Product Diseases and Vision-Driven Development with Radhika Dutt

In this episode, Dave and Peter sit down with Radhika Dutt, author of "Radical Product Thinking: The New Mindset for Innovating Smarter," to explore why iteration-obsessed product development is failing organizations.

Radhika shares hard-learned lessons from her 25-year career across diverse industries and five acquisitions, introducing the concept of "product diseases" like hero syndrome, pivotitis, and obsessive sales disorder that plague modern product teams. She challenges conventional wisdom around OKRs and goal-setting, explaining why they often create an illusion of performance while masking real problems.

The conversation explores why goals, targets, and OKRs backfire and what actually works instead. Radhika introduces her tried-and-tested alternative: a framework for puzzle-setting and puzzle-solving called OHLs (Objectives, Hypotheses, and Learnings). This approach helps companies develop a mindset that equips teams to experiment, learn, and adapt in a disciplined way, ultimately delivering far better results than traditional goal-setting methods.

The discussion dives deep into crafting detailed, hypothesis-driven vision statements that actually help teams make decisions, rather than fluffy corporate speak that sounds inspiring but provides no guidance. Radhika explains how to balance vision debt against short-term survival needs using her three-question puzzle-solving framework.

Key Takeaways:

• The importance of writing good hypotheses and understanding customer pain points deeply before defining experiments and measurements
• Organizations need to get much closer to their target customers to truly understand their behaviors and pain points, enabling better vision statements and hypotheses that resonate
• Effective vision statements must enable decision-making; if you can't make yes/no decisions based on your vision, and understand the trade-offs between short-term survival and long-term vision, it's not valuable enough

Free Resource: Download the OHLs template and toolkit: https://www.radicalproduct.com/toolkit/#OHLToolkit

Chapters

• 0:00 Introduction to Radical Product Thinking
• 7:01 Common Product Diseases Explained
• 12:17 Crafting Effective Vision Statements
• 17:56 Vision as Decision-Making Framework
• 21:31 Beyond OKRs: Performance and Metrics
• 27:50 The Puzzle Solving Framework
• 35:49 Closing Thoughts and Key Takeaways

Transcript

Peter Maddison: 0:04 Welcome to Definitely Maybe Agile, the podcast where Peter Maddison and Dave Sharrock discuss the complexities of adopting new ways of working at scale. Hello everyone, I'm here again with Dave, and we've got a special guest, Radhika, who is going to be telling us all about wonderful things to do with products. So, Radhika, would you like to introduce yourself?

Radhika Dutt: 0:24 Sure! I'm Radhika Dutt. My background is that I started off as an engineer doing electrical engineering, and I was in the startup world and then worked in all sorts of industries. So over the last 25 years, I guess there's one consistent thing, which is I've never held two consecutive jobs in the same industry. So I've worked in telecom, broadcast media and entertainment, robotics, even wine, and in the maritime industry. It's everywhere, right? And I remember a recruiter even once said to me, "You should consider focusing." Highly overrated.

Peter Maddison: 0:59 I'll say yeah.

Dave Sharrock: 1:02 Experience life. I mean, I'm assuming that means you've solved the problem in one industry, you move to the next industry, solve the problems there, move to the next one.

Radhika Dutt: 1:10 Well, I have been part of five acquisitions, so hopefully that's a good thing.

Radhika Dutt: 1:18 So, five acquisitions - these were either small companies, sometimes larger ones in various spaces, and a lot of them were for the product that I built. So hopefully, yes, that is sort of what I always aspire to - getting enough of an understanding of a market so that we can build products that truly can address the needs of that market. My first book, by the way, was Radical Product Thinking, because all of this sounds great, but in reality it was packed with a lot of hard lessons, lots of mistakes. Along the way I caught a lot of what I would call now, in retrospect, product diseases, and I'm happy to share examples of all of these hard lessons and product diseases that I'd caught along the way. But Radical Product Thinking is the book that I wrote that talks about how do we build transformative products while avoiding those product diseases that are just so common. And I talk about it as product diseases because we really should talk openly about them because they're so ubiquitous. So I'll pause there. That's my background.

