Data Storytelling Techniques

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

As I deliver #datastorytelling workshops to different organizations, I encounter a common misconception about how you should approach telling stories with data. To use a Lord of the Rings (LOTR) movie analogy, some #data professionals appear more focused on creating behind-the-scenes documentaries than actual narratives. They want to show the steps, methodologies, and approaches they used during their analysis rather than crafting a concise, compelling narrative. As a LOTR geek, I have watched many behind-the-scenes featurettes. However, I recognize that most people have only watched the LOTR movies and none of the documentaries. They're interested in compelling narratives--not the nitty-gritty of how the movies were made. When it comes to data stories, audiences are more interested in hearing an insightful narrative about a business problem or opportunity than an explanation of how you performed your analysis to assess the problem or opportunity. Taking a documentary approach with your data stories will introduce the following problems: ❌  Added complexity as you go into details that don’t matter to your audience (data collection/preparation, methodology, technical aspects, etc.). ❌  Loss of attention or interest as the audience waits to hear something meaningful. ❌  Less focused or clear communication as insights become buried in minutiae. ❌  Less time to discuss conclusions and determine next steps. ❌  Reduced actionability as extraneous details sidetrack the narrative and obscure the key takeaways. The only people who will get value from a behind-the-scenes documentary will be fellow data professionals. This is a much narrower audience than a broader business audience that is seeking insightful narratives about the business. I recommend delivering the narrative first and having your documentary ready in an appendix (if needed). Most of the time, no one will ask how you performed your analysis (unless they have questions about your numbers). With this approach, the audience will be focused on understanding your insight, implementing your recommendations, and taking action. That's a win-win. How do you avoid telling documentaries instead of narratives? 🔽 🔽 🔽 🔽 🔽 Craving more of my data storytelling, analytics, and data culture content? Sign up for my brand new newsletter today: https://lnkd.in/gRNMYJQ7

Maybe no one has told you this but, I will. Data and creativity are not enemies. I used to think that if you focus on the numbers, you lose the magic of creative writing. But after writing 1000s of LinkedIn posts, I can say that - Data can make you more creative, if used the right way. This is how I blend both - ➙ Get insights before the idea I don’t wait for inspiration to strike. I check the data. What’s trending? What content worked? What’s my audience engaging with? I use this as the foundation to build creative ideas. ➙ Experiment with data Data tells you what worked, but creativity tells you why it worked. I test new ideas based on past engagement. ➙ Storytelling with numbers Sometimes, a good story backed by numbers works wonders. I love combining my audience’s pain points (data) with a solution or experience (creativity). It relates way more. ➙ Track, don’t trap Yes, I track performance metrics like impressions and engagement. But I never let them dictate my every move. The goal is to understand what relates, not to repeat the same thing. Creativity grows when you explore new directions. The mix is simple - Data shapes the strategy, but creativity is in the execution. Know what’s working based on analytics but how you choose to deliver that content is your creative choice. P.S. Do you rely on data generated by AI? P.P.S. Do you like playing Jenga?

It’s crucial to navigate the often overwhelming discourse surrounding Diversity, Equity, and Inclusion with clarity and purpose. The first step is to establish clear, measurable objectives for your DEI initiatives. This involves setting specific goals, such as increasing representation of underrepresented groups in leadership roles or improving employee engagement scores. By having well-defined targets, you can focus your efforts and measure progress effectively, cutting through the noise and demonstrating tangible results. Another key strategy is to leverage data to inform your DEI efforts. Collecting and analysing data on workforce demographics, employee experiences, and the impact of DEI programs allows you to identify areas for improvement and track the success of your initiatives. Data-driven approaches help to ground your DEI strategies in evidence, making it easier to communicate the importance and effectiveness of these efforts to stakeholders. This not only helps in addressing misconceptions but also in building a strong case for continued investment in DEI. Finally, fostering an inclusive culture requires active and visible leadership. Leaders must model inclusive behaviors, such as actively seeking diverse perspectives and addressing unconscious biases. Providing regular training and creating platforms for open dialogue can help in building a more inclusive environment. Additionally, involving employees at all levels in DEI initiatives, through resource groups or feedback sessions, ensures that everyone feels a sense of ownership and commitment to these goals. By maintaining a continuous focus on improvement and staying informed about best practices, everyone can effectively cut through the cacophony and drive meaningful change within their organizations. #diversity #equity #inclusion #belonging

