How To Use Data To Communicate With Stakeholders

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

Data is only powerful if people understand and act on it That’s why just pulling numbers isn’t enough. A good report tells a story, answers key business questions, and helps decision-makers take action. To ensure your analysis actually gets used: ✅ Start with the right question – If you don’t understand what stakeholders really need, you’ll spend hours on the wrong metrics. It’s okay to ask clarifying questions. ✅ Make it simple, not just accurate – Clean tables, clear charts, and insights that anyone (not just data people) can understand. ✅ Provide context, not just numbers – A 20% drop in sales is scary… unless you also show seasonality trends and explain why it’s normal. ✅ Anticipate follow-up questions – The best reports answer the next question before it's asked. ✅ Know your audience – A C-suite executive and a product manager don’t need the same level of detail. Tailor accordingly. Your work should make decision-making easier. If stakeholders are confused, they won’t use your report No matter how technically correct it is. The best data professionals don’t just crunch numbers. They translate data into impact. Have you ever spent hours on an analysis only for no one to use it?

I've spent over 4,000 hours in stakeholder requirement-gathering meetings! Save hours of your life by asking these questions: 1. What do they plan to use the data for? 1. What initiative are they working on? 2. How will this initiative impact the business? 3. Is this for reporting or optimizing existing workflows? Understanding the purpose of the data helps you define its impact. 2. How do they plan to use the data? Will they access it via SQL, BI tools, APIs, or another method? 1. Do they have a workflow to pull data from your dataset? 2. Do they just do a `SELECT *` from your dataset? 3. Do they perform further computations on your dataset? This determines the schema, partitions, and data accessibility needs. 3. Is this data already present in another report/UI? 1. Is this data already available in another location? 2. Do they have parts of this data (e.g., a few required columns) elsewhere? Ensuring you're not recreating work saves time and avoids redundancy. 4. How frequently do they need this data? 1. How frequently does the data actually need to be refreshed? 2. Can it be monthly, weekly, daily, or hourly? 3. Is the upstream data changing fast enough to justify the required latency? Understanding frequency helps you determine the pipeline schedule. 5. What are the key metrics they monitor in this dataset? 1. Define variance checks for these metrics. 2. Do these metrics need to be 100% accurate (e.g., revenue) or directionally correct (e.g., impressions)? 3. How do these metrics tie into company-level KPIs? Memorize average values for these metrics; they’re invaluable during debugging and discussions. 6. What will each row in the dataset represent? 1. What should each row represent in the dataset? 2. Ensure one consistent grain per dataset, as applicable. 7. How much historical data will they need? 1. Does the stakeholder need data for the last few years? 2. Is the historical data available somewhere? Ask these questions upfront, and you'll save countless hours while delivering exactly what stakeholders need. - Like this post? Let me know your thoughts in the comments, and follow me for more actionable insights on data engineering and system design. #data #dataengineering #datastakeholder

Analytics teams spend weeks perfecting their reports and dashboards only to hear: “This is interesting, but what should we actually do?” Recently, a marketing professor DM’ed me about his students struggling with data storytelling. His marketing research class was comfortable with the reporting aspects. But when asked to offer a clear point of view or insight, they froze. Some worried it might come across as manipulating the data if they offered interpretations or recommendations. This hesitation isn’t limited to these students. Many data professionals feel uncomfortable pushing beyond the “what.” Here’s why: 👉 Fear of being wrong publicly, especially when data involves uncertainty 👉 Desire to appear objective and “let the numbers speak for themselves” 👉 Lack of business context or confidence in their domain knowledge 👉 Positioning as a support function rather than a strategic partner 👉 Not enough time to dig deeper 👉 Strong technical skills but underdeveloped communication skills As a result, analytics often stops before the diagnosis—just listing symptoms without explaining the cause, let alone the cure. We stop at reporting what happened: “Revenue dropped 18%.” 📉 And we hesitate to explain why it happened or what to do next. What we should say: “Revenue dropped 18% because our top customer segment shifted to a competitor with faster delivery options. We should pilot same-day shipping in three test markets.” Ironically, what stakeholders need most—interpretation and direction—is what analysts often avoid. And yet, we don't go to doctors just to confirm we're in pain. We go to understand the cause and find a cure. That’s where data storytelling comes in as it moves us from: ✅ 𝐖𝐡𝐚𝐭 = Symptoms (the metrics and trends) ✅ 𝐒𝐨 𝐖𝐡𝐚𝐭 = Diagnosis (why it’s happening) ✅ 𝐍𝐨𝐰 𝐖𝐡𝐚𝐭 = Treatment (what to do next) If you want your work to drive action, you can’t stop at symptoms. You need to offer meaning and a path forward. What’s one technique that’s helped your team move from reporting to storytelling and action? 🔽 🔽 🔽 🔽 🔽 Craving more of my data storytelling, analytics, AI, and data culture content? Sign up for my newsletter today: https://lnkd.in/gRNMYJQ7 Check out my brand-new data storytelling masterclass: https://lnkd.in/gy5Mr5ky Need a virtual or onsite data storytelling workshop? Let's talk. https://lnkd.in/gNpR9g_K