Innovation Feedback Systems

🚀 Now publicly available 🚀 The Data Innovation Toolkit! And Repository! (✍️ coauthored with Maria Claudia Bodino, Nathan da Silva Carvalho, Marcelo Cogo, and Arianna Dafne Fini Storchi, and commissioned by the Digital Innovation Lab (iLab) of DG DIGIT at the European Commission) 👉 Despite the growing awareness about the value of data to address societal issues, the excitement around AI, and the potential for transformative insights, many organizations struggle to translate data into actionable strategies and meaningful innovations. 🔹 How can those working in the public interest better leverage data for the public good? 🔹 What practical resources can help navigate data innovation challenges? To bridge these gaps, we developed a practical and easy-to-use toolkit designed to support decision makers and public leaders managing data-driven initiatives. 🛠️ What’s inside the first version of the Digital Innovation Toolkit (105 pages)? 👉A repository of educational materials and best practices from the public sector, academia, NGOs, and think tanks. 👉 Practical resources to enhance data innovation efforts, including: ✅Checklists to ensure key aspects of data initiatives are properly assessed. ✅Interactive exercises to engage teams and build essential data skills. ✅Canvas models for structured planning and brainstorming. ✅Workshop templates to facilitate collaboration, ideation, and problem-solving. 🔍 How was the toolkit developed? 📚 Repository: Curated literature review and a user-friendly interface for easy access. 🎤 Interviews & Workshops: Direct engagement with public sector professionals to refine relevance. 🚀 Minimum Viable Product (MVP): Iterative development of an initial set of tools. 🧪 Usability Tests & Pilots: Ensuring functionality and user-friendliness. This is just the beginning! We’re excited to continue refining and expanding this toolkit to support data innovation across public administrations. 🔗 Check it out and let us know your thoughts: 💻 Data Innovation Toolkit: https://lnkd.in/e68kqmZn 💻 Data Innovation Repository: https://lnkd.in/eU-vZqdC #DataInnovation #PublicSector #DigitalTransformation #OpenData #AIforGood #GovTech #DataForPublicGood

Following user feedback is a Product Management virtue. Is there an actual way to implement it, between all the noise, bugs, and stakeholder requests? Well… Most teams claim they are customer-driven. Yet the moment you open Zendesk, App Store reviews, survey results, and Slack threads, you instantly remember why everyone quietly avoids this work. Feedback is everywhere, contradictory, emotional, duplicated, and nearly impossible to turn into decisions.  It is chaos disguised as “insights.” This is why the new Amplitude AI Feedback release caught my attention and made it all the easier to decide to partner with them on this update. It successfully connects what users say with what they actually do, in one workflow. No extra tools.  No extra tabs. You see their words, frustrations, and praise. You see their behavior. And AI transforms it into ranked themes, rising trends, top requests, and complaints. Noise turns into clarity. Opinions turn into patterns. Patterns turn into action. And because it is native inside Amplitude, it kills the biggest problem in feedback work: Fragmentation. Everything flows into analytics, session replay, and cohorts, creating a full loop from insight to fix. You can trace why an issue matters, how many users care, how it impacts behavior, and which actions you should take. Finally, a single source of truth for PMs, UX, CX, and marketing. I’m also genuinely impressed with the supported sources of feedback: App Store, Google Play, Zendesk, Intercom, Freshdesk, Salesforce Service, Gong, Trustpilot, G2, Reddit, Discord, and X. Slack arrives in Q1, and there will be more! If you ever felt overwhelmed by feedback, this is one of the first attempts I have seen that genuinely solves the operational pain, not just the reporting part. It launches… Today! Take a look: https://lnkd.in/dAJKeTez What was the most successful update you know that came from the product’s users? Let me know in the comments. #productmanagement #productmanager #userfeedback

