Visualizing Decision Outcomes

Data science or data analytics without storytelling is void. You can do all the SQL, all the Python, all the modeling — but if the final insight is not communicated in the right visual form, the value is lost. This cheat sheet is a perfect reminder that choosing the right chart is not decoration — it is part of analysis. It breaks the decision down by purpose of insight: 1) Composition Waterfall, Progress bar, Pie, Gauge — great when you want to show parts contributing to a whole or target progress. 2) Comparison Bar charts, Row charts, Line charts, Combo charts — useful when comparing categories or trends over time. 3) Distribution & Relationship Histogram and Scatter plot — when you want to show how values are spread or how two variables interact. 4) Stage Analysis Sankey and Funnel — ideal for visualizing drop-offs or flow across process stages. 5) Single Value KPIs Number & Trend cards — best for dashboards where one metric needs to stand out with context. The skill is not in plotting a chart — the skill is in selecting the correct one for the question being asked. Your analysis is only as powerful as the clarity of how you present it. cc Metabase #DataAnalytics #DataScience #DataVisualization #StorytellingWithData #BI #Metabase #DashboardDesign #DecisionMaking

Which heatmap methodology makes more sense 3x3 or 5x5? RAW@AI: Great question—choosing between a 3x3 or a 5x5 heatmap is something risk managers often debate. Let's first explore your context briefly: Are you using heatmaps because stakeholders or auditors expect something visual and colorful once a year, or are your executives actively making decisions based on these outputs? If it's the former—simply providing a colorful visual to tick an audit or compliance box—either 3x3 or 5x5 can serve that purpose, though a 3x3 is typically simpler and quicker for stakeholders to grasp. If your risk assessment is used for genuine strategic decisions, however, neither approach is ideal. Here's why: The core issue with qualitative matrices like 3x3 or 5x5 heatmaps is that they suffer from subjectivity and inconsistency. Different people interpret "high" or "medium" differently, and there's little meaningful difference between a risk rated as "4" versus one rated as "3". In practice, these ratings seldom translate directly into actionable insights or inform critical decisions clearly. A better approach—and here's where the evolution of your risk management practice comes into play—is to gradually shift towards more quantitative methods that directly integrate into decision-making. For example, rather than trying to fit a complex reality into arbitrary categories, you could use a decision tree or Monte Carlo simulation to quantitatively evaluate actual outcomes and their probabilities. This approach provides clearer insight into how uncertainties directly affect your objectives and shows decision-makers the real-world implications of their choices. Consider this practical example: Imagine you're evaluating the risk of operational downtime in your supply chain. Rather than rating it as "medium likelihood, high impact" on a heatmap, you could model potential downtime scenarios using Monte Carlo simulations. You'd quantify how often downtime might occur, how long it would last, and what its financial impact would be. Decision-makers then receive clear, numeric outputs such as "there's a 40% chance annual losses will exceed $500,000 under our current maintenance schedule." That kind of insight directly informs whether investing more in preventive maintenance is justified. I recall from our previous exchanges that you mentioned the importance of clearly communicating risks to executives and stakeholders. Decision trees, tornado diagrams, and simulations don't just provide clarity—they communicate risk information in the language that executives speak: dollars, timeline impacts, and strategic trade-offs. But I know switching entirely overnight might be challenging. So, perhaps consider a hybrid approach: continue briefly using your heatmap (3x3 for simplicity), while gradually introducing these more quantitative methods on a key project or decision. Over time, stakeholders will start experiencing firsthand the value of more precise and actionable data.

