Problem Space Exploration

Does the Double Diamond need fixing? TL;DR: No—but let’s talk about how to make it work in practice. First, a quick overview of the Double Diamond: 1️⃣ Discover: Understand what the problem is, usually by talking to or observing customers. 2️⃣ Define: Use discovery insights to reframe and define a clear problem statement. 3️⃣ Develop: Co-create and explore different solutions with relevant people. 4️⃣ Deliver: Test solutions at a smaller scale, discard what doesn’t work, and iterate on what does. Makes a lot of sense, doesn’t it? Yet, not everyone thinks so. Some experts have pushed back on the model with arguments like: ❌ It’s too linear and lacks feedback loops. ❌ It doesn’t explicitly include critique or iterations for improvement. ❌ It oversimplifies the messy, iterative reality of product discovery and design. I don’t believe these arguments hold up. However, it’s true that many teams struggle to apply the Double Diamond, falling into the very pitfalls critics highlight. Here’s the reality: 1️⃣ Teams and organizations need structure. Linear, repeatable processes provide clarity. They’re easy to understand, learn, and apply consistently across teams. Wait what? I just said I don’t agree with the critique that the Double Diamond is too linear. Keep reading. 2️⃣ Creativity thrives within boundaries. A system for creativity that is reliable and repeatable gives teams a shared language and method to collaborate effectively without stifling ideas. Not everyone can "play Steve Jobs" and hope for magic to happen. But the Double Diamond can feel rigid if you don’t layer in practical methods that account for iteration, critique, and collaboration. Making the Double Diamond work in practice: ➡️ Problem Framing: To explore the problem space and define clear problem statements grounded in customer and business needs. ➡️ Design Sprints: To explore the solution space, run experiments, and establish confidence. Yes, these methods are linear. But they inherently incorporate iteration and flexibility. Problem statements defined in Problem Framing remain hypotheses until tested, which happens quickly in a Design Sprint. Afterward, you either iterate on the solution or revisit your problem statement or target audience—enabling fast, focused cycles of improvement. Why these methods work: ✅ They’re prescriptive, making them easy to learn and scale across teams. ✅ They’re fast—no waiting six months to figure out if a solution will work. ✅ They produce tangible outcomes: clear problem statements or customer-tested prototypes. ✅ They’re collaborative, involving the right people at the right time: stakeholders for problem definition and experts for solution exploration. ✅ They force customer-centricity: problems are defined based on insights, and solutions are validated with customers. It's the best of both worlds: navigating the messy, iterative process of product development using common-sense, step-by-step methods that don’t rely on luck or genius.

