Reviewing Progress Regularly

I've noticed a pattern. The questions HR leaders care about most and the metrics they're reporting... don't always line up. We're worried about things like: - Productivity - Engagement - Better decision-making - Retaining top talent But we're often reporting on things like: - Days in office - PTO usage - Training attendance - Dashboard usage This came up during a recent fireside chat I did with a Head of People Analytics when we were discussing how teams know whether an initiative is actually working. Here's the framework I shared: Outputs → The activities you completed Outcomes → The short-term results Impact → The long-term change you're REALLY after The challenge is that impact is usually the hardest thing to measure. It's slower.  Harder to quantify. Influenced by multiple factors. So it's natural to gravitate toward outputs and outcomes instead. BUT, the key is not to stop there. Outputs and outcomes measure your effort. Impact tells you whether it mattered. Before choosing a metric, ask: "What is the thing we're ultimately trying to change?" Then work backward from there. (Or better yet, pick 2-3 north star impact metrics for the quarter, and stack 90% of your outputs and outcomes against those.) —— 📌 Save this for the next time someone asks you, "What impact did that initiative have?" 👋 I'm Morgan. I teach HR teams to turn workforce data into insights and recommendations their leadership can act on.

“Show outcomes, not outputs!” I’ve given (and received) this feedback more times than I can count while helping organizations tell their impact stories. And listen, it’s technically right…but it can also feel completely unfair. We love to say things like: ✅ 100 teachers trained ✅ 10,000 learners reached ✅ 500 handwashing stations installed But funders (and most payers) want to know: 𝘞𝘩𝘢𝘵 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘤𝘩𝘢𝘯𝘨𝘦𝘥 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘰𝘧 𝘢𝘭𝘭 𝘵𝘩𝘢𝘵? That’s the outcomes vs outputs gap: ➡️ Output: 100 teachers trained ➡️ Outcome: Teachers who received training scored 15% higher on evaluations than those who didn’t The second tells a story of change. But measuring outcomes can be 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. It’s easy to count the number of people who showed up. It’s costly to prove their lives got better because of it. And that creates a brutal inequality. Well-funded organizations with substantial M&E budgets continue to win. Meanwhile, incredible community-led organizations get sidelined for not having “evidence”- even when the change is happening right in front of us. So what can organizations with limited resources do? 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵: That study from Daystar University showing teacher training improved learning by 10% in India? Use it. If your intervention is similar, cite their methodology and results as supporting evidence. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘀𝗶𝗺𝗽𝗹𝗲𝗿 𝘀𝘁𝘂𝗱𝗶𝗲𝘀: Baseline and end-line surveys aren't perfect, but they're better than nothing. Self-reported confidence levels have limitations, but "85% of teachers reported feeling significantly more confident in their teaching abilities," tells a story. 𝗣𝗮𝗿𝘁𝗻𝗲𝗿 𝘄𝗶𝘁𝗵 𝗹𝗼𝗰𝗮𝗹 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝘀: Universities need research projects. Find one studying similar interventions and collaborate. Share costs, share data, share credit. 𝗨𝘀𝗲 𝗽𝗿𝗼𝘅𝘆 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀: Can't afford a 5-year longitudinal study? Track intermediate outcomes that research shows correlate with long-term impact. 𝗧𝗿𝘆 𝗽𝗮𝗿𝘁𝗶𝗰𝗶𝗽𝗮𝘁𝗼𝗿𝘆 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: Let beneficiaries help design and conduct evaluations. It's cost-effective and often reveals insights that traditional methods miss. For example, train teachers to interview each other about your training program. And funders? Y’all have homework too. Some are already offering evaluation support (bless you). But let’s make it the rule, not the exception. What if 10-15% of every grant was earmarked for outcome measurement? What if we moved beyond gold-standard-only thinking? 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗮 𝗰𝗲𝗿𝘁𝗮𝗶𝗻 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗲𝗮𝗻 “𝗻𝗼𝘁 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹”. We need outcomes. But we also need equity. How are you navigating this tension? What creative ways have you used to show impact without burning out your team or budget? #internationaldevelopment #FundingAfrica #fundraising #NonprofitLeadership #nonprofitafrica