7 visuals any intelligence analyst can use to communicate assessments more effectively. Effective communication is as essential a skill for intelligence practitioners as collection or analysis. Without sound written, verbal, or visual communication to the decision-maker, all the time and effort invested into intelligence work is wasted. Where pictures express a thousand words, visual communication is an extremely powerful method to convey facts, logic, and judgements. The first rule is that any image is more appealing to the prospective audience than a wall of text. However, graphic design is not known as a strong point for most analysts. Here are 7 simple visuals any analyst can use: 1. Maps 2. Timelines 3. Process diagrams 4. Impact/risk/threat prioritisation (traffic lights) 5. Graphs 6. Network diagrams 7. Dashboards Some have bespoke software to aid production, but they can also be done – often quicker – with a basic understanding of powerpoint, word, and the screen snipping tool. Regardless of type, general rules for visual communication are: 1. The visual supports the message. Have a clear understanding of the key message before starting. Write out the message first, then produce the visual. 2. Basics still apply. Ensure the visual identifies the bottom line up front and clearly distinguishes between facts and assessment. 3. Keep it simple, less is more. Visuals are not an opportunity to cut and paste a written report into a powerpoint slide. Let the visuals stand in for words. 4. Measure once, cut twice. If you used a sketch or visual to help integrate or interpret the problem during analysis, that is a good indicator it will also help the decision maker to understand it. With a few tweaks and polish, you can reuse your rough drawings and save time in production. 5. Templates save time. For dashboards, maps, or storyboards, using the same format saves time. Once you find a format that works, reuse it. 6. Use visual shortcuts. There are many social and cultural visual cues you can use to aid communication. Traffic lights indicate relative threat or priority (green, orange, red). Icons like the red cross or crescent for medical, anchors for ships, lightning bolts for power. Ensure they fit the culture you're presenting to. 7. Learn some basic design hierarchy – scale, spacing, colour, positioning, and typography help prioritise information and guide the customer. This knowledge will elevate your skills well above the average analyst. Finally, iterate and innovate. The first attempt will be far from perfect, but practice will develop your technical ability and direct and indirect customer feedback will refine and home in on the best way to deliver the key message. Incorporating visual communication elevates good intelligence work into something that gets seen by decision-makers. #intelligenceanalysis #intelligencetraining #intelligenceleadership

I’ve been analyzing this HR Summary dashboard by Alice Rooney, and it is a masterclass in functional design. Too often, People Analytics becomes a "𝘥𝘢𝘵𝘢 𝘥𝘶𝘮𝘱," but Alice uses specific visualization techniques to drive executive decision-making. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝗺𝘆 𝘁𝗼𝗽 𝟯 𝗱𝗮𝘁𝗮-𝘃𝗶𝘇 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗶𝘀 𝗯𝘂𝗶𝗹𝗱: 𝟭. 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻: Instead of just showing a single "Average Salary," Alice includes a Salary Distribution bar chart. 𝗧𝗵𝗲 𝗟𝗲𝘀𝘀𝗼𝗻: Averages lie; distributions tell the truth. By showing the volume of employees in the 41K–60K bracket versus the outliers at 400K+, she provides an immediate sense of "normal" for the org. 𝟮. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗿𝗼𝘅𝗶𝗺𝗶𝘁𝘆 (𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻 𝗯𝘆 𝗗𝗲𝘀𝗶𝗴𝗻): By placing Satisfaction Rating and Performance Score side-by-side using the same 5-column layout, the dashboard forces a comparison. 𝗧𝗵𝗲 𝗟𝗲𝘀𝘀𝗼𝗻: Layout is a narrative tool. When you see a 3.0 satisfaction next to a 5.5 performance, the design itself asks the question: "Are we driving results at the cost of our culture?" 𝟯. 𝗖𝗹𝗲𝗮𝗻 𝗠𝗶𝗰𝗿𝗼-𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗖𝗼𝗻𝘁𝗲𝘅𝘁: The use of sparklines for Employee Growth and horizontal bar charts for Departmental Breakdowns keeps the UI scannable. 𝗧𝗵𝗲 𝗟𝗲𝘀𝘀𝗼𝗻: Use high-density, low-clutter visuals (like sparklines) to show trends without needing a full-sized axis for every metric. Great viz doesn't just show data—it makes the "next step" obvious. Kudos to Alice Rooney for a build that prioritizes actionable insights over visual noise. 👏