How to fail in an interview Topic: User Research Role: Product Owner/ Product Management 👔 Interviewer: "As a Product Owner, how do you incorporate user research into your decision-making process?" 🧑 Candidate: "I look at feedback from surveys and reviews to decide what users want." 👔 Interviewer: "That’s a start, but let’s dive deeper. Imagine this: You’ve launched a new feature, and initial feedback seems positive. However, over time, churn increases, and users complain it’s too complex. What steps would you take to ensure such issues are avoided in the future?" 🧑 Candidate: "I’d send out another survey to figure out what went wrong and try to fix it in the next release."   🎯 What the Product Owner Should Have Answered: ✍️ Empathize with Users: "I’d ensure continuous engagement with users through interviews, usability testing, and field studies. Surveys alone often miss the 'why' behind user behavior." ✍️ Iterative Validation: "I’d validate ideas early through prototypes or beta testing with a small user group. This helps uncover usability issues before a full release." ✍️ Metrics + Insights: "I’d combine qualitative insights with behavioral data, such as feature usage, drop-off rates, or session duration, to create a complete picture of user needs." ✍️ Feedback Loop: "After launching, I’d establish an ongoing feedback loop with users and prioritize iterative improvements based on data and direct user input."   🔍 Impact of a User-Centric Approach: ✅ Reduced Risk: Catching usability issues early prevents costly rework post-launch. ✅ Increased Engagement: Features designed with real user input drive better adoption. ✅ Stakeholder Confidence: A strong feedback loop demonstrates ownership and proactive problem-solving. 💬 Key takeaway: A Product Owner’s compass is user empathy. Research isn’t a task; it’s a continuous dialogue to build what users truly need.   📌 Your thoughts? How do YOU keep user voices at the center of your product decisions? 👇 👉 Join "Agile Interview Hub" for deeper insights: Link below

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

The 10-50-99 rule that improved my product launches: When reviewing designs: - At 10% completion, critique the concept - At 50%, critique the approach - At 99%, just check for bugs When I implemented this, our delivery time dropped by 40%. Previously: - We'd debate visual details at 10% - Request major changes at 99% - Create endless revision cycles Design feedback without structure creates waste. Different stages need different types of feedback. Early feedback should open possibilities. Late feedback should close gaps. Your team doesn't need your opinion at every stage. They need the right guidance at the right time. What feedback framework has helped your team deliver projects more efficiently? #ProductManagement #Leadership #ProductDevelopment #PMLife

💡Triple Diamond Design Process The "Triple Diamond" process is a process that builds upon the widely known Double Diamond design process. While the Double Diamond focuses on two main phases—problem definition and solution design—the Triple Diamond adds a third phase to add depth and breadth to the design methodology.  This variant of a triple diamond process, crafted by Ted Goas (https://lnkd.in/eJFCR8rF), adds a third diamond for iterative development. It emphasizes iterative cycles, prioritization of user needs, and continuous refinement of the solution throughout the product lifecycle. Quick overview of the 5 key phases of this process: 1️⃣ Discovery (What’s our problem?) This phase focuses on identifying the problem to solve. Goal: Understanding customer pain points & narrowing down insights into actionable focus areas. Activities: ✔ Customer empathy budding: Researching user needs. ✔ Market research: Analyzing market trends. ✔ Competitive analysis: Assessing competition. ✔ Insights prioritization: Organizing findings for strategic focus. ✔ Building product strategy: Setting goals for the product. 2️⃣ Definition (What’s our solution?) This phase focuses on solution ideation & validation. Goal: Generate multiple ideas, structure them and validate the most promising ideas Activities: ✔ Ideation: Brainstorming and generating ideas. ✔ Drafting experience workflow: Mapping out how users will interact with the solution. ✔ Wireframeing: Visualizing the solution. ✔ Initial prototyping: Creating early product models for testing. 3️⃣ Development (Let’s build our solution) This phase is about building, iterating, and refining the product. Goal: Breaking down features and iterating to reduce risks. Activities: ✔ Feature breakdown: Breaking the solution into smaller deliverable tasks. ✔ Iterative build cycle: Continuously building and improving the product. ✔ Collecting research insights: Using feedback to refine features. 4️⃣ Distribution (Initial customer feedback) Focuses on testing the product with users and preparing for the final release. Phases: ✔ Internal release: Early internal testing (alpha and beta testing) ✔ Early access program: Collecting feedback from early adopters. ✔ General (Public) release: Launching the product publicly. 5️⃣ Retro (What did we learn?) Post-release reflection phase to gather insights for future iterations. Using insights collected from feedback, metrics, and retrospective discussions to refine the product. 📕 A Comprehensive guide to product design process https://lnkd.in/eyh4YGy6 #design #designprocess #ux #uxdesign #productdesign #uidesign #ui