Peter Maddison: 2:20 Well sure, it's a fascinating background. The breadth is quite impressive. So when you talk about product diseases, do you want to give our audience an example of what sort of things you refer to?

Radhika Dutt: 2:31 Yeah, I'll start with the first startup that I had, which we started right out of our dorm rooms at MIT. You know, we had this grand vision that we are going to revolutionize wireless. That was our vision, right? And if you ask me 25 years later what that meant, I still don't know. We had caught the disease I call hero syndrome. Our focus was just on being big, on scaling. We measured success by the VC funding that we had raised and, you know, the thing is, like, this was 25 years ago but it all still sounds very familiar, doesn't it?

Dave Sharrock: 3:11 And what I'm intrigued about there is that you're talking about product, you're talking about success around a product, and yet the VC funding doesn't necessarily tie directly to that success, right? So there's an element of storytelling that comes with that, I assume as well.

Radhika Dutt: 3:26 Yeah, very much so. If anything, I actually think the VC business model sort of leads you astray very often, and there's a really interesting book about that that came out recently and I'll find the name later. But I think one of the lessons, also about product diseases, was from this VC model, where, you know, they want you to go big or go home, because the VC model is I want to invest in 10 companies. I just need one of those to be successful, the rest all - I want them to die out quickly because I don't want to invest more in middling companies, right. But that same model isn't necessarily good for an entrepreneur, and so I think a lot of these diseases come from also this idea that, oh, just keep pivoting till you find product market fit. And, by the way, that leads to the second disease I talk about. It's called pivotitis.

Dave Sharrock: 4:23 Yeah, and it's an interesting one because there's a lot of terminology. It's almost like the word salad that comes through where... So one of the - we spend a lot of time working with well-established, like large Fortune 100, Fortune 500 companies. They don't tend to have a need to pivot all the time, but they love the language because it allows them to say, "Well, I haven't thought this one through, but I'm going to go over here this time." And so you now have this continuous change of direction, when actually they should be, hopefully, in a pretty stable trajectory as they're exploring what they need to do. I don't know if you see difference. Is that a different disease, or?

Radhika Dutt: 4:59 No, that's a very, very relevant thing that I keep seeing as well, because I've seen pivotitis very often in larger companies too, being, I mean, just as prevalent. And the reason is that very often in larger companies, pivotitis can look like a pivot every sprint practically, because you're catering to whichever customer is screaming the loudest that particular sprint. Sometimes it's driven by, you know, even leadership saying, "Oh, you know, this is the latest thing, we really should be trying this other thing." So, yeah, pivotitis is really common in large companies too.

Dave Sharrock: 5:38 Now I think this is going to lead us to one of our core topics that we wanted to talk about, because when we're working with organizations that have this sort of like scattered, they jump from one thing to the next. One of the things that we'll want to do is go back to their vision statement and understand what their product vision is, because, in theory, that product vision should allow them to, you know, stay the course or focus on not necessarily the loudest voices, but something that's more in line with where they're trying to get to. But of course, this then leads to the what sort of vision does an organization have or product group have?

Radhika Dutt: 6:11 And you're so right. I think going back to the vision is such a critical thing because regardless of how much funding you have, large company or small, you have two to three pivots before you run out of money or momentum. So it doesn't mean that you never pivot, but you have to pivot with gravitas and for that you really need a detailed vision. So one of the things I see in companies is a vision statement is often broad and fluffy. So vision statement for Boeing goes like "to be the global enduring industrial champion in aerospace." And you know what does that mean? Really, do you measure global champion blah, blah of aerospace by revenues, by share buybacks and therefore returns, or like, how are you measuring, you know, being number one? There's Snapchat's vision, right, that says - what was it? - "contributing to human progress by empowering people to express themselves."