Why product roadmaps should be outcome based not feature-driven We do sprints to ship features, and they don’t always work out. Why? Because features alone don’t move the needle -outcomes do. A practice that I usually follow is to ask myself: What problem are we solving, and how will we measure success?” And that’s how we pivot from feature factories to outcome-driven roadmaps with actionable steps to make it stick. 𝗪𝗵𝘆 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 > 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 Outcome-based roadmaps focus on measurable results (e.g., “Increase free-to-paid conversion by 15%” vs. “Build a pricing calculator”). This shift: - Aligns teams around business goals, not just deliverables. - Empowers creativity (solve the problem, don’t just check a box). - Reduces waste by killing initiatives that don’t drive impact. But how do you actually make this work? Here’s My Practical Playbook 👇🏻 1️⃣ 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 “𝗪𝗵𝘆” - Define outcomes tied to business goals: Partner with leadership to align on 1-2 KPIs per quarter (e.g., “Reduce churn by 10%”). - Ask this question: “If we deliver X feature, what outcome does it enable?”. If there’s no clear answer, rethink it. 2️⃣ 𝗕𝗿𝗲𝗮𝗸 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝗶𝗻𝘁𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 Outcomes are broad—break them into testable hypotheses. - Example: To “Increase user engagement by 20%,” run:   - A/B test push notification timing.   - Pilot a gamified onboarding flow.   - Measure DAU/WAU ratios weekly. 3️⃣ 𝗔𝗱𝗼𝗽𝘁 𝗙𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 - OKRs: Link Objectives (outcomes) to Key Results (metrics). - Impact Mapping: Visualize how features connect to goals. - RICE Scoring: Prioritize initiatives by Reach, Impact, Confidence, Effort. 4️⃣ 𝗚𝗲𝘁 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗕𝘂𝘆-𝗜𝗻 - Frame outcomes as ROI: Show how “Reduce support tickets by 25%” cuts costs. - Prototype outcomes first: Share a mock roadmap with leadership, highlighting gaps in current feature-centric plans. 5️⃣ 𝗠𝗲𝗮𝘀𝘂𝗿𝗲, 𝗟𝗲𝗮𝗿𝗻, 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 - Track leading indicators (e.g., user behavior changes) alongside lagging metrics (e.g., revenue). - Celebrate “failures”: Killing a feature that didn’t drive outcomes is a win. 𝟯 𝗧𝗵𝗶𝗻𝗴𝘀 𝘁𝗼 𝗔𝘃𝗼𝗶𝗱 - - Vague outcomes: “Improve UX” → ❌ | “Reduce checkout abandonment by 20%” → ✅. - Overloading the roadmap: Focus on 1-2 outcomes per quarter. - Ignoring feedback loops: Revisit outcomes bi-weekly—adapt as data comes in. This week, try this: Audit your roadmap. For every feature, ask: “What outcome does this serve?” If it’s unclear, reframe it, or cut it. I believe outcome-based roadmaps is a survival tactic. Let’s build products that matter. 👉 How are you bridging the gap between features and impact? Would love to know your process.