Management Must Collect Feedback (Or You’re Flying Blind) A retail chain ignored frontline cashiers’ warnings about outdated payment systems for 18 months. By the time they acted, 22% of customers had switched to competitors. 300 stores closed. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗕𝗹𝗶𝗻𝗱 𝗦𝗽𝗼𝘁𝘀 𝗔𝗿𝗲 𝗖𝗼𝘀𝘁𝗹𝘆 – 85% of employees see problems leaders miss (Gallup). – Companies without feedback loops make decisions 3x slower (MIT Sloan). – 74% of turnover traces to “my voice doesn’t matter” (LinkedIn). 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸-𝗙𝗶𝗿𝘀𝘁 𝗖𝘂𝗹𝘁𝘂𝗿𝗲 → 𝗔𝗻𝗼𝗻𝘆𝗺𝗼𝘂𝘀 = 𝗜𝗴𝗻𝗼𝗿𝗮𝗯𝗹𝗲 Use tools like Officevibe for candid weekly pulse checks. Mandate managers to act on 1-2 team suggestions monthly. → 𝗥𝗲𝘄𝗮𝗿𝗱 𝘁𝗿𝘂𝘁𝗵-𝘁𝗲𝗹𝗹𝗲𝗿𝘀 Salesforce’s “V2MOM” system ties feedback to strategic goals. Publicly thank employees who surface uncomfortable truths. → 𝗖𝗹𝗼𝘀𝗲 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽 Share how feedback drove changes: “You said X. We did Y.” Google’s “TGIF” meetings let employees grill execs live. 𝗧𝗵𝗲 𝗥𝗢𝗜 𝗼𝗳 𝗟𝗶𝘀𝘁𝗲𝗻𝗶𝗻𝗴 Teams with strong feedback cultures innovate 56% faster (Harvard). 92% of employees stay loyal when heard (Microsoft). Customer satisfaction jumps 34% when frontline input is used (Forrester). No feedback? You’re not leading. You’re guessing. #Leadership #EmployeeVoice #CX

🔬 Invited to assess research novelty in Real-World Evidence — and one paper stood out Last week, I was invited by the University of Sussex Metascience Unit (UK Government) to contribute expert assessments to the Metascience Novelty Indicators Challenge. The task was refreshingly thoughtful. 📍 Instead of “rate this paper good/bad,” the survey asked reviewers to judge: • methodological originality • conceptual advancement • practical impact for the field • and whether ideas genuinely change practice A simple but mindful scale that separates true innovation from incremental noise. I reviewed five publications in Real-World Evidence (RWE), spanning ethics, transportability, AI imaging, and data science methods. One clearly stood above the rest: 📄 Data Science Methods for Real-World Evidence Generation in Real-World Data — Fang Liu, Annual Review of Biomedical Data Science ❓ Why? Because it doesn’t propose just another technique. It reframes the entire way we generate evidence from real-world data. 📍 Key messages from the paper: ✅ RWD are messy, heterogeneous, incomplete — traditional RCT-era methods are insufficient ✅ RWE requires an end-to-end pipeline, not isolated analytics ✅ Study design matters first (target trial emulation, pragmatic trials) ✅ Causal inference + ML must be combined, not confused ✅ Trustworthiness is non-negotiable: validity, uncertainty quantification, explainability, privacy, fairness ✅ Evidence must be regulatory-grade, not exploratory dashboards 🔊 In short: methodology + governance + ethics = credible RWE This resonates strongly with what we see daily across regulators, HTA bodies, and pharma teams. At Helios Academy Ltd – Where Science Meets Compassion, this is exactly the space we operate in: 🔺 Helping organisations move beyond “data access” toward decision-grade evidence that stands up to scrutiny. 🔺 Not more dashboards. 🔺 Better science. If you work in RWE, HTA, or evidence strategy, this paper is genuinely worth your time. Sometimes the most novel idea isn’t a new algorithm — it’s a better way to think. 🏷️ Keywords: #RealWorldEvidence #RWD #HealthData #CausalInference #HTA #EvidenceBasedMedicine #HeliosAcademy #Metascience ⚠️ Disclaimer: Views expressed here are my own. Helios Academy Ltd — “Where Science Meets Compassion” — is an independent educational initiative. This post does not represent the views of Astellas Pharma, my employer, and contains no confidential or company-related information.