Dave Sharrock: 7:08 I mean...

Radhika Dutt: 7:09 I don't even know what that means, right? But we have these grand vision statements because we've learned that they have to be inspiring, that it has to be easy to remember. Yes, these are all easy to remember, but you know, this is like telling your construction crew "build me the biggest, baddest house," and they don't know what to build unless you give them a clear floor plan and you give them elevations, etc. And that is the type of vision statement that I talk about in Radical Product Thinking. It's a fill-in-the-blank statement, so that you're not focusing on the words but you're really focusing on answering some key, profound questions.

Peter Maddison: 7:50 Do you want to give an example there? Yeah, well, I think you're sort of touching on there. So there's the vision of like where do you want - what do you want to be when you grow up, sort of the direction of that. And then there's the strategy going to get there, like by what method are we going to be able to do that? And if we don't define the elements of that, you end up with this big fluffy statement that doesn't actually help you because it doesn't guide you about, do I go left or right? If I want to be the biggest, most impactful person in the aerospace market, then okay, how are you going to do that? Like, what does that mean? How will you know when you got there, what does success look like? And until you start to think through some of those problems and define them, it becomes very difficult to figure out where... how does this help?

Radhika Dutt: 8:30 Yeah, and that's such an interesting point, right? What I often find is that when there is such a lack of clarity in the vision, what we end up doing is we set goals and OKRs, almost like duct tape on foundational cracks. These goals and OKRs are supposed to align us and tell us what's the impact we actually want to have, but the reality is there's a ton of negative side effects of goals and OKRs, and what you really needed in the first place was a detailed vision statement that talked about what is the problem you're trying to solve, how you're going to solve that, et cetera. But we just put duct tape on that.

Dave Sharrock: 9:07 Yeah, and it's almost so many times, at least in our experience or my experiences, you end up finding vision statements that are hypothesizing where the revenue is going to come from. Our customers will love this, so we've got to give them this functionality, and I always kind of go back to things like design thinking and that whole understanding of what's the customer's pain points, problems, why are they going to ship, why are they going to use your product, and really understanding that and incorporating that into the vision statement, because that should be the kind of core of what you're trying to build, if you understand that customer problem.

Peter Maddison: 9:42 Yeah, I always love the ones where they pick sort of "we're going to make this amount of money in this marketplace that we've never been in before with this and we don't even have a product to sell in it and we don't know what the customers in that market want, but we're going to make this amount of money there."

Radhika Dutt: 10:00 Yeah, exactly, it's exactly it. You know, it's like showing the rendering of a house. "Go build me this" and you still don't know kind of what do I do? What do the insights look like?

Dave Sharrock: 10:07 Can you talk a little bit because there's a... there's a point where a vision needs room for us to explore what's being built and have room to kind of change direction a little bit as we learn more. And you're also talking about blueprints and plans which are quite definite and fixed and something that effectively, we now need to go and build, but there's little room for adjustment there. How do you address that sort of need for uncertainty or need for room for things to emerge along with something which is concrete that teams can get behind and build?

Radhika Dutt: 10:38 Yeah. So the answer to that is we have to write a detailed vision statement and treat it as a hypothesis. It's your best guess at a given time when you're building this, right? And as you execute on this vision, you have to look back on this, especially if it's not a mature market. You might discover new things and where you were right or where you were wrong in this vision statement, practically every month, maybe every quarter, maybe every six months, but you revisit this vision statement and you point to "here's where we were right and here's where we were wrong and here's what we need to do instead." So, unlike a blueprint - this part I really agree. Maybe that's not the best analogy of a blueprint, because you're allowed to change your vision and I think that's a really important point. In fact, if anything, you should be revisiting it every six months to say "is this still valid and how does it need to change if it does?"

