Efficient Decision-Making Process

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

90% of strategic plans fail. Not because the ideas are bad. But because they're built backwards. Most CEOs start with tactics and actions. Then wonder why nothing connects. Here's what the best know: Strategy isn't a pyramid you climb. It's one you build from the top down. Start here: 1. Vision (Your "Why") ↳ Paint the future you want to create  ↳ Make it bold enough to inspire ↳ Keep it clear enough to follow 2. Mission (Your "What") ↳ Not just what you do ↳ But who you serve and why it matters ↳ Keep it focused and meaningful 3. Core Values (Your "How") ↳ Choose principles that guide decisions ↳ Pick values you'll actually live by ↳ Make them memorable and meaningful 4. Strategic Priorities ↳ Focus on 2-5 key areas only ↳ Choose what moves the needle ↳ Say no to everything else 5. Long-Term Goals ↳ Set specific 3-5 year targets ↳ Make them measurable ↳ Keep them ambitious but achievable 6. Initiatives ↳ Design programs that matter ↳ Create projects with purpose ↳ Connect everything back to vision 7. Strategies  ↳ Choose approaches that fit your culture ↳ Keep them flexible but focused ↳ Make sure they connect to your goals 8. Tactics  ↳ Pick methods that your team can master ↳ Choose tools that scale with you ↳ Test small, then expand what works 9. Action Plan  ↳ Break big goals into small steps ↳ Set clear owners and deadlines ↳ Create weekly check-in rhythms The truth is: Great strategy isn't about perfect plans. It's about clear direction. Start at the top. Build down with purpose. Watch your vision come alive. What’s your favorite way to build a strategic plan? P.S. Want a PDF of my Pyramid of Strategy cheat sheet? Get it free: https://lnkd.in/eVqMFi2j ♻️ Repost to help a CEO in your network. 💡 Follow Eric Partaker for more strategy insights.

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.

Yesterday I published an article in my Newsletter on Decision Velocity and I got several DMs on this on "Why is this so important?" If I had to point to the single mechanic that silently kills more transformations than any other, it would be decision velocity. Not because leaders don’t care. Not because they lack intelligence or intent. But because decision-making is where EVERY other weakness in the system shows up. When decision velocity is low, everything else degrades: • Clear strategy stalls • Operating models seize up • Technology backlogs grow • Teams lose momentum • Friction compounds • Accountability blurs • Good people disengage Decision velocity is not about speed for its own sake. It’s about keeping the system in motion. Most organisations don’t suffer from a lack of ideas !! They suffer from an inability to decide fast enough for those ideas to matter. And here’s the uncomfortable truth: Decision velocity is rarely the root cause => It’s the constraint. It exposes: • Structural ambiguity • Weak guardrails • Overloaded governance • Poor information hygiene • Risk-averse leadership behaviours • Capacity misalignment That’s why it feels (and often is) more important than some of the other mechanics. In practice, decisions are not independently made they are structurally bound. Decision velocity is only high when: • Ownership is clear • Guardrails exist • Information is trusted • Governance is designed to decide, not observe • Leaders are willing to decide with imperfect data When those mechanics are weak, decision velocity collapses first. So, is it more important than the others? No, it is often the limiting factor. In systems terms, it’s the bottleneck. Improve it, and the whole system accelerates. Ignore it, and every other improvement is throttled. That’s why, in transformation, I treat decision velocity as both: • A diagnostic • And a forcing function If decisions are slow, something else is broken. And until you fix that, no amount of strategy, technology, or effort will compensate. This is why Mechanic 6 sits where it does in the series. It's not a silver bullet, but it's the 'metric' where the organisation finds out if it’s actually ready to change. Subscribe to my newsletter https://lnkd.in/eCXTa4vE

🫙 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

In the world of leadership, making tough calls is inevitable, especially in times of uncertainty. Effective decision-making is a critical skill that can make or break a leader's success. Here are some strategies that have proven effective in my journey and can help you navigate the most challenging decisions: 1. Adopt a Robust Framework - OODA Loop (Observe, Orient, Decide, Act): This framework encourages rapid assessment and adaptation to changing conditions. It helps leaders stay agile and responsive. - Decision Matrix: Evaluate options based on criteria such as impact, feasibility, and alignment with organizational goals. This structured approach ensures comprehensive evaluation. 2. Balance Data and Intuition - Data-Driven Insights: Leverage data analytics to inform your decisions. However, don’t underestimate the power of your intuition, honed through experience and deep understanding of your field. - Scenario Analysis: Develop and analyze multiple scenarios to prepare for various potential outcomes. This helps in making informed decisions even in uncertain environments. 3. Engage a Diverse Advisory Group - Diverse Perspectives: Surround yourself with advisors from different backgrounds and expertise. Their varied viewpoints can uncover blind spots and offer innovative solutions. - Collaborative Decision-Making: Involve your team in the decision-making process. Collaboration fosters buy-in and leverages collective intelligence. 4. Maintain Flexibility and Agility - Iterative Approach: Break down decisions into smaller, manageable parts. This allows for adjustments based on feedback and evolving circumstances. - Pivot When Necessary: Be prepared to pivot if the situation demands it. Flexibility is crucial in navigating the complexities of the business landscape. 5. Focus on Long-Term Vision - Alignment with Vision: Ensure that your decisions align with the long-term vision and strategic goals of your organization. This keeps you on the right track even when immediate circumstances are challenging. - Sustainable Solutions: Aim for decisions that provide long-term value rather than quick fixes. 6. Reflect and Learn - Post-Mortem Analysis: After major decisions, conduct a thorough analysis to understand what worked and what didn’t. This continuous learning loop improves future decision-making. - Celebrate Successes and Learn from Failures: Acknowledge and celebrate your successes, but also embrace failures as learning opportunities. What strategies have you found effective in making tough decisions? #Leadership #DecisionMaking #StrategicThinking #ValueCreation #Entrepreneurship #PrivateEquity #VentureCapital #ConstructiveRebels

