it allows people to encourage each other. They give little kudos, thumbs up. And my wife’s like, that’s so dumb. And I’m like, yeah, but I can go tell other people. But what it also, what it also gives me though is not just fitness but it has a measure of fatigue. And so I can look at not only am I getting the right amount of workouts in or miles run or things like that, but it goes, you might be overdoing it, looking at your trends, you’re moving into a boundary where you risk injury. And I think that’s, that analogy ties back to org transformation too. When you’re doing too much your risk, um, creating a lack of safety, creating confusion, mixed messages, you fatigue your organization as well. And I, I love that.

Jorgen Hasselberg [34:55]: I love that too. And it’s also about being predictive. I mean, you are now looking at a leading indicator, like you’re saying, Hey, I’m about to, and then, then that’s the same thing with, with it surveys. They’re very often they can be a leading indicator of something else. And when you see that certain things are going up and all we talked about engagement, we talked about technical debt, you know, we see indications that Hey, we’re going in a direction that we know leads to no good place, then we take corrective action before we get there. And I think that could be really useful.

Dan Neumann [35:22]: Yeah, for sure. You don’t want to injure the organization if you will, to carry that analogy on. Wonderful. So Jorgen, this has been a wonderful time. I’ve enjoyed it. And um, I’m curious what your reading or consume or what, what kind of is along your continuous learning journey right now?

Jorgen Hasselberg [35:42]: Oh, a great question. Oh, there’s actually, there’s two things I’m doing in terms of reading right now. One thing is I’m reading a lot of crime novels, I try to get out of it. I see. The only thing I’ve been reading is just like, you know, the, the, the nonfiction stuff, cause I’m kind of an agile nerd as you probably figured out. And, uh, one of the things I did for 2020, the new year’s resolution, I still do those, is that I was going to say I’m going to read something different than this agile stuff. So right now I’m reading a ton of crime novels. I’m also reading. Um, there’s a book I just picked up, which is really awesome. It’s called the Blueprint and it’s about how you can create, uh, communities. Um, told me, remember the name of the author is a, he’s a Yale professor and a, it goes back to sort of the, uh, the classic communities and how they were created, uh, over time and sort of some of the patterns. So it is really interesting. I really love that book. And, uh, and um, it’s, it’s just giving me a lot of great ideas. I highly recommend that, uh, can’t remember the author now, but you’ll probably will have it.

Dan Neumann [36:43]: We’ll put it in the show notes. That’ll be fine. Um, and I did a podcast episode shortly before the new year about resolution. So, um, actually Strava did some research on when people fall off their, their new year’s resolutions and it’s, ah, I forget exactly. It’s about January 17th though.

Jorgen Hasselberg [37:01]: So we made it.

Dan Neumann [37:02]: You made it. Yeah. So you can Pat yourself on the back. You’re, you’ve, you’ve almost tripled at this point. You know, the, the average time people stick to their new year’s resolution. So congratulations on that.

Jorgen Hasselberg [37:14]: Well that was easy because those choir novels are really fun. So yeah. Good. No.

Dan Neumann [37:18]: So my wife watches a lot of, um, murder investigation shows and I see, I view it as she’s just ruling out all the ways to get caught. So when she does off me, she’ll be locked with the only thing that could possibly work.

Jorgen Hasselberg [37:31]: You think that’s a, when not an if pretty sure.

Dan Neumann [37:34]: And we’ve spent enough time, I’m sure you understand why. So we have it on tape now. So this is my evidence trail. Well, you’re going to have thoroughly enjoyed our time together and learned something and look forward to, um, having another conversation in the future.

Jorgen Hasselberg [37:53]: Awesome. Thank you so much Dan. I really appreciate this.

Outro [37:58]: This has been the Agile Coaches’ Corner podcast brought to you by AgileThought. The views, opinions, and information expressed in this podcast are solely those of the hosts and the guests and do not necessarily represent those of AgileThought. Get the show notes and other helpful tips from this episode and other episodes at agilethought.com/podcast.

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