Dave Sharrock: 11:32 I kind of love that idea because then you don't need to kind of map all the way to a significant, you know, far, far away vision. You can sort of navigate through. There's some natural steps. I need to prove this work, so I'm going to chase that down and make sure that's working. Once we've got that, then there's a natural stage as we go to that next level. So that fits in. So I mean we talk all the time about incremental delivery, about taking sort of steps and reevaluating, taking another step, re-evaluating, and yet so many times, if you've got that vision locked in and it's painted on the walls of the organization and we've invested a whole bunch and it's within the brand, well, that's not getting adjusted and changed and evolved as we learn more.

Radhika Dutt: 12:16 See, exactly I agree with that. And that's why the radical vision statement is a fill-in-the-blanks statement so that you don't fall in love with your own words - that it is OK to change it. You know, in fact, you don't have this ownership of this vision, feeling like, "Oh my gosh, you know, I spent so much in crafting this vision. I'm going to frame this," if anything. It's more like, you know, you look back and you go like, "Ah, you know, now that six months have passed, I see where I was wrong." Like it should just be glaringly obvious to you, like that's really what you want in this vision. And the other thing is you never want someone to repeat your vision in your words. You really want them to say it in their own words. And then you know that they've internalized it, because that's the point of it, like it shouldn't be something that's memorized, right.

Dave Sharrock: 13:10 Now it sounds to me like if you know the vision statement has been missed or is going in the wrong direction, it's obviously measurable. You've got some really clear understanding of what should be changing as you go forward. Is that right?

Radhika Dutt: 13:22 Well, so let me give an example of a vision statement, right, in the Radical Product Thinking format. And this is a company that was acquired for the product that we built. So the vision would read as follows: Today, when sales teams for TV advertising, when they want to create quotes for TV ad sales and offer numbers in terms of audience segments and which programs will reach those audience segments, they have to look up data from a bunch of different data providers, and it's a swivel chair approach where you log into one, then log into another, et cetera, and creating such a quote takes eight hours. This is unacceptable because TV advertising is really falling behind digital advertising. All of this is automated and TV advertising is going to die as a result if we don't change things. We envision a world where there's a single interface that gives you access to several data platforms and accessing all of this is really seamless and easy and it allows you to plan your entire TV campaigns really easily. We're bringing about this world through a data product that allows all of these data platforms to be plug and play and make it all accessible through a single sign-on and a really simple interface that doesn't require an analytics degree to be able to run SQL queries, but just simple drag and drop. So you see that I hadn't told you anything about the company, anything about the product, but you could understand exactly...

Radhika Dutt: 14:55 And, by the way, this is a really complicated product, a really complicated market, but the vision sort of really simplified all of that. I haven't told you all the details, like, for instance, you know we could be targeting the sellers, we could be targeting the buyers. It was a complex marketplace, but this all simplified it to this clarity of who we were targeting. And I think some of this question that you go to, you know, is well, does it have to be measurable, etc. But all of that happens as part of the execution and we can talk about the puzzle setting and puzzle solving in a moment that you talked about. Is that, you know, we feel like I want to be ubiquitous, so I want to build this product that works for sellers, that works for buyers, it works for everyone. But the reality is, you know, one lesson I learned from my publicist in Radical Product Thinking is don't say you want to sell to everyone. Say that, talk about who has the most urgent need for your product and then, once you conquer that market, then you go off to the next and that's your vision evolution.

Dave Sharrock: 16:08 I was going to let Peter jump in there.

Radhika Dutt: 16:10 I feel like I've dominated a lot of the conversation so far.