I'd like to share with you a powerful method that's been instrumental in our journey towards making more nuanced and balanced decisions. The Six Hat Solution, developed by Edward de Bono, is a powerful tool for teams and leaders. It's designed to help people explore different perspectives towards a complex situation or challenge, making our decision-making process more structured and comprehensive. 1. Emotional Viewpoint: Reflecting on our emotions offers initial insights. How does this situation make us feel? Personally, the prospect of our upcoming project invokes a mix of excitement and apprehension. Acknowledging our feelings can highlight potential concerns or areas of strong motivation. 2. Factual Analysis: Grounding our discussion in facts ensures a solid foundation. What are the undeniable truths of our current situation? With our project, the realities include our deadlines, budget constraints, and the resources at our disposal. These facts help clarify the scope of our challenge. 3. Optimistic Outlook: Focusing on the positives, we identify which aspects are most likely to succeed. In our scenario, the creativity and resilience of our team stand out as invaluable assets. This positivity is crucial for maintaining momentum. 4. Critical Perspective: Conversely, acknowledging what might not work allows us to anticipate and address potential issues. For us, the constraints of time and the untested nature of some technologies are concerns that need strategic planning. 5. Creative Exploration: By thinking creatively, we open the door to innovative solutions. Could adjusting our approach or incorporating new methodologies enhance our outcome? This phase pushes us beyond our initial assumptions. 6. Synthesised Solution: Finally, integrating all perspectives, we determine the most viable path forward. A phased project implementation, leveraging both proven and new technologies in stages, appears to be our best strategy. What complex decisions are you facing that could benefit from this multi-perspective approach? #leadership #mindset #culture #growth #success #problemsolving

Behaviors are learned and reinforced. To make performance evaluations more inclusive, you need to proactively craft new practices. 🧠 Unbiasing nudges, intentional and subtle adjustments I craft with my clients, can play a pivotal role in achieving an objective and inclusive performance assessment. 👇 Here is what to consider: 🔎 Key Decision Points Analyze your evaluation process to identify key decision points. In my practice, focusing on assessment, performance goal setting, and feedback processes has proven crucial. Introduce inclusive prompts at each stage to guide unbiased decision-making. 🔎 Common Biases Examine previous reviews to unearth prevailing biases. Halo/horn effects, recency bias, and affinity bias often surface. Counteract these biases by crafting nudges tailored to your organization, integrating them seamlessly into your review spreadsheets. 🔎 Behavioral Prompts I usually develop concise pre-decision checklists tailored to each organization. The goal is to support raters' metacognition and introduce timed prompts during the evaluation process. 🔎 Feedback Loops Begin with small-scale implementation and collect feedback. Compare perceptions of both raters and ratees to gauge effectiveness. 🔎 Ongoing Training Avoid off-the-shelf solutions; instead, tailor training to your organization's unique context and patterns. Your trainer should understand your specific needs and design a continuous training program that reinforces these unbiasing nudges, providing managers with the necessary competencies. 🔎 Pilot and Evaluation Define metrics to measure progress and impact. Pilot your unbiasing nudges and regularly evaluate their effectiveness. Adjust based on feedback and insights gained during the pilot phase. 👉 Crafting inclusive performance evaluations is an ongoing journey. Yet, I believe, it's one of the most important ones. Each evaluation matters as it defines a person's career and sometimes even the future. ________________________________________ Are you looking for more DEI x Performance-related recommendations like this?  📨 Join my free DEI Newsletter:

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

Planning into an uncertain future VII An alternative approach to making decisions that model the impact of a decision now on the future (that is, a form of “lookahead policy”) is to approximate the value of being in the state that a decision x_t now leads you to (and this downstream state may depend on information that does not arrive until after you make decision x_t).   There are four strategies we might use to create this approximation (see equations below):   1)   If the state variable S_t is discrete, and there are not too many states (hint – this almost never happens) we can compute the value V_{t+1}(S_{t+1}) of landing in state S_{t+1}. 2)   Since we almost never have a small number of discrete states, we can use machine learning to approximate the value of being in a state. This opens up a range of algorithmic strategies studied under the umbrella of “approximate dynamic programming” and “reinforcement learning.” However, there is still an expectation within the “max” operator which can cause problems (typically expectations can never be computed exactly). 3)   We can use an idea called the “post-decision state variable” which is the state after we make a decision, but before new information has arrived. This means that the term within the max operator is deterministic, and this opens the door to handling decisions that are vectors. 4)   Computer scientists stumbled into this strategy using an idea they call “Q-learning” where Q(s,x) is the value of being in state s and making decision x. This is based on an old idea called “cue learning” introduced in the field of psychology, illustrated by the experiments involving Pavlov’s dog.    This strategy has attracted a tremendous amount of attention, but practical applications tend to be limited to problems where it is possible to estimate reasonable approximations of the value function. This depends on exploiting problem structure or having access to extremely large numbers of estimates of the value function.   Approximating value functions using neural networks (more recently deep neural networks) is attracting considerable attention, although it is not clear how much of this work is making its way into production. I have been successful using equation (3) for complex resource allocation problems where I can exploit structure such as concavity (when maximizing) – this would never be possible with a neural network.   This material is so rich that it requires five chapters (chapters 14-18) in   https://lnkd.in/dB99tHtM (“tinyurl.com/” with “RlandSO”).