✂️ How To Map Unintended Consequences of UX Decisions (https://lnkd.in/dprq_aGc), with practical techniques to visualize, map and start planning for unintended consequences of design decisions — with systems thinking, impact ripples, iceberg visuals and feedback loops. By Martin Tomitsch and Justin Farrugia. 🤔 Not every design outcome is predictable and linear. ✅ Small changes can set large ripple effects in motion. ✅ Users don’t act in isolation; they react to feedback loops. ✅ Immediate metrics (e.g. clicks) often mask long-term impact. 🚫 We often focus on UX flows → but overlook causality, ripples. ✅ Systems Maps visualize relationships and consequences. ✅ We study direct and indirect effects of a suggested change. ✅ Quadrant Matrix → We map changes on Impact vs. Repetition. ✅ Impact Ripple → Direct impact, Indirect Impact, Big Picture. ✅ Iceberg Model → Events, Patterns, Structures, Mental Model. No design decision exists in isolation. Often we try to use linear user journey maps to understand how people use our product or go through specific flows. We measure the impact of A/B tests to see if we achieve a desired outcome and move the needle. We track conversion, clicks, engagement. In other words, we track metrics that often hide the complexities of user interactions and relationships between features and flows in our products. Complex systems often have conflicting loops — a feature that drives short-term retention might drive long-term churn or abandonment. Often these effects are delayed, invisible and appear to be highly unlikely at first. So before focusing on fine details of a feature, it's always a good idea to sit down and explore direct and indirect impact of the changes — for different user profiles, and the different workflows that users apply daily. A great reminder that as designers we are often so focused on fine little details too early — mostly to outperform the competition in some way. But we often forget that our product must excel in user's workflows with a few critical systems, dozens of other apps and hundreds of other tabs. --- ✤ Useful Toolkits and Books: Designing Tomorrow, by Martin Tomitsch, Steve Baty https://lnkd.in/dmXEZREr Thinking in Systems: A Primer, by Donella Meadows https://lnkd.in/dXbm5EEA Good Services: How to Design Services that Work, by Louise Downe https://lnkd.in/d5SigzvX The Great Mental Models, by Rhiannon Beaubien https://lnkd.in/dnT_GtDT Useful Books on Systems Thinking, by James Pomeroy https://lnkd.in/dH7d9exZ #ux #design

how to make better biz decisions in 2026: (and maximize your efforts w/ AI) AI can generate reccs, but humans still make the decisions. to make better choices faster, use the 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗦𝘂𝗿𝗳𝗮𝗰𝗲𝘀 model, a way to map all the factors that influence a decision. think of a decision surface as a 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗮𝗽 that shows:  • 𝗶𝗻𝗽𝘂𝘁𝘀: the information or data points that matter  • 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: limits you must operate within (time, budget, resources)  • 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀: the consequences of choosing one option over another visualizing these factors creates a “surface” where every possible option can be evaluated. 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: deciding on a marketing campaign  • inputs: audience size, budget, past engagement  • constraints: brand guidelines, launch deadlines  • trade-offs: reach vs. cost, creativity vs. predictability when you plot these, you can see which options are feasible, which are optimal, and where AI can help generate/rank possibilities. 𝗵𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁 𝘄/ 𝗔𝗜:  1. define the decision you need to make  2. list all inputs, constraints, and trade-offs  3. ask AI to generate options within this framework  4. evaluate options against the surface to pick the best one thinking in decision surfaces turns AI from a magic box, into a 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝗿, giving you better options faster while keeping humans in control.

11 visuals that change how you decide When choices pile up, words get noisy. Pictures cut through. Want cleaner calls, faster? Start thinking in diagrams, not paragraphs. Which of these would save you an hour this week? Here are 11 I use (and teach) on repeat: 1. Effort × Impact Matrix ↳ Do high-impact, low-effort first. Kill low-impact, high-effort. ↳ Revisit weekly so the box doesn’t lie. 2. Two-Door Map (Reversible vs Irreversible) ↳ If you can undo it, decide fast. If you can’t, slow down. ↳ Match the speed to the cost of being wrong. 3. Barbell Strategy ↳ Keep most resources in proven plays; place small, bold bets on upside. ↳ Safety + spikes beats mushy middle. 4. Regret-Minimization Timeline ↳ Look from your 80-year-old lens: what choice would you be proud of? ↳ Optimise for stories you want to tell. 5. Spheres of Control ↳ Control, influence, accept. Put energy where it compounds. ↳ Drama shrinks when the circles are honest. 6. Options Portfolio Heatmap ↳ Keep 3 live options warm; score by upside × momentum. ↳ Prune monthly so “maybe” doesn’t eat time. 7. Pre-Mortem Tree ↳ Assume it failed; list root causes; design counter-moves now. ↳ Prevention is cheaper than heroics. 8. Evidence Ladder ↳ Claim → anecdote → data → experiment → repeatable result. ↳ Move up the rung before you move the budget. 9. Opportunity-Cost Clock ↳ Every yes spends hours you can’t re-earn. ↳ Budget time like cash, protect peak hours like gold. 10. Feedback-Loop Map ↳ Short loops for learning, long loops for leverage. ↳ Sequence so you learn fast, then scale what works. 11. Energy Triangle (Time × Money × Energy) ↳ A decision that drains energy is a hidden tax. ↳ Choose the path that protects your battery. Which visual do you want as a poster above your desk? ♻️ Share this with someone who needs cleaner decisions ➕ Follow Helene Guillaume Pabis for clear, human-first playbooks ✉️ Newsletter: https://lnkd.in/dy3wzu9A