The creative process is not clean. It’s not a straight line. It’s a loop, a mess, a dance. But the Double Diamond is still your best map. It’s not reality. It’s a compass. Here’s how to use it to make real progress: Two halves keep you on track: 1. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝘀𝗽𝗮𝗰𝗲: Find the right problem to solve. 2. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘀𝗽𝗮𝗰𝗲: Build the right answer. Each half has two moves: 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: A. Research (diverge): Open up. Gather facts. Learn. B. Focus (converge): Pick a problem. Get clear. Commit. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: C. Ideation (diverge): Explore options. Sketch. Go wide. D. Iteration (converge): Test. Refine. Ship. Repeat. Real work is not linear. You loop. You bounce. You learn. Here’s how to make the Double Diamond work in the real world: 𝗪𝗶𝗿𝗲 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝘁𝗼 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀 • Start with a clear outcome. Pick a metric to move. • Map your bets with an Opportunity-Solution Tree. Stay out of the solution trap. • Keep a weekly discovery rhythm. Small, steady learning wins over big, rare studies. 𝗦𝘁𝗼𝗽 𝗴𝗼𝗹𝗱-𝗽𝗹𝗮𝘁𝗶𝗻𝗴 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 • Write a one-pager: Hypothesis, Evidence, Unknowns, Decision date. • Time-box your research. Five calls or ten days, then move. 𝗗𝗲𝗰𝗶𝗱𝗲 𝗹𝗶𝗸𝗲 𝗼𝘄𝗻𝗲𝗿𝘀 𝗶𝗻 𝗳𝗼𝗰𝘂𝘀 • Pick one target segment and one job for this cycle. • Declare non-goals. Cut scope creep before it starts. • Check your problem statement: user, need, why it matters. 𝗜𝗱𝗲𝗮𝘁𝗲 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗵𝗮𝗼𝘀 • Start with silent brainwriting. Share after. Avoid groupthink. • Force quality: At least five real options before you pick. 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 𝘁𝗼 𝘀𝗵𝗶𝗽, 𝗻𝗼𝘁 𝘁𝗼 𝗽𝗲𝗿𝗳𝗲𝗰𝘁 • Set an appetite. Decide how much time this deserves. • Start with low-fidelity prototypes. Raise fidelity only where proof is needed. • Define kill/keep/double-down rules before you test. 𝗞𝗲𝗲𝗽 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗮𝗻𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗶𝗻 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹 • Dual-track wins. Always be discovering the next bet while you deliver the current one. 𝗚𝗿𝗼𝘂𝗻𝗱 𝗿𝘂𝗹𝗲𝘀 𝗳𝗼𝗿 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗲 𝘁𝗲𝗮𝗺𝘀 𝘁𝗵𝗮𝘁 𝘀𝗵𝗶𝗽: • Hypothesis-driven and time-boxed. Make a bet. Set a clock. Move. • Interdisciplinary by default. PM, Design, Eng, Data, GTM. Better ideas, faster buy-in. • Human-centered is business-centered. Real user value creates business value. • Iterative, not linear. Expect rework. Celebrate it. That’s learning. 𝗔𝗻𝘁𝗶-𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗵𝗮𝘁 𝘄𝗮𝘀𝘁𝗲 𝗰𝘆𝗰𝗹𝗲𝘀: • Framework worship. The Diamond is a compass, not a contract. • Solution-first reflex. “We already know.” You probably don’t. • Endless divergence. No time box, no decision gate. • Linear thinking. Refusing to loop back when the data says so. The creative process is messy. The Double Diamond helps you mess with intent. Use it as a map, not a rulebook. And let your team learn, decide, and ship—on purpose.

𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗽𝗮𝗰𝗲 𝗮𝗻𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗦𝗽𝗮𝗰𝗲: 𝗞𝗲𝘆 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 From the book The Lean Product Playbook by Dan Olsen, emphasizing their importance in creating products that align with customer needs. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗽𝗮𝗰𝗲: The problem space refers to the specific customer problem, need, or benefit that a product seeks to address. It focuses on understanding and articulating what the customer requires and why they need it, without prematurely determining how to solve it. A well-defined problem space ensures a clear understanding of the customer's challenges, paving the way for effective solutions. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗦𝗽𝗮𝗰𝗲: In contrast, the solution space encompasses the specific implementation or design that addresses the identified customer need. It deals with the how—the features, services, or products created to solve the problems outlined in the problem space. 𝗛𝗼𝘄 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗗𝗲𝗳𝗶𝗻𝗲𝘀 𝘁𝗵𝗲 𝗠𝗮𝗿𝗸𝗲𝘁? A clear understanding of the problem space is critical because it directly influences market definition. Identifying and articulating specific customer needs or problems enables companies to pinpoint the target market segment experiencing these issues. This clarity allows for the development of tailored solutions that resonate with the intended audience, enhancing product-market fit. 𝗪𝗵𝘆 𝗪𝗲 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗦𝗽𝗲𝗻𝗱 𝗠𝗼𝗿𝗲 𝗧𝗶𝗺𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗽𝗮𝗰𝗲 𝗳𝗼𝗿 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀? Spending adequate time in the problem space lays the groundwork for more effective solutions. A deep understanding ensures that products tackle genuine customer challenges, avoiding misalignment between market needs and product offerings. By clarifying the problem space, teams can make informed decisions about product features and design, ensuring they are relevant and valuable. Focusing on real problems reduces the risk of investing resources into unnecessary or unwanted features. 𝗪𝗵𝘆 𝗣𝗲𝗼𝗽𝗹𝗲 𝗚𝗿𝗮𝘃𝗶𝘁𝗮𝘁𝗲 𝗧𝗼𝘄𝗮𝗿𝗱 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀? While problem analysis is essential, people often gravitate toward the solution space because developing solutions provides concrete outcomes, which can be more satisfying than abstract problem analysis. Solutions allow teams to apply their creativity and expertise to build something new and innovative. Jumping to solutions gives a sense of immediate progress, even if the underlying problem is not fully understood. Dan Olsen cautions that without a thorough understanding of the problem space, solutions may miss the mark, resulting in products that fail to meet customer needs. To achieve successful product development, it's essential to balance the enthusiasm for creating solutions with diligent problem analysis. This approach ensures that solutions are not only innovative but also effective in addressing the right problems.