𝟯 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗧𝗼 𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗲 𝗘𝗔 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝗺𝗲𝗻𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 Enterprise Architecture creates value—showing value is another discipline. In sales or operations, impact is directly measurable. EA’s contributions are often 𝗶𝗻𝗱𝗶𝗿𝗲𝗰𝘁 𝗮𝗻𝗱 𝗹𝗼𝗻𝗴-𝘁𝗲𝗿𝗺. We want to 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝗾𝘂𝗮𝗻𝘁𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗽𝗿𝗼𝗼𝗳 of EA’s success, yet traditional KPIs fall short. How to break through this measurement challenge? Here are 𝟯 𝗔𝗰𝘁𝗶𝗼𝗻𝗮𝗯𝗹𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝘁𝗼 𝗾𝘂𝗮𝗻𝘁𝗶𝗳𝘆 𝗘𝗔’𝘀 𝗶𝗺𝗽𝗮𝗰𝘁, making value undeniable: 𝟭 | 𝗗𝗲𝗳𝗶𝗻𝗲 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗖𝗲𝗻𝘁𝗿𝗶𝗰 𝗞𝗣𝗜𝘀 𝙋𝙧𝙤𝙗𝙡𝙚𝙢: EA metrics like 𝘢𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘤𝘰𝘮𝘱𝘭𝘪𝘢𝘯𝘤𝘦 or 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭 𝘥𝘦𝘣𝘵 𝘳𝘦𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝗱𝗼𝗻’𝘁 𝗿𝗲𝘀𝗼𝗻𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 leaders. 📌 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Shift the focus to 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝗮𝗹𝗶𝗴𝗻𝗲𝗱 𝗞𝗣𝗜𝘀: • 𝗧𝗶𝗺𝗲-𝘁𝗼-𝗠𝗮𝗿𝗸𝗲𝘁 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻 → How EA reduces bottlenecks in project execution • 𝗖𝗼𝘀𝘁 𝗔𝘃𝗼𝗶𝗱𝗮𝗻𝗰𝗲 → How EA reduces IT spend with standardization • 𝗥𝗶𝘀𝗸 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻 → How EA mitigates security, compliance, and technical risks ✅ 𝗜𝗺𝗽𝗮𝗰𝘁: EA shifts from overhead to a 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙚𝙣𝙖𝙗𝙡𝙚𝙧. 𝟮 | 𝗟𝗶𝗻𝗸 𝗘𝗔 𝘁𝗼 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 𝙋𝙧𝙤𝙗𝙡𝙚𝙢: EA’s strategic impact is real but 𝘰𝘧𝘵𝘦𝘯 𝘧𝘦𝘦𝘭𝘴 𝘪𝘯𝘵𝘢𝘯𝘨𝘪𝘣𝘭𝘦 in financial reporting. 📌 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Create 𝘁𝗿𝗮𝗰𝗲𝗮𝗯𝗶𝗹𝗶𝘁𝘆 between EA initiatives and financial results: • Calculate 𝗰𝗼𝘀𝘁 𝘀𝗮𝘃𝗶𝗻𝗴𝘀 from eliminating redundant systems • Quantify 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗴𝗮𝗶𝗻𝘀 from streamlined workflows and reduced rework • Show how 𝗘𝗔 𝗿𝗲𝗱𝘂𝗰𝗲𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗿𝗶𝘀𝗸, avoiding downtime or fines ✅ 𝗜𝗺𝗽𝗮𝗰𝘁: EA is positioned as a 𝗱𝗿𝗶𝘃𝗲𝗿 𝗼𝗳 𝗥𝗢𝗜, not an advisory function. 𝟯 | 𝗨𝘀𝗲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝗶𝗲𝘀 𝙋𝙧𝙤𝙗𝙡𝙚𝙢: EA leaders struggle to 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝘃𝗮𝗹𝘂𝗲 𝗰𝗼𝗺𝗽𝗲𝗹𝗹𝗶𝗻𝗴𝗹𝘆. 📌 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Leverage 𝘀𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 with before-and-after case studies: • Show how 𝗘𝗔 𝗲𝗻𝗮𝗯𝗹𝗲𝗱 𝗮 𝘀𝘂𝗰𝗰𝗲𝘀𝘀𝗳𝘂𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 • Highlight projects where 𝗘𝗔 𝗽𝗿𝗲𝘃𝗲𝗻𝘁𝗲𝗱 𝗰𝗼𝘀𝘁𝗹𝘆 𝗺𝗶𝘀𝘀𝘁𝗲𝗽𝘀 • Use tangible examples to make EA’s impact clear for leadership ✅ 𝗜𝗺𝗽𝗮𝗰𝘁: EA’s role becomes 𝘃𝗶𝘀𝗶𝗯𝗹𝗲, 𝗿𝗲𝗹𝗮𝘁𝗮𝗯𝗹𝗲, 𝗮𝗻𝗱 𝗶𝗻𝗱𝗶𝘀𝗽𝗲𝗻𝘀𝗮𝗯𝗹𝗲. 🚀 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 𝗜𝗳 𝘆𝗼𝘂 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 EA’s impact, 𝘆𝗼𝘂 𝗺𝗮𝗸𝗲 𝗶𝘁 𝘃𝗶𝘀𝗶𝗯𝗹𝗲. 🔹 Focus on 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗞𝗣𝗜s 🔹 Link EA work to 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 🔹 Use 𝘀𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 to make EA’s value undeniable 💡 How does your organization measure EA success? Let’s discuss. 👇 ➕ Follow Kevin Donovan, ring the bell 🔔 👍 Like | ♻️ Repost 🚀 Join Architects' Hub! Subscribe 👉  https://lnkd.in/dgmQqfu2 #EnterpriseArchitecture #KPIs #BusinessValue

I audited 464 of my AI agent sessions. The numbers were worse than I expected. 1.2B tokens. $255.73 in spend. 26% subagent failure rate. 97% of sessions never compacted. One session had 39 consecutive tool errors. One broken command retried 12 times with no stdin. The embarrassing part: all of this data was already sitting in my logs. I had spent months improving prompts, trying models, tweaking configs, and building workflows. But I had not built the habit that actually matters once agents become part of daily work: Read the system. Not vibes. Not demos. Not "this felt faster." Actual logs: 1. Where did the agent spend tokens? 2. Which tools failed repeatedly? 3. Which subagents completed useful work? 4. Where did context management break? 5. Which sessions should have stopped earlier? This is why I think harness engineering is becoming the successor to prompt engineering. The bottleneck is no longer "can the model do the task?" The bottleneck is whether the system around the model creates visibility, constraints, feedback loops, and stop conditions. I wrote up the full audit here: https://lnkd.in/gTn9WA-y If your team uses AI coding agents, what are you actually measuring: model quality, or the workflow around the model?