65% of employees want more feedback. But most leaders struggle to give it effectively. Some are too vague. Some sound too harsh. Others avoid it completely to prevent conflict. The best leaders know feedback isn’t just about evaluation. It’s about growth. The good news? These challenges can be fixed. Here are four simple frameworks to help you deliver feedback the right way. FEEDBACK F - Focus on the situation with clear, specific context. E - Explain what happened in a non-judgmental way.   E - Empathise with the other person’s perspective.  D - Describe the impact of their actions constructively. B - Bridge the gap between now and their desired outcome.  A - Act by setting clear next steps.  C - Coach them and use feedback to support development. K - Keep things positive by acknowledging their progress. The SBI Model - Great for Performance Improvement S - Situation: Explain the specific event. B - Behaviour: Describe the behaviour seen. I - Impact: Share the effect on the team, task, or outcomes. The COIN Model - Build relationships while giving feedback C - Connect: Open the conversation on a positive note. O - Observe: Share neutral observations. I - Impact: Discuss the effects of the behaviour. N - Next Steps: Agree on the next steps together. GROW Model - Combines feedback with coaching G - Goal: Define the goal you have. R - Reality: Identify the current state or challenge. O - Options: Brainstorm different solutions. W - Way Forward: Decide on next steps to achieve the goal. Great feedback isn't just about pointing out what's wrong. It's about guiding growth, strengthening relationships, and helping your team become better. Like any skill, the more you practice, the easier it will get.  Use these frameworks to make your feedback clear, constructive and actionable. What's your go-to feedback approach? ________________ ♻️ If you like this post, share it to help your network  ➕ Follow me Felix Bertram for more content on leadership and growth.

I’m excited to share a key piece of work I’ve recently developed: the Mining Analytics Maturity Assessment Framework, designed to help mining companies understand where they truly stand on their digital transformation journey — and how to progress strategically toward predictive and optimisation-driven operations. In many operations, advanced analytics, AI models, and digital platforms are deployed before the organisation is ready to absorb and operationalise them. This leads to stalled initiatives, limited value delivery, and missed opportunities. To solve this, the framework introduces: 🔹 Six maturity levels — from raw data capture to real-time closed-loop optimisation 🔹 28 measurable indicators across System, Data, People, and Process 🔹 Clear alignment with high-value mine-to-mill use cases such as fuel optimisation, comminution control, predictive maintenance, and value-chain integration 🔹 Radar charts, maturity curves, and structured roadmaps that help leaders prioritise investments and de-risk digital programs The goal is simple but powerful: 👉 Enable mining organisations to move from “sense and respond” to “predict and act.” Digital transformation is not a technology problem — it is a capability evolution. When miners understand their baseline maturity, they can build the right foundations, accelerate adoption, and deliver sustainable performance improvements across production, cost, energy, and ESG metrics. I look forward to continuing conversations with industry partners who are shaping the next generation of data-driven mining. #Mining #DigitalTransformation #Soofastaei #AdvancedAnalytics #MineToMill #AIinMining #OperationalExcellence #DataStrategy #Innovation