Peter Maddison: 16:15 I think that's a perfect way of putting it together and, yeah, your definition there encompasses a lot of what gets missed when it gets boiled down. I've seen organizations, to your point, spend months and months of very expensive senior executives locked up in a room to come up with the ultimate vision statement that's so generic because it's trying to satisfy everybody that it means nothing to anybody and doesn't help in any way, shape or form, and nobody's happy at that point because nobody got what they needed out of it. But, as you say, if you can articulate it, start to articulate and inform what is it that we're actually trying to do? Who are we trying to serve as we put this together? What does all of this look like? Then it becomes a much clearer articulation and it becomes a much more valuable thing to have. One of the pieces I often challenge clients with is around "well, are you going to be able to make a decision based on this?" If you're not going to be able to make any decision - yes or no - based on what you've defined, then it's really not very much use. Let's go and start to refine this and enhance it and bring some of the sort of other key components in. So it's actually a little bit more valuable as a tool within the organization.

Radhika Dutt: 17:28 That's such an important point, right, that you should be able to make decisions based on a detailed enough vision like this. Because what happens is we have fluffy vision statements, like being the number one in aerospace, and then, you know, your vision is no longer acting like a filter. Should we do share buybacks? Yeah, sure, you know that makes us number one too. Should we do this? Like everything is "yeah, yeah, let's just do it," right. It's not acting like a filter, whereas when you have a detailed vision like this, then you can start to see the yin and yang of long-term versus short-term and it drives your decisions. And so the way I drive decision-making using this vision is I talk about it on a two-by-two grid where your y-axis is "is this a good vision fit or not?" That's the long-term and the short-term is your x-axis, "is this good for survival or not?" And so decisions that are good for vision and survival, you know, those are the easy decisions, right, and we often sort of hang around in that quadrant of easy decisions.

Radhika Dutt: 18:28 The harder decisions are investing in the vision, which is when it's good for the vision but it's not great for short term. So examples of this would be, you know, taking some time to fix technical debt or in product, this would be, you know, taking some time to do user research or doing training, et cetera. So that's investing in the vision. And the opposite of investing in the vision is when you're taking on what I call vision debt. Right, this is when you're doing things that are good for short-term survival. So when you say, for example, "Hey, this deal, if we just build this custom feature, we'll win this wonderful deal," that's not great for the long term vision, because it adds those custom features you're going to have to maintain, but it's great for the short term, and so that's vision debt. And, by the way, if you take on a lot of that vision debt, you catch the disease I call obsessive sales disorder.

Peter Maddison: 19:18 I was going to say there's going to be a disease associated. You saw it coming.

Radhika Dutt: 19:25 I love it. But you know, once you start to plot these things on a quad, on these four quadrants of easy decisions versus danger, versus vision debt and investing in the vision, now you can start to see how you balance going after your vision versus just chasing short term stuff, right, and so this sort of thinking means you're using this vision for everyday decision making.

Dave Sharrock: 19:51 There's an element, and again it's one of those things that the vision absolutely should be in front of us all the time, but because it allows us to make decisions and because it allows us to, for example, pay down technical debt at the right point, when, when it... you know, there's a risk to survival, for example, if it's going forward, rather than, and so many times these things are seen as it's somebody's favorite topic to follow through, and now you end up getting distracted for whatever reason - you know, very good reasons - you're trying to clean up the technical architecture, whatever it might be, but there may be other things on the horizon to chase after. Now this feels - and I know this is a leading question, but this feels like very naturally set up for objectives, OKRs, various things like that, and I know that's definitely not the way you want to see it going. How do you tie in the vision that you're describing there with performance, with the work that gets done, with teams focusing on the right things and being able to demonstrate progress on things that might take a while to get done?

