Using Data to Improve Student Outcomes

Many amazing presenters fall into the trap of believing their data will speak for itself. But it never does… Our brains aren't spreadsheets, they're story processors. You may understand the importance of your data, but don't assume others do too. The truth is, data alone doesn't persuade…but the impact it has on your audience's lives does. Your job is to tell that story in your presentation. Here are a few steps to help transform your data into a story: 1. Formulate your Data Point of View. Your "DataPOV" is the big idea that all your data supports. It's not a finding; it's a clear recommendation based on what the data is telling you. Instead of "Our turnover rate increased 15% this quarter," your DataPOV might be "We need to invest $200K in management training because exit interviews show poor leadership is causing $1.2M in turnover costs." This becomes the north star for every slide, chart, and talking point. 2. Turn your DataPOV into a narrative arc. Build a complete story structure that moves from "what is" to "what could be." Open with current reality (supported by your data), build tension by showing what's at stake if nothing changes, then resolve with your recommended action. Every data point should advance this narrative, not just exist as isolated information. 3. Know your audience's decision-making role. Tailor your story based on whether your audience is a decision-maker, influencer, or implementer. Executives want clear implications and next steps. Match your storytelling pattern to their role and what you need from them. 4. Humanize your data. Behind every data point is a person with hopes, challenges, and aspirations. Instead of saying "60% of users requested this feature," share how specific individuals are struggling without it. The difference between being heard and being remembered comes down to this simple shift from stats to stories. Next time you're preparing to present data, ask yourself: "Is this just a data dump, or am I guiding my audience toward a new way of thinking?" #DataStorytelling #LeadershipCommunication #CommunicationSkills

Ever presented rock-solid research only to hear "Thanks, but we're going with our gut on this one"? Securing stakeholder buy-in is rarely about the quality of your work. It's about something deeper. When you’re dealing with a research trust gap, ask yourself 5 questions. 👽 Are you speaking alien to earthlings? When you say jargon like "double diamond" or "information architecture," your stakeholders hear gibberish. Business leaders didn't learn UX in business school—and most never will. Translate everything into business outcomes they understand. Revenue growth. Customer retention. Cost savings. Competitive advantage.  Speak their native language, not yours. ⏰ What keeps them awake at 3am? Behind every skeptical question is a personal fear. That product manager who keeps shooting down your findings? They're terrified of missing their KPIs and losing their bonus. Have honest conversations about what they're personally on the hook for delivering. Then show how your research helps them achieve exactly that. ❓Are you treating assumptions as facts? You might think you know what questions matter to your stakeholders. You're probably wrong. Before starting research, explicitly ask: "What questions do you need answered to make this decision?" Then design your research to answer exactly those questions. ⚒️ Are you dying on the hill of methodological purity? Sometimes you have 8 hours for research instead of 8 weeks. Being dogmatic about "proper" research methods doesn’t always pay off. Focus on outcomes over process. If quick-and-dirty gets reliable insights that drive decisions, embrace it. 🍽️ Are you force-feeding them a seven-course meal when they wanted a snack? Executives need 30-second summaries. Product managers need actionable findings. Junior team members need hands-on learning. Tailor your approach to each one. You can also use my stakeholder persona mapping template here: https://bit.ly/43R7wom What’s the best advice you’ve heard about dealing with skeptical stakeholders?

How do you make your data science results report a runaway success? One that has your stakeholders forwarding it to their entire teams and still discussing it for days afterwards. It's not about flashy visuals... Nor about sophisticated analysis techniques... And definitely not about being a data science rockstar like Andrew Ng. It's about understanding your audience and delivering your results in a way that's tailored specifically for them. Want to see what I mean? Check out the Uplevel report on the impact of GenAI on developer productivity (link in comments), which went viral across 75 global media outlets. I did and here's what I learned. 1. Get straight to the point The Uplevel report is targeted at engineering business leaders who are likely very busy in their jobs. To allow for this, the report is only 2 pages long and doesn't waste any time in getting to the point. After a few short paragraphs to provide context on the data analysis behind the report, the authors get right to the most interesting findings, ensuring few readers are lost along the way. 2. Insights first, evidence second, technical details buried at the end Most business leaders don't want to know "how the sausage gets made". They just need to know the headline results, followed by enough evidence required to support them. This is what the Uplevel report provides. Technical details are also given, for those who really want to know, but those are positioned right at the end, ensuring they don't dilute the impact of the report. 3. Finish with a clear "so what?" In the words of data storytelling expert Brent Dykes, "without action, insights are just empty numbers." The Uplevel report doesn't just share the most interesting findings of the analysis, it provides clear next steps to allow business leaders to create value from these results - transforming the findings from "nice to have" facts to actionable insights. Give them a try when you next write a report and see what a difference they make. And if you want more advice on presenting data science results, I recently sat down with two of the authors of the Uplevel report, Dr Matt Hoffman and Lauren Lang, to record a Value Boost episode where we discuss the essential questions you can ask yourself to ensure your presentations never fall flat. You'll walk away knowing: 1. The critical business context most data scientists overlook when presenting their work 2. How to ensure your technical content works as hard as you do - whether presented live or shared asynchronously  3. The "so what" framework that instantly makes your analysis more compelling to leaders Listen to it now and transform your next data presentation. You can find it on Apple Podcasts, Spotify, or at the link below. https://lnkd.in/g5AAwy-D Apply these questions to your next presentation and watch your stakeholders' eyes light up with understanding instead of glazing over with boredom. #datascience #podcast #communications

