Tips for Engaging in Data Storytelling

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

If you are looking for a roadmap to master data storytelling, this one's for you Here’s the 12-step framework I use to craft narratives that stick, influence decisions, and scale across teams. 1. Start with the strategic question → Begin with intent, not dashboards. → Tie your story to a business goal → Define the audience - execs, PMs, engineers all need different framing → Write down what you expect the data to show 2. Audit and enrich your data → Strong insights come from strong inputs. → Inventory analytics, LLM logs, synthetic test sets → Use GX Cloud or similar tools for freshness and bias checks → Enrich with market signals, ESG data, user sentiment 3. Make your pipeline reproducible → If it can’t be refreshed, it won’t scale. → Version notebooks and data with Git or Delta Lake → Track data lineage and metadata → Parameterize so you can re-run on demand 4. Find the core insight → Use EDA and AI copilots (like GPT-4 Turbo via Fireworks AI) → Compare to priors - does this challenge existing KPIs? → Stress-test to avoid false positives 5. Build a narrative arc → Structure it like Setup, Conflict, Resolution → Quantify impact in real terms - time saved, churn reduced → Make the product or user the hero, not the chart 6. Choose the right format → A one-pager for execs, & have deeper-dive for ICs → Use dashboards, live boards, or immersive formats when needed → Auto-generate alt text and transcripts for accessibility 7. Design for clarity → Use color and layout to guide attention → Annotate directly on visuals, avoid clutter → Make it dark-mode (if it's a preference) and mobile friendly 8. Add multimodal context → Use LLMs to draft narrative text, then refine → Add Looms or audio clips for async teams → Tailor insights to different personas - PM vs CFO vs engineer 9. Be transparent and responsible → Surface model or sampling bias → Tag data with source, timestamp, and confidence → Use differential privacy or synthetic cohorts when needed 10. Let people explore → Add filters, sliders, and what-if scenarios → Enable drilldowns from KPIs to raw logs → Embed chat-based Q&A with RAG for live feedback 11. End with action → Focus on one clear next step → Assign ownership, deadline, and metric → Include a quick feedback loop like a micro-survey 12. Automate the follow-through → Schedule refresh jobs and Slack digests → Sync insights back into product roadmaps or OKRs → Track behavior change post-insight My 2 cents 🫰 → Don’t wait until the end to share your story. The earlier you involve stakeholders, the more aligned and useful your insights become. → If your insights only live in dashboards, they’re easy to ignore. Push them into the tools your team already uses- Slack, Notion, Jira, (or even put them in your OKRs) → If your story doesn’t lead to change, it’s just a report- so be "prescriptive" Happy building 💙 Follow me (Aishwarya Srinivasan) for more AI insights!

Data without a story is just a spreadsheet. A story without data is just an opinion. Ever wondered why some presentations leave you stunned while others put you to sleep? The answer might be simpler than you think: It's all about how you present your data. Let's dive into a masterclass on data visualization, courtesy of Hans Rosling's iconic TED talk. Rosling starts with a bombshell: Swedish top students know statistically significantly less about the world than chimpanzees. Wait, what? He goes on… Rosling used a simple quiz: → 5 pairs of countries → Each pair: one country has twice the child mortality of the other → The task: Identify which country in each pair has higher mortality The results from his students were…shockingly bad. Why this story works: Simplicity: The test is easy to understand Contrast: Humans vs. Chimpanzees (unexpected comparison) Personal connection: We all think we're smarter than chimps Just like startups need to solve high-intensity problems, your data needs to address high-intensity curiosities. Rosling didn't pick random facts. Instead, he chose a topic that matters (child mortality), a comparison that shocks (educated humans vs. random guessing), and results that challenge assumptions (We're not as informed as we think). This is the "Intensity Imperative" of data storytelling. How to Apply This: 1/ Find the Unexpected What data point in your industry would surprise even the experts? Where do common assumptions fall apart when faced with real numbers? 2/ Make It Personal How can you frame data so your audience sees themselves in the story? What universal human experiences can you tap into? 3/ Simplify, Then Simplify Again Can you explain your key data point in one sentence? If not, keep refining until you can. 4/ Use Vivid Comparisons Instead of abstract numbers, how can you relate your data to everyday concepts? Example: "This much carbon dioxide would fill 1 million Olympic-sized swimming pools" 5/ Build Tension, Then Release Start with a question or premise. then let the data reveal the answer dramatically.

