Efficient Decision-Making Process

When you start comparing options across many criteria, something surprising happens: suddenly almost everything looks optimal 🤔 When taking decisions where there are multiple targets, a popular #DataScience way of deciding between options is finding those that are "Pareto optimal": those options where there isn't another option that is better in every way. This set is useful because it highlights the trade-offs you need to consider, rather than overwhelming you with possibilities. 🏠 For example, when house-hunting, there are multiple features you could consider. If you look at just one feature (say the size of the house), then there will be a single optimal house: the one that's biggest in the data you're looking at. 📈 If you add another feature, say the price, then the set of Pareto optimal houses grows: now there are several (shown in red) where no other house is both cheaper and larger. 💥 In the data I collected in my local area (via Rightmove), using just three features means that nearly 1/4 of the houses are already Pareto optimal - and by 10 features, nearly all of them are! This happens because the fraction of Pareto optimal points rises quickly with the number of features - in fact, to keep the fraction manageable you'd need exponentially more data as you add more criteria. So in real-world decision-making with many criteria, almost everything looks "optimal", and you need to use another method to actually choose. The bottom-right plot here shows that this empirical, very much non-random housing data even roughly agrees with the theoretical expectation for uniformly random data 🚀 #DataScience #MachineLearning #Optimization #DecisionMaking

How do you measure systems change? A recent report by the Freedom Fund identifies 9 methods that are particularly well-suited to measuring complex, non-linear systems change — especially in contexts like modern slavery, advocacy, and power shifting. Here’re the leading approaches: 1️⃣ Outcome Harvesting - Start with the change, then trace back to identify contributing interventions. Great for capturing both intended and unintended outcomes. 2️⃣ Most Significant Change (MSC) - Collects stories of change from stakeholders, then collaboratively decides which are most significant. Ideal when change is unpredictable. 3️⃣ Process Tracing - Tests causal pathways between interventions and outcomes. Useful for examining whether theories of change hold up. 4️⃣ Narrative Assessment - Co-creates stories with advocates to unpack the how and why of change, with a focus on decision-making and strategy. 5️⃣ Ripple Effects Mapping (REM)- Participatory mapping of a project’s wider impact. Visualises intended and unintended effects across a system. 6️⃣ Bellwether Method - Uses interviews with influential actors to gauge whether an issue is gaining traction in public discourse or policy. 7️⃣ SenseMaker® - Gathers micro-narratives from diverse voices and lets participants interpret their own stories, blending qualitative and quantitative insights. 8️⃣ Social Network Analysis (SNA) - Maps relationships and influence within a system, highlighting enablers and blockers of change. 9️⃣ General Elimination Methodology - A structured way to rule out weak causal explanations, narrowing down to the most convincing evidence for what caused change. These methods help evaluators move beyond metrics to capture shifts in relationships, power and norms — the essence of systems change. Source of images: https://lnkd.in/eesfmqUM

Measuring what works in conservation Conservation has never lacked ideas. Protected areas, payments for ecosystem services, community management, certification schemes, and public campaigns have all been promoted as responses to biodiversity loss. What has often been missing is reliable knowledge about how well these interventions work, for whom, and under what conditions. A growing body of research argues that answering those questions requires moving beyond counting activities to determine whether outcomes can truly be attributed to conservation actions. Recent commentaries highlight this shift. One warns that scarce funds may be directed toward “well-intentioned but ineffective efforts” without stronger causal evidence. Another argues that biodiversity policy suffers from an “evidence problem,” with many interventions not grounded in robust research. Together, they reflect a field attempting to move from persuasion to proof. Traditional conservation monitoring tracks trends such as forest cover or species abundance. These indicators are useful but do not reveal why change occurred. A forest might remain intact because of protection, or because it lies far from roads & markets. Impact evaluation addresses this uncertainty by asking what would have happened without the intervention (the counterfactual). Because this alternative reality cannot be observed directly, researchers approximate it using comparison groups or statistical methods. Establishing causation is difficult in complex socio-ecological systems. Protected areas, for example, are rarely placed randomly; they are often located where deforestation pressure is already low. Studies that fail to account for this selection bias can overestimate effectiveness. More rigorous approaches frequently produce smaller but more credible estimates of impact. To address these challenges, conservationists increasingly borrow methods from economics & public health. Randomized controlled trials offer the strongest evidence but are often impractical or unethical. Quasi-experimental techniques attempt to construct credible counterfactuals when experiments are not feasible. No single method suits every context, and evaluation needs evolve as projects mature. Evidence gaps remain substantial. Many strategies have been studied unevenly across regions, and practitioners often lack the resources to interpret complex analyses. Institutional incentives can also discourage rigorous evaluation, as organizations may feel pressure to demonstrate success rather than uncertainty. Despite these obstacles, the emerging consensus is pragmatic. Not every project requires a randomized trial, but most benefit from a clear theory of change & systematic learning. Biodiversity loss continues at a pace that leaves little room for ineffective interventions. Determining what works will not solve the crisis on its own, but without that knowledge, even well-funded efforts risk missing their mark.