𝐓𝐡𝐞 𝐚𝐫𝐭 𝐨𝐟 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐭𝐨 𝐝𝐢𝐯𝐞𝐫𝐬𝐞 𝐬𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬 One of the most underappreciated challenges in leading data initiatives isn't the technology, it's effectively engaging with multiple stakeholder groups who each need different information, presented differently. Success can be best supported by tailoring your approach across three distinct audiences: 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐯𝐞/𝐁𝐨𝐚𝐫𝐝 𝐋𝐞𝐯𝐞𝐥 These stakeholders need the 30,000-foot view focused on: 🔹 Business impact and ROI 🔹 Risk mitigation strategies 🔹 Resource allocation justification 🔹 Clear timelines with defined milestones When presenting here, focus on outcomes rather than methods, using business metrics they already value and understand. 𝐂𝐫𝐨𝐬𝐬-𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬 Department leaders and business partners require: 🔹 How the project will affect their operations 🔹 Specific benefits to their teams 🔹 Required involvement and resource commitments 🔹 Timeline of when they'll see tangible results Ensure you translate technical concepts into functional benefits, always answering their implicit question: "What's in it for my team?" 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐒𝐌𝐄𝐬 / 𝐃𝐨𝐞𝐫𝐬 These specialists need: 🔹 Architectural decisions and their rationale 🔹 Technical dependencies and integration points 🔹 Clear technical requirements and acceptance criteria 🔹 Roadmaps for implementation and technical debt management With this group, go deeper into the "how" while still connecting it to the "why." The true art lies in maintaining consistency across these different views. The timeline shown to executives must align with what the technical team is building and what business stakeholders are expecting. The promised business outcomes must be technically feasible. Successful data leaders don't just understand data, they understand people and can adapt their communication to bring everyone along on the journey. What challenges have you faced when communicating complex data initiatives across different organisational levels? #DataLeadership #StakeholderManagement #DataStrategy #TechnicalLeadership

Imagine you've performed an in-depth analysis and uncovered an incredible insight. You’re now excited to share your findings with an influential group of stakeholders. You’ve been meticulous, eliminating biases, double-checking your logic, and ensuring your conclusions are sound. But even with all this diligence, there’s one common pitfall that could diminish the impact of your insights: information overload. In our excitement, we sometimes flood stakeholders with excessive details, dense reports, cluttered dashboards, and long presentations filled with too much information. The result is confusion, disengagement, and inaction. Insights are not our children, we don’t have to love them equally. To truly drive action, we must isolate and emphasize the insights that matter most—those that directly address the problem statement and have the highest impact. Here’s how to present insights effectively to ensure clarity, engagement, and action: ✅ Start with the Problem – Frame your insights around the problem statement. If stakeholders don’t see the relevance, they won’t care about the data. ✅ Prioritize Key Insights – Not all insights are created equal. Share only the most impactful findings that directly influence decision-making. ✅ Tell a Story, Not Just Show Data– Structure your presentation as a narrative: What was the challenge? What did the data reveal? What should be done next? A well-crafted story is more memorable than a raw data dump. ✅ Use Clean, Intuitive Visuals – Data-heavy slides and cluttered dashboards overwhelm stakeholders. Use simple, insightful charts that highlight key takeaways at a glance. ✅ Make Your Recommendations Clear– Insights without action are meaningless. End with specific, actionable recommendations to guide decision-making. ✅ Encourage Dialogue, Not Just Presentation – Effective communication is a two-way street. Invite questions and discussions to ensure buy-in from stakeholders. ✅ Less is More– Sometimes, one well-presented insight can be more powerful than ten slides of analysis. Keep it concise, impactful, and decision-focused. Before presenting, ask yourself: Am I providing clarity or creating confusion? The best insights don’t just inform—they inspire action. What strategies do you use to make your insights more actionable? Let’s discuss! P.S: I've shared a dashboard I reviewed recently, and thought it was overloaded and not actionably created