Radhika Dutt: 20:55 You know I love that you jumped right to performance. Right, because, you know, we jump from vision to "are you performing well enough to achieve this vision?" And I think we can frame the question even differently. You know, how do you define performance when it's a really complex puzzle you're really solving, right? Because the thing is, performance used to be easy to measure in the 1940s and that's where this idea of goal setting came about. Peter Drucker revolutionized, you know, management thinking in the 1940s because he said, instead of command and control, let's do goal setting together with employees and then measure them against that. And that worked because he used to... He got all of these ideas working with GM and a workforce that was primarily working on assembly lines with little automation, and so you could tell that, you know, this person here is a much better performer than this other one. He installed 45 tires, the other one did 40, right, like you can see performance in this very clear way, because there's one right answer for how you install tires and the problems that we solve. How do you know that it's good performance when someone says, you know, "I achieved 50% more clicks on this." Is it really good? Better performance? And that's the thing I was struggling with, you know, because I would often see teams present information to me and see them giving me numbers. Right, but it didn't always equate to higher performance.

Radhika Dutt: 22:30 And what I found is OKRs often give leaders the illusion of performance because, you know, you think you're seeing these numbers, you're tracking progress. The reality is, when I show you those numbers, I am choosing which numbers I'm showing you and my incentive is to show you numbers where I'll go, "Ta-da, look, I achieved whatever you wanted me to," and whatever the bad numbers are, I've just swept them under the rug. And you, as a leader, have no sense that I've done that. And, by the way, like people always respond to this saying, "Oh yeah, that just means you haven't set the right targets." Well, you can set whatever targets I want, whatever you want, and, you know, there will always be gaming of it. In fact, there are laws known for this. There is Goodhart's law in economics that says "when a measure becomes a target, it ceases to be a good measure." There's Campbell's law that says, you know, "if you're evaluating based on certain metrics, those metrics are most likely to be gamed and therefore corrupted and therefore no longer useful." So we know those things from other fields, but we haven't applied that in management.

Dave Sharrock: 23:36 We could dig into so much on the management side. I know we discuss this all the time because of course, there's such a big shift. I mean, the work we are doing now is just not - many organizations, the work many organizations are doing now just needs to be managed in a different way for everything that you've just been...

Peter Maddison: 23:54 And we do quite often end up in organizations teaching them the very laws you're talking about and describing to them how to design good metrics. Because, yes, all metrics will be gamed. And if you... so, the understanding, the consequences of those metrics being gamed and what will happen then, and how do you then balance these things out are really truly understanding a lot more about what is it I'm looking at. As a consequence, the data that I'm gathering, and how am I turning that data into valuable information that I can use to make knowledgeable decisions, and all of that flow through an organization is critical to put into place. But, exactly to your point, very often nowhere near that level of thought goes in to how do we define a goal and set some metrics? And if I've got an ulterior motive in what I'm defining, then yeah, I'm going to find a bunch of things that make me look good.

Radhika Dutt: 24:44 And you know, like I find a bunch of ways people try to sort of contort logic and say, "but we can still make OKRs work," right. I mean, a lot of people will say, "Well, just divorce compensation from OKRs and everything will be fine." Well, I've never seen things work out that way because, you know, in terms of compensation, it's not like one, you know, has this incentive to show that you're a high performer just because you're thinking about this year's bonus, you're thinking about your career track. It's a long term thought process that you have, right, and you want to be seen as a high performer. And this isn't malicious, you know, just even subconsciously. If I know that this is how you're evaluating me, it sort of makes even me subconsciously want to look at the good metrics and want to ignore the bad metrics. Right, and the reality is what you most want employees is to... You want them to play detective, to look at the bad metrics and then say, "Huh, I wonder what's actually going on." You don't want them to ignore it.

Dave Sharrock: 25:51 There's so many, there's a lot of things to think about. As you're describing it, the thing that jumps to my mind is culture and an organization that the headache with so many performance measures is they're pass/fail. And if I do well, if I hit the performance measures, I'm considered to be successful and I progress in that organization. And yet the vision that you've outlined, the progress that you want to make with hypotheses and testing things out, is not a pass/fail. You actually should fail low because you're now learning. So this is one of the things that we certainly see is how do you create a culture where a metric or a measure or anything like any of those things is acceptable? When we go this idea, we've delivered against it. We did everything we could. Total failure was the wrong thing for us to do and not have that be a problematic conversation.