Data alone won’t get you approved. (Let’s fix it with 5 secret Storytelling ingredients) I spent 2 years on an EV fast-charging project. Published 2 papers & filed 2 patents. A success, right? BUT… when I presented to sponsors... there was pin-drop silence in Zoom room. I lost them to their smartphones. Another opportunity wasted. MISSING PIECE? I was presenting data, not stories. Steal this 5-step process to turn boring data into stories: 1/ SETTING (Time & Context) ↳ When and where your story unfolds ↳ Creates immediate relevance BUSINESS EXAMPLE: ❌ "We had system downtime" ✅ "At 2 PM on Black Friday, our checkout system crashed while 50,000 customers were trying to buy" DAILY USE: → Status meetings: "During yesterday's client call..." → Problem reports: "Right before the quarterly review..." → Strategy presentations: "In the current economic climate..." TAKEAWAY: Context turns facts into urgency. 2/ CHARACTERS (Your Stakeholders) ↳ WHO gets impacted by your message ↳ Makes abstract problems personal BUSINESS EXAMPLE: ❌ "Customer satisfaction declined 15%" ✅ "Jennifer, our top enterprise client who renewed for 4 years straight, called to cancel her contract" DAILY USE: → Executive updates: Name the affected teams/customers → Budget requests: Show WHO benefits from approval → Change proposals: Identify WHO struggles with current state TAKEAWAY: People fund people, not percentages. 3/ NORMAL STATE (Baseline) ↳ How things operated before the problem ↳ Establishes what "good" looks like BUSINESS EXAMPLE: "For 18 months, our support team handled 200 tickets daily with 4-hour response time" 4/ DISRUPTION (The Change) ↳ What broke the normal pattern ↳ Creates tension that demands action BUSINESS EXAMPLE: "Then the product launch tripled our user base overnight, and response time hit 48 hours" TAKEAWAY: Story is about contrast: “before” vs. “what went wrong.” 5/ RESOLUTION (New Normal) ↳ What happened AFTER addressing the disruption ↳ Shows outcome and path forward BUSINESS EXAMPLE: "We hired 3 specialists, automated tier-1 responses, and cut response time to 90 minutes while handling 600 daily tickets" DAILY USE: → Project wrap-ups: Show the measurable improvement → Lessons learned: Share what changed permanently → Success stories: Provide the roadmap others can follow TAKEAWAY: Your resolution becomes their next action plan. IMPLEMENTATION FRAMEWORK: Before your next presentation, answer these: 1️⃣ WHEN/WHERE does this matter most? 2️⃣ WHO gets affected if nothing changes? 3️⃣ HOW were things working before? 4️⃣ WHAT specifically broke or changed? 5️⃣ WHERE does this lead us next? 5 questions. 5 elements. Every presentation. ♻️ REPOST if your presentations need more impact ➕ Follow Waqas, P. for communication skills 💾 SAVE for future use 📌 How often you see presenters losing audiences to smartphones?

If your HR data lives on dashboards, you owe it to your dashboard users to turn those numbers into stories that spark action. Here’s the key: Context, Clarity, and Connection I'm a founder of natural language AI for HR data insights, I talk to a lot of HR executives. Not every company is ready to hook AI up to their HRIS or dashboards for full cycle automation of custom reports. So here's how you can take action now: 🟩 Context: Don’t just say, “Our turnover rate is 15%.” Show why it matters: “Turnover has jumped from 10% to 15% since we cut back on flexible work policies. That’s costing us an extra $250K in rehiring and training.” 👉 By adding context—historic trends, external comparisons, or qualitative insights from exit interviews—you transform an isolated number into a relevant piece of intelligence. 🟩 Clarity: Skip buzzwords. Instead of burying leaders in pivot tables, highlight the core insight: “Our data shows that when managers engage in weekly check-ins, their teams stay 12% longer and report 20% higher job satisfaction.” 👉 Simplicity is crucial. Data can be daunting, so distill it into its most straightforward form. Describe what’s happening in plain language, highlight the key takeaway, and avoid excessive buzzwords. 🟩 Connection: Make the data human. Share a brief story about an employee’s journey—how they left due to inflexible hours, or how a new mentorship program increased retention. 👉 This personal angle sticks in leaders’ minds and moves them to act. When data is told as a story, it becomes memorable, persuasive, and actionable. That’s how you move from presenting numbers to driving real change. Storytelling is GREAT ⭐ 👍 Visualization: By combining that number with a narrative—like highlighting how three top performers left due to inflexible work policies—suddenly, you have context and emotion. Decision-makers can visualize the impact on projects, productivity, and team morale. 👍 It Captures Attention Leaders face a tidal wave of emails, reports, and dashboards every day. A compelling story cuts through the clutter. Instead of reading yet another data dump, they encounter a narrative that clearly connects metrics to outcomes (such as cost savings, product quality, or customer satisfaction). 👍 It Accelerates Buy-In Numbers can be debated or ignored, but when they’re woven into a story that resonates—especially one that ties to real pain points—leaders are far more likely to take action. A powerful story engages emotion and logic, making it easier for people to rally behind a solution. What's your Data storytelling tip? What works with your leaders ? #peopleanalytics #hrdata #peopledata #hr #dashboards #hrdashboards