“What gets measured gets managed.” — Peter F. Drucker This oft-cited axiom by the father of modern management, is more than a business truism. It is a call to intentional governance—a reminder that what we choose to observe becomes a reflection of our strategic priorities. Nowhere is this principle more consequential or more underleveraged than in the realm of TD Too often L&D strategies are guided by intuition, anecdote, or calendar-based rituals rather than data. We invest in leadership journeys, behavioral modules & capability academies yet struggle to articulate the causal link between our initiatives & business performance. This is not a measurement failure. It is a measurement avoidance. As TD professional & a current doctoral candidate in Business Administration—I have come to realize that without a robust architecture of metrics, we risk reducing learning to a “feel-good” function rather than a force multiplier. In business, that which is not measured becomes invisible. And invisibility breeds irrelevance. To be treated as strategic, TD must learn to speak the language of the business—a language steeped in data, outcomes & evidence. This means: Linking learning interventions to capability uplift Measuring behavioral change, not just completion Tying development to talent retention, engagement & readiness Correlating leadership programs with succession pipeline health Moving beyond vanity metrics toward business-aligned KPIs “We cannot improve what we cannot see, & we cannot defend what we cannot quantify.” My research in Business Administration has further clarified: organizations are systems of interdependencies & measurement is the currency that makes those systems intelligible. When we quantify talent outcomes—be it through ROI models, capability indices, or predictive analytics—we are not just measuring learning. We are codifying value. We are translating soft skills into hard currency—an act that elevates L&D from operational to strategic, from reactive to anticipatory. The goal is not to reduce people to numbers. It is to ensure that people strategies earn their rightful seat at the strategic table. If we measure engagement, it improves. If we measure manager effectiveness, it strengthens. If we measure internal mobility, it accelerates. Measurement doesn’t dilute the human experience—it amplifies our ability to serve it with clarity, consistency, and conviction. A Call to TD Leaders If TD is to be the engine of agility, innovation, & culture—then it must also be the custodian of strategic measurement. Let us embrace: Data literacy as a core L&D competency KPIs that resonate beyond HR dashboards A mindset that sees evaluation not as audit, but as advocacy Because what gets measured doesn’t just get managed—it gets the respect, resources, and relevance it deserves. #TalentDevelopment #PeterDrucker #LearningMetrics #StrategicHR #DBA #HumanCapital #CapabilityBuilding #WorkforceStrategy #DoctoralResearch #Future

Most healthcare providers say they care about outcomes. But their systems are still designed around visits, not the patient journey. Real value-based care starts with this mindset shift: You’re not treating a visit. You’re managing a care journey. So, what should providers actually do to make that real? 1. Map the care journey Start with key cohorts—e.g., diabetic seniors or post-acute care patients. Ask: What does a good 6-month journey look like? Then map it backwards. What data, interventions, and check-ins are needed? 2. Expand the data lens Clinical data is just 50% of the story. You need SDOH (housing, food, income), behavior (adherence, mood), and context (caregivers, home support). 3. Stratify risk proactively Don’t wait for ER visits. Build simple models that combine clinical risk + social risk. Then segment patients into high, rising, and stable risk groups. Use AI to predict who's likely to fall through the cracks. 4. Close the loop with AI AI should surface next-best-actions: Who needs a nudge today? What’s changing in their baseline? What care gaps are widening? Think of AI not as a tool, but as a teammate, watching the journey 24/7. 5. Build a longitudinal feedback loop If you don’t measure outcomes across time, you’re blind. Use dashboards that show: Outcome trends per patient cohort, ROI on interventions, Impact of addressing SDOH. At Inferenz, our mission is clear: Help providers operationalize the care journey using data, AI, and human-centered design. Because value-based care isn’t a future model: it’s an execution challenge. And we’re building the rails to make it real.