I spent years creating "beautiful" dashboards that executives ignored. Then I discovered 4 strategies that turn complex charts into decision drivers. Here's how to make your data impossible to ignore: It all started with an insight from Storytelling with Data by Cole Nussbaumer Knaflic. Your tools don't know your story. You must bring it to life. 𝗕𝗲𝗳𝗼𝗿𝗲: Hours creating fancy charts with gradients and random colors. 𝗔𝗳𝘁𝗲𝗿: Simple visuals that stakeholders actually use. 4 Core Visualization Principles: 1. Strip Chart Junk ↳ Remove unnecessary gridlines ↳ Delete pointless labels 2. Focus Single Message ↳ One insight per chart ↳ Everything else creates noise 3. Strategic Color Usage ↳ Highlight only critical data ↳ Gray out supporting information 4. Clear Takeaways ↳ State conclusions upfront ↳ Make messages obvious The transformation results in improved attention, understanding, and taking action. Your Implementation Plan: 1. Delete pointless gridlines 2. Remove unnecessary labels 3. Choose one color for key highlights 4. Write titles that state your conclusion Small adjustments create a massive impact. Which visualization principle will you implement first? Share your approach below! 📚 Resource: Storytelling with Data: https://amzn.to/4fHenmA ♻️ Repost to help others create impactful data stories

Visualizing data helps humans digest complex information 10X faster than text, yet most dashboards actually slow down decision-making. Edward Tufte's pioneering work reveals why: effective data visualization requires ruthlessly eliminating noise to amplify signal—what he calls "above all else, show the data." 1. Maximize the Data-Ink Ratio 🔍 Remove decorative elements that don't convey information. Every pixel should serve a purpose. Those 3D effects and heavy gridlines? They're actively hiding your insights. 2. Answer "Compared to What?" 📊 Tufte's favorite question drives his "small multiples" concept—mini-charts arranged side-by-side with consistent scales. When executives see monthly revenue across six product categories simultaneously, patterns emerge instantly. 3. Context Belongs On the Visualization 📝 Annotate directly on charts rather than in legends or footnotes. A small note "Promo campaign launch" on a sales spike explains more than a meeting ever could. 4. Embrace Sparklines for Trends 📈 These "word-sized graphics" pack tremendous insight alongside metrics. A tiny 30-day trendline next to "Conversion Rate" immediately conveys direction without requiring separate charts. 5. Design for Decisions, Not Aesthetics 🎯 The true test: does this visualization help someone make a better decision? If not, it needs rethinking. At SourceMedium.com, these principles guide our data visualization design, which has powered up to 30x growth for some of our customers over the years. We're now designing these principles into our AI data analyst agent to make it a seamless part of your daily workflow – no more thinking about the best way to make charts, you simply get the most effective visualizations based on your questions and preferences. This represents a fundamental paradigm shift from conventional dashboards and web apps. SourceMedium.ai doesn't just present data; it delivers insights with Tufte-inspired clarity and purpose, integrating directly into your team's communication channels. The best data visuals aren't the flashiest—they're the ones that disappear, leaving only understanding behind.

Here’s something no one talks about: The Secret to Designing Dashboards That Drive Decisions Most dashboards look good but fail where it matters—helping users take real action. Here’s what sets great dashboards apart: ✅ Start with the Story: What’s the goal? Guide users to what matters most. ✅ Tell a Story with Data: Don’t just show numbers—connect the dots to give clear insights. ✅ Focus on Outcomes: Highlight what’s working, what’s not, and what to do next. ✅ Design for Focus: Ditch the noise. Use clean visuals and clear priorities to guide decisions. ✅ Simplify Interaction: A single click should get users to where they need to act. ✅ Surface Key Metrics: Stop hiding critical data behind endless tabs—bring it upfront. ✅ Make Decisions Effortless: Design for action, not confusion—clear CTAs to drive next steps. Dashboards aren’t just visuals—they’re decision tools. Design them right, and you empower users to take action faster and smarter. Which dashboard mistake do you think people overlook the most? Let’s chat below! Check out the case study: https://lnkd.in/gDj8JYeN Dribbble: https://lnkd.in/ginHK6g4