🫙 Bottlenecks are the most disruptive parts of any company. They are also the best opportunity to make an impact and build confidence for designers ↓ Many designers are perceived as difficult, annoying people. We ask for access to users, we question already made decisions, we raise red flags for “bad” assumptions and tend to defend users against “evil forces” of the business. We also disrupt the status quo, instigate changes and have very, very strong opinions (and rightfully so!). No wonder designers often don't have a lot of trust in the beginning of a project. So we need to build up trust and confidence in our work first. We need to explain that we want to help, rather than disrupt; to simplify without oversimplifying; to streamline work without breaking existing habits. And typically my journey to address it is by focusing on bottlenecks. Bottlenecks are hidden and disruptive problems in organizations. Every unit has one. They are well-known and obvious to employees, but invisible to senior managers as they are detached from daily operations. Too often bottlenecks are a rule, rather than an exception: 1. Poorly structured meetings → a lot of opinions, but little impact 2. Restrictive rules/requirements → delayed delivery, poor quality 3. Employees always stressed → promises rarely kept 4. Heavy dependency on “best people” → massive delays, idle time 5. Slow and unstable decisions → no trust within teams/units 6. The culture of daily firefighting → cutting corners in wrong places 7. Fragmented, broken “flow” time → little time to do the work 8. Heavy technical/design debt → no innovation, poor workflows 9. Conflicting interests/priorities → extreme frustration, quiet quitting 10. “We’ve always done it this way” → decisions can’t be questioned Often you'll be blocked, but every now and again you can spot an opening. Ideally, it's difficulties that affect a lot of people — from moderating poorly organized meetings to enabling access to data or users, to clearing up conflicting priorities. And sometimes it's just a better way to organize work. Even little things like folder organization or new defaults can go a long way. So I ask around, listen, pay attention and take notes. Eventually bottlenecks start emerging, and it's a great opportunity to take action. And it starts with small pilots and little experiments — in the team where you currently are. Frequent solutions: 1. Improve project kick-off meetings 2. Refine default settings (Miro, Teams etc.) 3. Clarify and visualize roles/responsibilities 4. Design better overlaps for designers/devs 5. Distribute critical skills owned by "best people" 6. Establish and design rituals (e.g. focus times, retros) 8. Build relationships with sales, customer success Every now and again, small consistent changes will bring people on your side, and can bring along large seismic shifts at scale. You just need patience, persistence, and finding and exposing meaningful problems to solve. #ux