Recently, I was asked in my LinkedIn messages, "Anna, how can I improve my storytelling on LinkedIn? Is there a framework to follow?" Enter The SARI Framework. In a sea of content, stories stand out. However, crafting one that sticks requires a clear structure. S—Set the Scene: Establish who, what, where, when, and why. It's not just about context; it's your first move to engage and prime your audience for what's coming. A—Actions: What did you do? This is where your story gets its momentum. R—Results: What happened as a result? Success or failure, it's all valuable. I—Insights: The key takeaway. What did you learn? This is what your audience will likely remember and appreciate the most. Storytelling is about change, whether it's external or internal. Your story should show a journey from point A to point B. Why does this matter on LinkedIn? We're here to learn from each other. Sharing your insights contributes to this collective learning process. If you find this useful, give it a like, share, or comment. Let's get better at storytelling together. P.S. Which part of the SARI Framework do you find most challenging, and why? Let's discuss. #whatsyourstory #storytelling #storytellingtips #framework

Data Without a Story? You're Missing the Point! Leading multiple large-scale projects, I’ve learned that data alone isn’t enough. Raw numbers, charts, and spreadsheets rarely inspire action. It's the story within the data that drives change. Think about it: 📊 Context is Your Compass: Without understanding the "why" behind the numbers, your audience is lost. Start by framing your data within a relevant narrative. 📊 Visuals That Resonate: Choose charts and graphs that make your data clear and compelling. Ditch the clutter and focus on the key takeaways. 📊 Attention is Currency: Guide your audience's eyes to the most important points. Use design to highlight the insights that matter. 📊 Design for Understanding: Apply design thinking principles to make your data accessible and engaging, empowering faster, better decisions. Essentially, it's about transforming data into a narrative that resonates. You're not just presenting numbers; you're crafting a persuasive argument, a compelling case for action. When data tells a story, it becomes more than just information. It becomes a powerful tool for influence and change. What are your go-to strategies for turning data into a compelling story? – 👉 Follow me, Rony Rozen, for more real-world insights on tech leadership.

The longer I work in Data and AI, the more I realize that communication is the key. It's easy to think that data science, machine learning, or artificial intelligence is all about programming and complex math. While technically true, this is just half the story. 𝐁𝐮𝐭 𝐡𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: Is the value in the technical complexity or in how you communicate it? One phrase I remember from my communication coach now is, "You can always communicate everything." It's a simple phrase, but you can get something from others by communicating it right. How true the word above is reflected in my experience. When working for a client or employed, I was expected to solve problems with my technical expertise. In the early days, I will discuss the solution and result using many technical terms. You know what happens? A mess. Business people mostly do not understand how our technical things work. Many don't even want to know as long as we are solving their problems. Everything is all about the business, after all. When I became a founder, my train of thought also changes: 𝐃𝐨𝐞𝐬 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡 𝐬𝐞𝐥𝐥 𝐢𝐭𝐬𝐞𝐥𝐟 𝐨𝐫 𝐢𝐬 𝐢𝐭 𝐚𝐥𝐥 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐬𝐭𝐨𝐫𝐲? Well, I will answer that it's how you package the tech in a nice story. You can even see it yourself: the most engaging technical content has a story behind it. So, communication is important even if you work in technical fields. Here are some tips you can use to improve communication as data people: ✅𝐊𝐧𝐨𝐰 𝐘𝐨𝐮𝐫 𝐀𝐮𝐝𝐢𝐞𝐧𝐜𝐞: Tailor your message to what matters—code for peers, impact for leaders. ✅𝐓𝐞𝐥𝐥 𝐚 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐲: Structure insights as Problem → Insight → Impact for clarity. ✅𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲 𝐒𝐦𝐚𝐫𝐭𝐥𝐲: Use analogies to relate complex ideas without losing depth. ✅𝐕𝐢𝐬𝐮𝐚𝐥𝐬 𝐖𝐢𝐧: A good chart speaks louder than a thousand data points. ✅𝐄𝐥𝐞𝐯𝐚𝐭𝐨𝐫 𝐏𝐢𝐭𝐜𝐡 𝐑𝐞𝐚𝐝𝐲: Explain your project in 30 seconds—what, why, so what. ✅𝐀𝐬𝐤 𝐟𝐨𝐫 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤: You're on point if non-technical folks get it. ✅𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: Own your insights—clarity with confidence earns trust. Do you have any experience and tips you want to share? Discuss it below!👇 Want to learn more and get daily data science tips in your email inbox? Subscribe to my Newsletter>>> https://lnkd.in/g639tmpD ——————— You don't want to miss #python data tips + #datascience and #machinelearning knowledge + #AI. Follow Cornellius Y. and press the bell 🔔 to learn together. ———————