Peter Maddison: 26:44 And if it's tied to the individual's performance where you run into a problem, then yes, that is going to very much happen. But that's the association of basically replacing the Drucker's MBOs with OKRs and just slotting one in for the other and treating them in exactly that way, because they then become very much a target, with a measurement that I'm going to get scored against and I have to succeed, which means I'm going to game it, so that I do. Exactly so.

Radhika Dutt: 27:10 I think this is where the puzzle setting and puzzle solving comes in. And you know, even before getting into puzzle setting, I actually want to jump into puzzle solving, because the answer to your question really lies in there. How do you create this way of thinking about failure being acceptable? I actually don't even like to talk about it as failure, and you know what you said about failing in really small ways is really the way to go, because even as a company, you don't want these big failures, right? So the puzzle solving template that I've developed over time in working with organizations means asking three questions. So it's three questions in this template.

Radhika Dutt: 27:52 The first question is "how well did it work?" And even that question, by the way, you notice that it's not a binary question of "did you or didn't you achieve this." It's nuanced and asking you "how well did it work?" And it requires defining a hypothesis and leading and lagging indicators, and this way you've actually defined what does "how well" and "did it work" mean? And so the hypothesis is where you say, "If we try this experiment, then this is the outcome I'm expecting, because blah, blah," right. And so in asking this first question, this triggers the left brain, or the analytical thinking where you're saying, you know, "this is the experiment, the hypothesis leading lagging indicators. What am I seeing?" And then the next question comes in.

Radhika Dutt: 28:36 So the second question is "what did you learn?" This is where I see a lot of teams sort of spit out a bunch of stats. You know, I start to see numbers like "OK, there's a number of weekly active users, time spent on site, you know, bounce rate, blah, blah, blah." And I have to stop my team and say, "No, no, no, no. We're not going to just list stats for me. You have to play detective, look at various numbers, figure out what you're learning, like what is actually happening, and tell me the story, like tell me the narrative behind it." And to tell me the story, by the way, they're gonna have to do a lot of digging and figuring this out, but what I want is the narrative, and that triggers the creative part of the brain.

Radhika Dutt: 29:18 And then comes the final question, which is really the light bulb moment that I find. And that question is "based on how well it went and what you've learned, if I were to give you a magic wand, what would you ask for?" And that last question brings so much clarity. I just love to see the answer that people come up with on that, right, because all of a sudden they have reflected and then they have conviction in their decisions and they make better decisions in answering that last question. And so this is what we want to see. And, as a leader, like when you start to see and when you yourself start to present information in this format, it's not so much failure that you're normalizing, but it's learnings that you're really normalizing this process of learning and you're creating space for learning and sort of rituals for learning, and you hopefully learn in small chunks so that you know you don't have one big bang project, and then you say, "Oh, here's the list of failures and what have we learned from it."

Peter Maddison: 30:24 I really like that as a model. I mean, you're stringing together a lot of what I would call good coaching questions together into a way of building and encouraging that learning as a part of how you start to look at what is it we're trying to achieve. What did we learn from this? If I could wave a magic wand, what would be the one thing I'd fix? I've been asking that question a lot this week actually to people, so, but I think that's a really good way, I think, of drawing out a lot of what we're actually trying to get from and going through these exercises of setting an objective, figuring out how we're going to measure it. But what we're really looking for is what did we learn? Like? What are we going to do next? What do we want to learn next? Like? What do we want to learn next? Like what might we try to learn? The next thing we need to know and that's how I mean it takes you back to Senge and talking about learning organizations-