If you’re scaling fast and things feel a little…wobbly, you’re not alone. It tends to feel like this: → Roles and goals are getting fuzzy. → Decisions are slowing down. → And you’re starting to feel the pressure of being in everything, all the time. Yep, I see you. And I believe your 1-1s could be the secret sauce to scaling smoothly. Yeah, those chats you’re already having with your team? They’re not just great for trust, feedback, and psychological safety (big fat YES PLEASE to all of that). They’re also one of the most underused tools for designing your company Operating System. Because when used right, your 1-1s can: ➤ Reveal where systems are breaking ➤ Clarify who owns what ➤ Spot decision bottlenecks ➤ Uncover the real culture at play And, they help you reduce that old founder dependence that's keeping you deep in the detail. So, let’s upgrade your 1-1s into a design tool that make your conversations a goldmine for building connection AND systems, clarity, and culture: 1️⃣ Map the full work journey Why? You’re not just collecting feedback about what's working and not working you’re designing workflows that scale. 💬 Example question: “Walk me through a recent project, from idea to delivery. What helped or got in the way?” 🟰 This helps you design repeatable, scalable ways of working, by making invisible systems visible. 2️⃣ Uncover decision friction Why? If your team’s always waiting for founder input, you’re stuck (and likely stressed). 💬 Example question 1: “When you’re unsure, who do you go to?” 💬 Example question 2: “What decisions do you wish you could make yourself?” 🟰 Use this to design smarter decision rights and autonomy levels. 3️⃣ Spot org debt early Why? Duplication. Gaps. Confusion. They creep in fast. 💬 Example question 1: “Where is it unclear who owns what?” 💬 Example question 2: “Where do you feel like you’re reinventing the wheel?” 🟰 These insights shape roles, boundaries, and team structure. 4️⃣ Decode cultural signals Why? Operating systems are processes AND patterns of behaviour. 💬 Example question 1: “What gets rewarded here?” 💬 Example question 2: “What feels off - something we say we value, but don’t really practice?” 🟰 Perfect for informing rituals, values-in-action, and behavioural norms. See what’s happening? By bringing some of these questions into your 1:1s (FYI, you don't need to ask them all at once, that would be INTENSE) you can build connection, and co-design the system to ensure smooth scaling. Every 1-1 is a design input. Use it to create the systems, clarity, and culture that scale with you. #Scaling #companyOperatingSystem #HighPerformanceTeams ------ Hi 👋 I'm Alicia, co-founder of The Future Kind. We collaborate with founders, C-suite and People Ops leaders to design company operating systems that scale. Want to know more? Follow along or DM me, I love to hear form you. 💌

𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: 𝗪𝗵𝘆 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗗𝗼𝗻’𝘁 𝗙𝗮𝗶𝗹 𝗳𝗼𝗿 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗗𝗮𝘁𝗮 — 𝗧𝗵𝗲𝘆 𝗙𝗮𝗶𝗹 𝗳𝗼𝗿 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝘁𝗶𝗼𝗻 Most organizations proudly say “𝘞𝘦 𝘩𝘢𝘷𝘦 𝘵𝘰𝘯𝘴 𝘰𝘧 𝘥𝘢𝘵𝘢.”  But the truth?  They’re stuck not because of 𝗱𝗮𝘁𝗮 𝘀𝗰𝗮𝗿𝗰𝗶𝘁𝘆, but because of 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝘁𝗶𝗼𝗻 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 that slow every decision. 𝘏𝘦𝘳𝘦 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘧𝘰𝘶𝘳 𝘣𝘪𝘨𝘨𝘦𝘴𝘵 𝘳𝘰𝘢𝘥𝘣𝘭𝘰𝘤𝘬𝘴 𝘐 𝘴𝘦𝘦 𝘪𝘯 𝘤𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘦𝘷𝘦𝘳𝘺 𝘥𝘢𝘺: 1️⃣ 𝗦𝗶𝗹𝗼𝗲𝗱 𝗱𝗮𝘁𝗮 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 - Every department guards its own spreadsheets, so no one gets a full picture. Data becomes territory, not a shared asset. 2️⃣ 𝗗𝗲𝗹𝗮𝘆𝗲𝗱 𝗠𝗜𝗦 𝗰𝘆𝗰𝗹𝗲𝘀 - By the time reports arrive, the moment to act has already passed. Yesterday’s numbers can’t drive today’s decisions. 3️⃣ 𝗡𝗼 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 - Data without context is just noise. When reports miss the “so what,” leaders struggle to translate insights into action. 4️⃣ 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗿𝗲𝗽𝗼𝗿𝘁 - When teams derive different conclusions from the same dashboard, alignment breaks instantly. The fix isn’t “𝘮𝘰𝘳𝘦 𝘥𝘢𝘵𝘢.” It’s 𝗯𝗲𝘁𝘁𝗲𝗿 𝗰𝗹𝗮𝗿𝗶𝘁𝘆, 𝘁𝗶𝗴𝗵𝘁𝗲𝗿 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝘁𝗶𝗼𝗻 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲. 𝘏𝘦𝘳𝘦’𝘴 𝘸𝘩𝘢𝘵 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘴𝘱𝘦𝘦𝘥𝘴 𝘶𝘱 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯 𝘷𝘦𝘭𝘰𝘤𝘪𝘵𝘺: ✔️ Unifying data sources into a single truth ✔️ Shorter, faster MIS loops ✔️ Reports that tell a story, not just show numbers ✔️ Standardized interpretation guidelines so teams act in sync 🔍 The question isn’t “𝗗𝗼 𝘄𝗲 𝗵𝗮𝘃𝗲 𝗱𝗮𝘁𝗮?” — 𝗶𝘁’𝘀 “𝗔𝗿𝗲 𝘄𝗲 𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝗲𝗻𝘀𝗲 𝗼𝗳 𝗶𝘁 𝗳𝗮𝘀𝘁 𝗲𝗻𝗼𝘂𝗴𝗵?” 👉 𝙃𝙤𝙬 𝙙𝙤 𝙮𝙤𝙪 𝙨𝙚𝙚 𝙤𝙧𝙜𝙖𝙣𝙞𝙯𝙖𝙩𝙞𝙤𝙣𝙨 𝙞𝙢𝙥𝙧𝙤𝙫𝙞𝙣𝙜 𝙙𝙚𝙘𝙞𝙨𝙞𝙤𝙣 𝙫𝙚𝙡𝙤𝙘𝙞𝙩𝙮? #DataAnalytics #DecisionIntelligence #BusinessInsights #MISReporting #DataDrivenDecisionMaking