Most “data storytelling” is missing a piece. And it’s usually why the work doesn’t land. You can have: Great charts → but no clear point A clean, logical deck → but no action taken A persuasive message → but no data to back it up Each of these looks good on its own, but none of them reliably drive decisions. What actually works is the combination of three things: 1. Data visualization: Make the insight easy to SEE 2. Narrative structure: Make the insight easy to FOLLOW 3. Influence: Make the insight easy to ACT ON Most teams are strong in one or two of these. But that always leaves a gap: Strong viz + structure → clear report… that doesn’t move anything Structure + influence → compelling anecdote… without evidence Viz + influence → key stat… without enough context to unpack it The work that drives decisions sits at the intersection of all three: The right insight, delivered in the right way, framed so people actually do something with it. THAT’s the difference between sharing information and actually influencing decisions. This is also how I approach working with teams when tailoring workshops. In early conversations, we usually map where the gaps are: are we clear but not driving action? persuasive but not landing it visually? Then we focus on closing that gap to move closer to the center. 📌 Save this for your next big presentation Learn more about my most popular workshop here: https://lnkd.in/g_pKPCKh Where in this diagram do you see people getting stuck most often? What would help them move closer to the center? (Also - I welcome any feedback on the diagram as I continue to refine the labels!) --

In #datastorytelling, you often want a specific point to stand out or “POP” in each data scene in your data stories. I’ve developed a 💥POP💥 method that you can apply to these situations: 💥 P: Prioritize – Establish which data point is most important. 💥 O: Overstate – Use visual emphasis like color and size as a contrast.   💥 P: Point – Guide the audience to the focal point of your chart. The accompanying illustration shows the progressive steps I’ve taken to make Product A’s Q3 $6M sales bump stand out. Step 1️⃣: Add headline. One of the first things the audience will attempt to do is read the title. A descriptive chart title like “Products by quarterly sales” is too general and offers no focal point. I replaced it with an explanatory headline emphasizing the increase in Product A sales in Q3. The audience is now directed to find this data point in the chart. Step 2️⃣: Adjust color/thickness I want the audience to focus on Product A, not Product B or Product C. The other products are still useful for context but are not the main emphasis. I kept Product A’s original bold color but thickened its line. I lightened the colors of the two other products to reduce their prominence. Step 3️⃣: Add label/marker I added a marker highlighting the $6M and bolded the label font. You’ll notice I added a marker and label for the proceeding quarter. I wanted to make it easy for the audience to note the dramatic shift between the two quarters. Step 4️⃣: Add annotation You don’t always need to add annotations to every key data point, but it can be a great way to draw more attention to particular points. It also allows you to provide more context to help explain the ‘why’ or ‘so what’ behind different results. Step 5️⃣: Add graphical cue (arrow) I added a graphical cue (arrow) to emphasize the massive increase in sales between the two quarters. You can use other objects, such as reference lines, circles, or boxes, to draw attention to key features of the chart. In terms of the POP method, these steps align in the following way: 💥 Prioritize – Step 1 💥 Overstate – Step 2-3 💥 Point – Step 4-5 Because data stories are explanatory rather than exploratory, you need to be more directive with your visuals. If you don’t design your data scenes to guide the audience through your key points, they may not follow your conclusions and become confused. Using the POP method, you ensure that your key points stand out and resonate with your audience, making your data stories more than just informative but memorable, engaging, and persuasive. So next time you craft a data story, ensure your data scenes POP—and watch your insights take center stage! What other techniques do you use to make your key data points POP? 🔽 🔽 🔽 🔽 🔽 Craving more of my data storytelling, analytics, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7

90% of data analysts show pretty charts. Only 10% know which chart tells the right story. But here's the brutal truth: They don't want to know what happened. They want to know what to do about it. 𝐓𝐡𝐢𝐬 𝐦𝐢𝐧𝐝𝐬𝐞𝐭 𝐬𝐡𝐢𝐟𝐭 𝐬𝐞𝐩𝐚𝐫𝐚𝐭𝐞𝐬 𝐠𝐫𝐞𝐚𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬 𝐟𝐫𝐨𝐦 𝐠𝐨𝐨𝐝 𝐨𝐧𝐞𝐬: Good analysts report: "Sales dropped 15% last quarter." Great analysts recommend: "Sales dropped 15% in Region A. Shift budget to Region B, where conversion is 3x higher." Good analysts show: "Customer churn increased"   Great analysts advise: "Churn spiked after the pricing change. Reverting would save $2M annually." 𝐓𝐡𝐞 𝐜𝐡𝐚𝐫𝐭 𝐭𝐲𝐩𝐞 𝐬𝐭𝐢𝐥𝐥 𝐦𝐚𝐭𝐭𝐞𝐫𝐬. 𝐁𝐮𝐭 𝐨𝐧𝐥𝐲 𝐢𝐟 𝐢𝐭 𝐝𝐫𝐢𝐯𝐞𝐬 𝐚𝐜𝐭𝐢𝐨𝐧: Bar Chart → Compare performance, identify winners to scale Line Chart → Spot trends early, predict what's coming Scatter Plot → Find correlations that unlock opportunities Heatmap → Highlight problem areas that need immediate attention 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥𝐢𝐭𝐲: Your stakeholders have 50 dashboards already. They don't need another one. 𝐓𝐡𝐞𝐲 𝐧𝐞𝐞𝐝 𝐲𝐨𝐮 𝐭𝐨 𝐭𝐞𝐥𝐥 𝐭𝐡𝐞𝐦 𝐚 𝐬𝐭𝐨𝐫𝐲: - What's broken - Why it matters - What to do next Stop being a reporter. Start being an advisor. The best visualization isn't the prettiest one. It's the one that gets a decision made. ♻️ Share this with an analyst ready to level up 𝐏.𝐒. I share data storytelling insights and career tips in my free newsletter. Join 19,000+ readers → https://lnkd.in/dUfe4Ac6