Most analyst job descriptions scream: SQL Python Dashboards Those matter. But they’re not what make you a trusted analyst. In practice, 80% of your impact comes from the “invisible” 20% of skills: • How you think about the problem • How you frame your insights • How you talk to stakeholders So instead of chasing every tool, focus on the communication fundamentals first. Here’s the stack I wish someone had shown me early in my career 👇 The Data Analyst's Communication Stack ↳ 𝗖𝗹𝗮𝗿𝗶𝘁𝘆 & 𝗔𝘂𝗱𝗶𝗲𝗻𝗰𝗲 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 - who’s in the room, what they care about, how the business actually measures success, and how to say it in plain language ↳ 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 & 𝗙𝗿𝗮𝗺𝗶𝗻𝗴 - top-down storytelling, CCR / SCQA / PREP, “so what?” and “now what?” so people don’t get lost in the details ↳ 𝗣𝗲𝗿𝘀𝘂𝗮𝘀𝗶𝗼𝗻 & 𝗜𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 - tying insights to revenue, cost, risk, or customer; showing options and trade-offs; handling objections; ending with a clear owner and next step ↳ 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 & 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - shaping a narrative arc, choosing the right chart, using action-oriented titles and annotations, and directing attention to what matters ↳ 𝗟𝗶𝘃𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 + 𝗕𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀 - asking clarifying questions, explaining in real time, pushing back on vague “can you just…” requests, and choosing the right channel (async vs live) ↳ 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 - decision-focused problem framing, root cause vs symptom, hypotheses, disconfirming evidence, assumptions, and “what would change my mind?” You don’t need to master all of this at once. Start here: Pro-tip #1: Pick one real meeting this week and practice better clarifying questions instead of better charts. Pro-tip #2: For your next slide deck, rewrite every title so it says what happened + why it matters, not just the metric name. Pro-tip #3: Treat communication as a skill you train on purpose, not something you “hope” gets better with time. Save the map, come back to it whenever you feel stuck, and ask yourself: “Which layer of my communication stack is actually holding this analysis back?” PS: I’m running a live orientation on 𝗝𝗮𝗻𝘂𝗮𝗿𝘆 𝟭𝟮𝘁𝗵 for new analysts who want to master this communication stack inside my course, The Analyst Edge. If you want the details, I’ll put the link in the comments.

Everyone in data should learn this one thing: How their stakeholders actually make decisions. Here’s the cheat sheet Top CMOs wish you knew: If you want to be a successful data professional, you need more than SQL, models, and dashboards. You need to understand how your stakeholders think. In many companies, marketers are your most high-impact stakeholders. Here’s what the top CMOs taught me about working with them: → Speak their language: CAC, LTV, margin, payback - this is how they make decisions. → Embrace ambiguity: marketing is probabilistic, messy, and full of imperfect data. → Be proactive: spot anomalies before they do. Trust is built when you’re ahead. → Understand their world: Show up in the tools they already live in (Google Sheets, Hubspot) → Support their tests: better experiments = better growth; you’re the co-pilot, not the police. → Go deep: understand how their campaigns actually work - not just the numbers. The truth? Data teams become indispensable the moment they stop thinking like technicians and start thinking like their stakeholders. ♻️ Repost if you’ve ever heard “this is great, but what does it mean for our growth?” 👉 And join 3,000+ data leaders who read my free newsletter for weekly tips on building impactful data teams in the AI-era: https://lnkd.in/gyPUFzMD

Stakeholders don't care about your data tools—they care about making better decisions. A successful data solution delivers real-world impact through: 1. Decision Speed ↳ Enable faster insights to action ↳ Reduce time from question to answer 2. Visible Value Creation ↳ Highlight cost savings and revenue gains ↳ Connect metrics directly to business outcomes 3. Intuitive Experience ↳ Create simple, clean layouts ↳ Implement only essential filters ↳ Design for the decision-maker, not the analyst 4. Stakeholder Enablement ↳ Build education into the experience ↳ Create resources that empower self-service Step into your stakeholders' shoes and deliver what they actually need. Remember: Your impact isn't measured by technical complexity—it's measured by decisions improved. What decision-focused feature has delivered the most value in your organization? --- Follow Don Collins for practical insights on analytics, leadership, and data storytelling that drives results.

𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 & 𝗗𝗮𝘁𝗮: 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗻𝗴 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘁𝗼 𝗡𝗼𝗻-𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 Data analysts often face a big challenge not just analyzing data, but explaining it in a way that makes sense to business team. A great analysis is useless if decision-makers don’t understand it! Here are some ways analysts can communicate better with non-technical stakeholders: ↳ 𝗧𝗲𝗹𝗹 𝗮 𝗦𝘁𝗼𝗿𝘆, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗡𝘂𝗺𝗯𝗲𝗿𝘀:– Instead of sharing raw data, focus on the key takeaway. What does the data mean for the business? ↳ 𝗔𝘃𝗼𝗶𝗱 𝗝𝗮𝗿𝗴𝗼𝗻:– Terms like "p-value," "ETL," or "normalization" might not be familiar to everyone. Use simple language that connects with your audience. ↳ 𝗨𝘀𝗲 𝗖𝗹𝗲𝗮𝗿 𝗩𝗶𝘀𝘂𝗮𝗹𝘀:– A well-designed chart is more powerful than a table full of numbers. Choose the right visual to highlight the key insight. ↳ 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗧𝗵𝗲𝗶𝗿 𝗡𝗲𝗲𝗱𝘀:– Before presenting data, ask stakeholders what decisions they need to make. This helps you focus on relevant insights. ↳ 𝗘𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀:– A two-way conversation ensures stakeholders fully understand the data and feel confident using it. Great analysts don’t just crunch numbers, they bridge the gap between data and decision-making. What strategies have helped you communicate better with non-technical teams? #dataanalytics