Data without a story is just… numbers. And numbers don’t make decisions. People do. Stakeholders want a narrative that moves them to act. Here are 5 storytelling hacks in data that stakeholders love (and that drive real impact): 1. Lead with the punchline     Don’t warm them up with a 20-slide build-up. Start with the big reveal: “If we fix onboarding, churn drops 20%, that’s $3M saved annually.” Stakeholders love clarity upfront. Then you can unpack the details. 2. Make the data human     Percentages are forgettable, people aren’t. Instead of “25% of users churn after week one”… Say: “1 out of every 4 new users walks away before they even meet us.” Suddenly, the problem feels real. 3. Use contrast for drama Great stories need tension. Data storytelling is no different. “We spent $1.2M on marketing last year… but only $200k of that actually drove conversions.” Contrast makes people lean in. 4. Translate everything into money or time Metrics are nice. Impact is better. “Efficiency up 10%” sounds good… But “This saves 40 engineering hours a month” makes people care. Dollars and hours are universal languages. 5. End with the action shot Never leave them wondering, “So what?” Finish with the next step: “Here are 2 experiments we can run next month to fix this.” Stories without a call to action die in the room. Remember: Data storytelling isn’t dumbing it down. It’s leveling it up so the right people act on it. Because the chart doesn’t create impact. The story does. If you want to read stories about how other data professionals are getting interviews consistently and how they convert them into an offer, visit our website. If you found this post valuable, follow me, Jaret André and DataShip for more.

Communicating complex data insights to stakeholders who may not have a technical background is crucial for the success of any data science project. Here are some personal tips that I've learned over the years while working in consulting: 1. Know Your Audience: Understand who your audience is and what they care about. Tailor your presentation to address their specific concerns and interests. Use language and examples that are relevant and easily understandable to them. 2. Simplify the Message: Distill your findings into clear, concise messages. Avoid jargon and technical terms that may confuse your audience. Focus on the key insights and their implications rather than the intricate details of your analysis. 3. Use Visuals Wisely: Leverage charts, graphs, and infographics to convey your data visually. Visuals can help illustrate trends and patterns more effectively than numbers alone. Ensure your visuals are simple, clean, and directly support your key points. 4. Tell a Story: Frame your data within a narrative that guides your audience through the insights. Start with the problem, present your analysis, and conclude with actionable recommendations. Storytelling helps make the data more relatable and memorable. 5. Highlight the Impact: Explain the real-world impact of your findings. How do they affect the business or the problem at hand? Stakeholders are more likely to engage with your presentation if they understand the tangible benefits of your insights. 6. Practice Active Listening: Encourage questions and feedback from your audience. Listen actively and be prepared to explain or reframe your points as needed. This shows respect for their perspective and helps ensure they fully grasp your message. Share your tips or experiences in presenting data science projects in the comments below! Let’s learn from each other. 🌟 #DataScience #PresentationSkills #EffectiveCommunication #TechToNonTech #StakeholderEngagement #DataVisualization