Gemba Walk: Understanding the Process Where It Actually Happens In continuous improvement, reports, dashboards, reviews, and escalation notes are important. But they rarely show the complete reality of how work actually happens. That reality is visible at the Gemba the real place where work is performed, value is created, and process behaviour becomes visible. A Gemba Walk is not a fault-finding visit. It is a structured leadership practice to observe the process with clarity, respect, and curiosity. It helps leaders understand: How work flows Where delays occur Where handoffs become weak Where rework is generated Where standards are followed or unclear Where quality, safety, or compliance risks emerge Where the team needs better support Where improvement opportunities exist A meaningful Gemba Walk begins with a clear purpose. Are we trying to understand a recurring deviation? Review process performance? Verify standard work? Identify bottlenecks? Assess quality, safety, compliance, or delivery gaps? Once the purpose is clear, observation becomes sharper. The best insights come when the process is observed live during actual execution. This is when variations, constraints, workarounds, communication gaps, waiting time, and hidden inefficiencies become visible. The focus should remain on the process, not on personalities. The most useful questions are often simple: What is expected to happen? What is actually happening? Where is value being created? Where is time being lost? What makes the work difficult? What can be simplified, standardized, or improved? The real value of a Gemba Walk begins after the observation. Capture facts. Prioritize gaps. Assign owners. Define timelines. Follow up visibly. Convert learning into CAPA, training, standardization, risk reduction, or improvement actions. Without follow-up, a Gemba Walk remains only a visit. With disciplined follow-up, it becomes a strong improvement tool. In manufacturing, laboratories, quality systems, R&D, supply chain, and service operations, Gemba helps connect leadership decisions with ground reality. Reports show outcomes. Gemba shows the conditions behind those outcomes. A good Gemba Walk brings clarity. A better one builds ownership. A mature one strengthens the system. Gemba is not about supervision. It is about understanding the process before improving it. #GembaWalk #LeanSixSigma #Kaizen #ContinuousImprovement #QualityManagement #OperationalExcellence #DMAIC #ProcessExcellence #QualityAssurance #QualityControl #RCA #CAPA #ManufacturingExcellence

How do you measure a system changing? It's not easy, but it is possible. As evaluators, strategists and funders increasingly focus on systems change, there’s growing urgency to move beyond traditional evaluation methods. A recent report from The Freedom Fund offers clear, practical insights - grounded in anti-slavery work - that are deeply relevant for any systems change effort. Here are three takeaways that stood out for me 👇 1️⃣ The challenge (Why systems change is hard to measure) - Systems are dynamic and constantly shifting, making it difficult to map clear cause and effect. - Power, a core lever of change, is relational and hard to quantify. - Traditional methods often overlook key perspectives, especially of those most affected. This complexity demands new tools and new mindsets. 2️⃣ The opportunity (What successful measurement looks like) - Start with systems thinking: co-design your theory of change with the system in mind. - Use a mixed-methods approach, especially qualitative tools to surface nuanced change. - Prioritise diverse, intersectional voices, especially those traditionally excluded. - Embrace learning from unintended outcomes as well as intended ones. Systems change takes time. Measurement must too. 3️⃣ The toolkit (How to measure what matters) The report highlights a range of approaches—some of my favourites include: - Outcome Harvesting – work backwards from real-world change. - Most Significant Change – uncover what matters most to those affected. - Process Tracing – test causal pathways. - Social Network Analysis – visualise relationships and influence. - SenseMaker® – blend story and data to make sense of messy change. If you're navigating systems change, whether in modern slavery, health equity, climate, or beyond, this report is worth a read.

Over the years working in chemical processing, one of the recurring challenges I’ve faced is with heat exchangers. They are essential for energy efficiency, but even minor issues can create significant downtime and cost. Not long ago, we encountered a serious fouling issue in one of our exchangers. The deposits were reducing heat transfer efficiency, causing higher energy consumption and forcing frequent shutdowns for cleaning. 🔍Instead of treating it as just another maintenance task, we carried out a detailed root cause analysis: • Reviewed process conditions and flow patterns. • Checked velocity and temperature profiles. • Involved both the operations and maintenance teams in the discussion. The findings showed that low fluid velocity was the main driver for fouling. By redesigning the piping layout and adjusting the operating parameters, we were able to: ✅ Increase turbulence and reduce fouling. ✅ Extend cleaning cycles from every 3 months to once a year. ✅ Achieve over 15% improvement in efficiency. For me, the key takeaway is that every technical problem is also an opportunity to innovate and improve reliability. Collaboration and data-driven decisions can transform a recurring issue into a long-term success.