Dave Sharrock: 31:18 Well, I mean, can I maybe drill in a bit deeper? Because, again, one of the things I see so many times is there's the depth you described, the kind of puzzle outcomes. One of the things is the difficulty we often see or I've certainly experienced is well-formed hypotheses or experiments. It's very easy to get confused, with any sort of an uptick being a positive result. And I remember one of the things and this really drove it home in one of the startups I used to work in and we saw over a couple of years continuous growth in subscriber growth and I mean, everything looked great whenever you looked at this curve and after a few years we eventually figured out to remove the growth of the market and then everything just went flat and it looked like nothing had just gone wrong. Because of course yes if the market's growing, then your subscriber growth is growing, which is, but that doesn't mean you're growing ahead of every of the market itself, and that just the that. Defining a hypothesis, an experiment or a problem and how to solve it is really difficult to do. I mean, we've all tried it and failed as many times as we've tried it and succeeded. How do you guide people to create really strong sort of defensible, well articulated experiments or hypotheses?

Radhika Dutt: 32:28 That is such a great question and I think the answer to this lies in the depth of our understanding of the problem, right. I think, unless you have a deep enough understanding of what problem you're solving for like in the product space, right? Unless we have a deep enough understanding of the user persona, like you've really understood their workflow, what they're trying to accomplish, where are they struggling, if you're able to put yourself in their shoes well enough, because you've truly observed them, etc. Then you know what you want to design as functionality in terms of the solution to the problem that you observe. And so, following from there, like if you have really thought through what that solution looks like, then you have a sense of how are you going to measure it.

Radhika Dutt: 33:19 What I find happens is when the first two aren't clear enough and we try to write a hypothesis and an experiment, it doesn't work well, right, and very often, I think, when you're struggling to write a hypothesis, when you don't know what would I measure really, you know you kind of have to go back to the first two problems and it's an indicator that you need to really get to the heart of what is the problem you're solving for and how well have you understood the solution, like, have you tested your solution, even in terms of just user testing, using prototypes, clickable prototypes, whatever else, so that when you write a hypothesis and an experiment you really know, kind of, what you're testing for?

Peter Maddison: 34:03 Thank you, I like that. Well, I think we're at time now, maybe even a little over. It's been such a great conversation, so thank you very much, Radhika. So at this point in our conversation for our listeners, we typically ask for sort of three things, and so they basically one point from each of us from the conversation that we thought was a highlight of the conversation, and so I will let you go first, Radhika, what would you like the audience to take away from this conversation today?

Radhika Dutt: 34:33 You know this last point that you made, Dave. It really made me think about how do you write good hypotheses and leading and lagging indicators. I think it was an aha moment for me and something that I want to write about in the book as well, because I think it is a hard problem writing good hypotheses, and so thank you for that highlight.

Dave Sharrock: 34:54 Awesome, Dave here's one of the things that is spinning around in my head after this conversation is and I think this is touched on in so many different ways from the vision statement when you're clearly describing or articulating something where the understanding of the customer and the customer's pain points is very vividly described, right through to what we were just closing out now, which is needing to know how customers behave and where things work well for them and where they don't. And that takeaway for me is just how much closer many organizations and product teams need to be to their target customer to really understand what's going on and therefore be able to talk about hypotheses and vision products which really resonate with the customer. Awesome.

Peter Maddison: 35:37 I think for me, I really liked your articulation of decision making with the vision, making with the vision like, is the vision actually going to be able to make decisions, and is this decision one that I'm making for survivability versus the direction I'm actually ultimately trying to go? And I quite like that as a way of articulating that, uh, that concept to people. So, thank you very much. All it's been a great conversation. I really enjoyed it and, uh, I look forward to uh, many more as always, and thank you again, yeah.

Radhika Dutt: 36:09 Thank you so much for having me. This was just such a pleasure and so insightful. Thank you.

Dave Sharrock: 36:13 Thanks a lot, Radhika.

Peter Maddison: 36:14 Thanks Peter, Thanks Radhika. Thanks Dave. You've been listening to Definitely Maybe Agile, the podcast where your hosts, Peter Maddison and Dave Sharrock, focus on the art and science of digital agile and DevOps.