“Data doesn’t tell stories—people do. Without you, data is just a socially awkward introvert at the party.” Let’s face it: data on its own is boring. It’s a spreadsheet nobody wants to open, a chart your audience pretends to understand, or that one guy in meetings who says a lot but never gets to the point. But pair data with a good storyteller? Now you’ve got magic. The kind that gets people to nod, lean in, and maybe even email you after the meeting to say, “Wow, you’re so smart. Did you make that graph yourself?” Of course, there’s a fine line between telling a story with data and taking it on a joyride. Spin the numbers, and you lose credibility. But dump too much raw data on your audience, and their eyes will glaze over faster than a PowerPoint with 87 slides. So how do you strike the balance? Easy: start with the ending. What’s the big point you’re trying to make? What action do you want your audience to take? Once you know that, you can reverse-engineer the whole thing, like assembling IKEA furniture—except without the leftover screws and existential crisis. Here’s the playbook: 1️⃣ Pick your data carefully: Not all numbers are helpful. Some are rockstars, and others are just background dancers. If it doesn’t push the story forward, cut it. Trust me, nobody’s going to miss slide #14 with the tiny font and the 19-bar chart. 2️⃣ Think of your data as chapters: Start with the hook. Build the tension. Save the jaw-dropping stat for the big finish. Your audience shouldn’t just read your data; they should feel like they’re binge-watching a Netflix thriller. 3️⃣ Visualize with purpose: A great chart is like a good meme—instantly clear and impossible to forget. Keep it simple, keep it bold, and please, for the love of pie charts, stop using Comic Sans. The truth is, data doesn’t speak for itself. That’s your job. When you use it to tell a story that’s clear, compelling, and maybe even a little funny, you’ll make an impact that sticks. How do you turn awkward data into a showstopper? Let me know below—bonus points if your answer involves bad charts or good jokes.

𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 is the Secret Sauce to Great Dashboards. When I first started building dashboards, I thought the focus should be on how much data I could include. But over time, I realized it’s not about the data, you need to show why the data matters. Here’s what I learned about effective data storytelling: 𝟭. 𝗙𝗶𝗻𝗱 𝘁𝗵𝗲 𝗡𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 Every dataset has a story to tell. Whether it's tracking business growth or analyzing customer behavior, your job is to highlight the insights that matter most. Don’t just show numbers, connect them to real-world outcomes. 𝟮. 𝗞𝗲𝗲𝗽 𝗶𝘁 𝗙𝗼𝗰𝘂𝘀𝗲𝗱 The best stories are clear and concise. Your dashboard should focus on key metrics that lead to action. Overloading with unnecessary details dilutes the impact. 𝟯. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝘄𝗶𝘁𝗵 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 Your visuals need to guide the user, not overwhelm them. I found that less is more, use charts and graphs that support the narrative, not distract from it. 𝟰. 𝗟𝗲𝗮𝗱 𝘄𝗶𝘁𝗵 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗡𝗼𝘁 𝗗𝗮𝘁𝗮 Data storytelling isn’t about showing every number, it’s about showcasing the insight behind the numbers. What’s the data saying? How does it affect your audience's decisions? 𝟱. 𝗘𝗻𝗴𝗮𝗴𝗲 𝗘𝗺𝗼𝘁𝗶𝗼𝗻𝗮𝗹𝗹𝘆 Numbers are facts, but stories are what people connect to. Find the human side of your data, whether it's a growth story, a challenge, or an opportunity and make your audience care. #Dashboards that tell a compelling story don’t just inform, they inspire action. What’s your approach to data storytelling? ♻️ Like or Repost to Inspire Your Network. Follow Manali Kulkarni #Storytelling #DataVisualization #Analytics