Problem Solving: The Art of Navigating Complexity in the AI Era I've learned that in enterprise settings, problems rarely come with neat definitions or clear boundaries. They're messy, interconnected, and often evolving as we work on them, and solutions dont appear magically; you have to work on them from multiple perspectives. While AI excels at solving well-defined problems, the uniquely human skill lies in unpacking complexity by breaking down ambiguous challenges into workable components. This means becoming comfortable with uncertainty, asking better questions, and resisting the urge to jump to solutions. It's like compound interest for problem-solving; the more you invest in understanding the problem space, the greater your returns in solution effectiveness. The most effective problem solvers I work with have mastered four capabilities:   1. Deconstructing multi-layered problems into manageable pieces   2. Studying the problem from different perspectives.   3. Iterating rapidly between hypothesis and testing, and   4. Synthesizing insights across domains and stakeholders. However, I've discovered that AI can serve as an exceptional thought partner in this iterative process. When facing complex challenges, I utilize AI to stress-test my hypotheses, explore potential blind spots I might miss, and rapidly prototype various solutions to the problem. It's like having an always-on collaborator, and a whole slew of subject matter experts in different domains who can help you think through multiple scenarios simultaneously. The future belongs to leaders who can dance with ambiguity while maintaining human agency in defining problems and making decisions. With AI as our thought partner, every one of us can now possess superpowers, accessing knowledge in any domain and accelerating thinking cycles that once took weeks and months to complete, now into minutes and hours. Foundry for AI by Rackspace (FAIR™) D Scott Sanders Ben Blanquera #ProblemSolving #AI #Leadership #CriticalThinking #EnterpriseSolutions #FutureOfWork #ComplexSystems

🚫 Stop wasting millions on innovation. I’ve seen too many corporate innovations fail — not because of a lack of effort or brilliant minds. The real problem? Companies rush to build and push new products. They chase perceived problems. Leadership spots a trend — maybe it’s AI, maybe it’s a competitor’s new feature — and the directive follows: 👉 “Go build that!” But here’s the truth: 💡 We’re addicted to solution. We build solutions in search of a problem. We get excited by the what, but we don’t deeply understand the why. Ask yourself: - How many internal tools just sit unused? - How many features launched that solved no real pain? - How much tech was bought without first understanding the core business challenge? 👉 The most impactful innovation doesn’t start with a product idea or technology. It starts with deep understanding of the problem space. And that’s hard, uncomfortable work. It means: 🔹 Understanding human behavior — seeing how people struggle and adapt. 🔹 Challenging assumptions — asking why things are done this way instead of accepting the status quo. ✅ Can you clearly state the problem? ✅ Is it validated with evidence? ✅ Does it impact real people? If not, your solution is a gamble — a shot in the dark that wastes time, money, and energy. Let’s change corporate innovation culture. Before you approve the next solution: 👉 Challenge your teams. 👉 Make them articulate the problem clearly. 👉 Demand evidence. A problem-first approach isn’t slower. It’s smarter and more impactful. #Innovation #CorporateInnovation #ProblemSolving #DesignThinking #ProductDevelopment #IgnoredTruths #BusinessTransformation

Ever watched a local fabricator build a better mechanism than your SolidWorks assembly? I have. Multiple times. That's creative problem solving in action. And it's fundamentally different from how we're taught to solve problems in engineering school. Here's the thing... 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐨𝐥𝐯𝐢𝐧𝐠 is like following a recipe. You break down the problem, apply known formulas, and march towards the solution step-by-step. It's predictable, repeatable, and works brilliantly for well-defined problems. Think of it as solving for stress in a cantilever beam. You know the formula (σ = My/I), plug in the values, and boom—you have your answer. 𝐂𝐫𝐞𝐚𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐨𝐥𝐯𝐢𝐧𝐠 is more like cooking without a recipe. You work with what you have, make unexpected connections, and sometimes end up with something entirely different (but better) than what you planned. Remember ISRO's Mars Orbiter Mission (Mangalyaan)? With a budget of just ₹450 crores—they couldn't follow NASA's playbook. Instead of using expensive deep-space communication systems, they got creative. They used the spacecraft's own trajectory around Earth to build up velocity (like a slingshot), saving massive amounts of fuel. They even put the orbiter in sleep mode during the journey to conserve power. The mission succeeded not because they had the best resources, but because they reimagined the problem. In product development, I've noticed: - Analytical thinking helps you optimize a design by 10% - Creative thinking helps you eliminate 90% of the design The magic happens when you blend both approaches. Start with creative exploration (what if we eliminated this component entirely?), then validate analytically (will it still meet the safety factor?). Some engineers reach for ANSYS at the first sign of trouble. Others start sketching on napkins. Which one are you? --- Edit 1: The sole intention of the post is to push engineers to summon their creative faculties before reaching out to analytical tools for maximum impact. Note: Technical details are overly simplified for general understanding. Dig into NASA and ISRO Tech Briefs for more details. Debates comparing space agencies capabilities are highly discouraged! #FrugalInnovation #RocketScience #ProblemSolving