The Hidden Bottleneck in Finance: Scaling People vs. Scaling Decisions Finance is built on decision-making, yet the majority of time is spent preparing for decisions, not making them. Private credit is a prime example. The market grew from $300B to $2T+ in just three years. But firms didn’t scale their underwriting teams at the same pace. They couldn’t. So what happened? Analysts now operate at 2-3x capacity, spending nights sifting through unstructured data—not because it makes them better investors, but because it’s the only way to keep up. The problem isn’t a lack of talent. It’s how we use that talent. Look at the chart attached. In 2020 PE employed 12m people and spend $900bn in wages. Assuming this increased in lock-step with PE AUM, this number would be ~20 million people and $1.4 trillion in wages! And how do most of these professionals spend their time: 📄 Extracting data from PDFs 📊 Reconciling numbers across spreadsheets 📜 Formatting reports to match templates None of this is decision-making. None of this compounds knowledge or insight. The solution isn’t more people—it’s changing the unit of scale. Historically, we scaled finance by adding headcount. But today, scaling means moving from people-driven workflows to intelligence-driven workflows. AI isn’t just about speed. It’s about redefining the work itself. The question isn’t whether AI can do the manual tasks. The question is: What does finance look like when no one has to do them anymore? #AI #AIAgents #Finance

Last year, MIT reported that 95% of enterprise AI pilots delivered no measurable financial return. A recent Forbes CIO article noted that Carnival Cruise Line had run more than 100 GenAI pilots. Six reached production. The typical response is to double down on execution: • Invest in better tooling • Upskill teams • Accelerate deployment Those matter. But in many organisations, the deeper issue appears earlier — in evaluating idea viability before committing engineering capacity. Implementations are often approved before propositions are economically stress-tested. Formal governance exists. Stage gates exist. Training is in place Portfolio reviews exist. Yet product selection decisions at the ‘first mile’ can still be influenced by stakeholder personal opinion, technology trend pressure, or perceived ease of implementation. By the time a pilot is deemed unsuccessful, significant management attention and engineering capacity and financial has already been consumed. The question for decision makers is not simply: “How do we improve product delivery?” It may be: “Are we applying sufficient economic discipline at the point of idea selection?” “Are there data driven tools to help us do this effectively?” In regulated sectors, weak selection carries additional risk — compliance exposure, customer vulnerability, reputational impact. As pilots become cheaper to launch but more expensive to scale, the first mile of innovation becomes strategically significant. I explore this idea — including approaches such as simulated market screening before implementation and aligning team upskilling more to P&L— in my latest Medium article and in Demystifying Digital Transformation (Springer, 2024). https://lnkd.in/es8sJjWS For those overseeing enterprise product portfolios: Where is the real bottleneck today — execution capacity, or pre-build selection rigor? #DigitalTransformation #AITransformation #AI #Digitalbusinesstransformation #TechnologyStrategy #Demystifydigital

**The Hidden Cost of Being the Decision Bottleneck (And 4 Questions to Fix It)** I spoke with a VP last week who felt like a human router. Her day was a series of back-to-back meetings where every conversation ended with, "Let me get back to you on that." She had a capable, experienced team that was completely gridlocked, waiting for her approval on decisions they could - and should - be making themselves. This is a classic leadership paradox. We hold onto decisions believing we're protecting quality or managing risk. In reality, we're creating a bottleneck that slows momentum and, worse, stunts our team's growth. New research in Harvard Business Review offers a powerful framework to break this cycle. It's not about delegating more; it's about delegating smarter. The key is asking four specific questions: → Who is closest to the action? The salesperson who just got off a client call often has clearer insight than an executive reviewing a report. Their proximity offers a clarity you can't get from a distance. → Is this a pattern decision? If you've made this type of call before—approving discounts, prioritizing features—it's a candidate for a system, not your personal attention. Routinize and hand it off. → Whose perspective would lead to a better answer? Don't default to hierarchy. The sharpest insight might come from a junior engineer or a customer service rep. Expertise trumps title. → Where is momentum stalled? Sometimes, the biggest risk isn't a wrong decision—it's no decision at all. Identify who can break the logjam and empower them to move forward. Delegation isn't about losing control. It's about extending it. Every decision you delegate with intention is an investment in your team's capability and your own strategic capacity. What's one decision you're holding onto that someone on your team could own and grow from? #leadership #delegation #talentmanagement #leadershipdevelopment Source: "Should You Delegate That Decision? Ask These 4 Questions" by Cheryl Strauss Einhorn, Harvard Business Review (August 2025)

🏥 Health Center Suitability Analysis for Bishnupur Subdivision: A GIS-Based Approach 🗺️ Access to healthcare is a critical component of sustainable urban planning. This study focuses on identifying the most suitable locations for establishing health centers in Bishnupur Subdivision, ensuring equitable healthcare access through GIS-based Multi-Criteria Decision Analysis (MCDA). 🔍 Why is this important? Healthcare infrastructure planning must be strategic and data-driven to cater to growing populations, reduce accessibility gaps, and optimize resources. This analysis helps identify locations that maximize accessibility while minimizing environmental and logistical constraints. 📌 Methodology Used: A Weighted Overlay Analysis (WOA) was conducted using: ✅ Slope(Degrees) – Flat terrain preferred for infrastructure development (Weight: 10%) ✅ Land Use Land Cover (LULC) – Avoids unsuitable areas like forests or water bodies (Weight: 20%) ✅ Distance from Rivers (Meters)– Ensures selection of flood-free areas (Weight: 15%) ✅ Distance from Roads (Meters)– Guarantees better connectivity and accessibility (Weight: 20%) ✅ Proximity to Existing Health Centers(Meters) – Avoids redundancy and ensures even distribution (Weight: 15%) ✅ Distance from Settlements(Meters) – Prioritizes accessibility for residents (Weight: 20%) 📊 Findings & Future Prospects: Highly suitable areas are concentrated in low-slope zones with proximity to major roads and settlements, ensuring easy accessibility and infrastructure feasibility. The analysis eliminates unsuitable regions, such as steep slopes, flood-prone zones, and forested lands, ensuring a strategic and sustainable approach. This GIS-based approach provides actionable insights for urban planners and policymakers to enhance healthcare accessibility in Bishnupur subdivision. Future studies could incorporate demographic trends, climate risk assessment, and socio-economic factors for more refined analysis. 📢 How can GIS contribute to better urban health planning? Let’s discuss! 💬 I am a student, still learning and exploring the vast world of geospatial analysis. If you find any errors or have suggestions for improvement, I’d love to hear your insights! 🌍📚✨ #GIS #HealthcarePlanning #HealthCenters #SpatialAnalysis #UrbanPlanning #SustainableDevelopment #PublicHealth #GeospatialAnalysis #SmartInfrastructure #Bishnupur