🗺️ 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐥 𝐒𝐩𝐚𝐜𝐞 : 𝐓𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐒𝐢𝐦𝐩𝐥𝐞𝐱 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 🔎 In our recent exploration of smart experimentation methods, one approach merits closer attention for its underappreciated potential: Simplex Optimization (#SOpt). Despite being mentioned significantly less frequently in publications compared to Design of Experiments (#DoE) and Bayesian Optimization, SOpt offers distinct advantages that merit consideration. Its simplicity and iterative exploration capabilities make it a formidable tool in navigating parameter spaces and uncovering optimal solutions. ⚙ 𝑯𝒐𝒘 𝒅𝒐𝒆𝒔 𝒊𝒕 𝒘𝒐𝒓𝒌 ? Imagine you realize an organic chemical reaction where time and temperature need to be adjusted to provide the highest yield. In order to explore your experimental space and find the most promising conditions, here are the steps needed : 1️⃣ Start with an initial simplex, a geometric figure defined by a number of coordinates (vertex) equal to one more than the number of factors being optimized (here N+1 = 3). 2️⃣ Evaluate the response (yield) at each vertex of the simplex. 3️⃣ Determine the centroid of all points except the worst-performing one (lowest response value). Reflect this worst point through the centroid to obtain a new experimental point. 4️⃣ Evaluate the response at the new point. 5️⃣ Depending on the performance of the new point, update the simplex by replacing the worst point with the new point. If the new point is better than the best existing point, consider moving the simplex in that direction. If the new point is worse, consider contracting the simplex towards better-performing points. 6️⃣ Continue this process iteratively until a termination criterion is met, such as reaching a specified number of iterations or achieving a desired level of convergence. 👍🏼 𝑩𝒆𝒏𝒆𝒇𝒊𝒕𝒔 Versatility: It can handle various constrained optimization problems. Light computation: Simplex optimization does not require complex analysis, making it a user-friendly and very graphical tool for low dimensional spaces. 👎🏼 𝑫𝒓𝒂𝒘𝒃𝒂𝒄𝒌𝒔 Local optima: Simplex optimization may converge to a local optimum rather than the global one. Initialization & dimensionality sensitivity: The performance of the method can be sensitive to the choice of initial points and problem dimensionality. Finding a good initial simplex may require some trial and error. Interpretability: No model is used to learn from previous iterations, preventing to identify influential factors or realize predictions. 💡 While Simplex Optimization may reside in the shadow of its more widely recognized counterparts, its efficacy in iterative exploration and simplicity of approach underscore its relevance in the pursuit of optimal solutions within complex parameter spaces. 📖 Reference : "𝐴𝑛 𝑎𝑙𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑣𝑒 𝑚𝑒𝑡ℎ𝑜𝑑 𝑓𝑜𝑟 𝐷𝑒𝑠𝑖𝑔𝑛 𝑜𝑓 𝐸𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑠" by Mark L. Crossley : https://shorturl.at/EIPWZ

One of the biggest traps in product management, and how great Product Managers avoid it As product managers, it’s easy to get excited and jump straight from the problem space into the solution space. But that’s where many products fail. 👉 The problem space is all about deeply understanding the customer’s pain points, "THE WHY" 👉 The solution space is about creating features, tools, or experiences "THE HOW" When we skip the why and rush into the how, we risk building features customers never needed in the first place. Great PMs resist that temptation. They spend time in the problem space, validate problems with real users, ask questions, and dig deeper. Only then do they guide the team into the solution space, confident they’re solving the right problem. Don’t fall in love with the solution. Fall in love with the problem.