How to Communicate Data Findings to Innovate Effectively

How I would visualize a regression: I wouldn’t. IMO, it’s better to visualize the *takeaway* than the analysis. Here’s what I mean: If you know how to run a regression, you were probably taught that there are two “right” ways to report the results: - A regression table - A regression plot Perfect for academic publications or audiences, not so great for business leaders and stakeholders. While regressions tell you the direction and strength of a relationship, they rarely make the insights tangible or compelling. Let's say your organization faces an attrition challenge. Your regression analysis shows employee engagement is the strongest predictor of retention sentiment. You want leaders to prioritize engagement initiatives. You could show them your regression plot... but I doubt you'll change any minds. Instead, visualize the takeaway. The process: ➤ Conduct analysis: Run your regression to identify root causes ➤ Extract key insight: Employee engagement drives retention ➤ Make it tangible: Transform your insight into concrete metrics (e.g., retention sentiment by engagement level) ➤ Design your visualization: Create one powerful visual showing what's at stake The outcome: A simple chart showing disengaged employees are 5x more likely to be flight risks than engaged employees. Is it less technical than the regression plot? Yes. Is it an oversimplification? To some extent. But it delivers the message leaders need to hear, and it’s still grounded in robust statistical analysis. I'm curious: How would you share regression findings? —— ♻️ Repost to help your network. 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

You were just put in charge of the data team at a 2500-person company. And guess what? On day one, the business has already asked about AI and new dashboards. It might be tempting to simply tell your stakeholders "No" or maybe start techno-dumping on why you currently can't implement AI. But that wall of techno babble will simply make their eyes glaze over. You're confusing and not providing clarity. So if you're looking to better to communicate here are a few techniques I use to help get everyone on the same page. 1. Analogies ✅ Do this: Use familiar analogies tailored to their world(do they like to golf, garden, etc) . "AI without reliable data is like building without foundation and on top of sand." ❌ Not that: Don't rattle off system dependencies or mention Kafka, dbt, and data contracts in your first meeting. 2. Impact Framing ✅ Do this: Translate everything into outcomes. "Right now, we can't confidently say which campaigns are actually driving qualified leads, fixing this could help us avoid wasting 100k on a campaign like we did last month." ❌ Not that: "Our data warehouse isn't set up to handle multi-touch attribution at the moment."(ok but why do they care?) 3. Cost of Inaction ✅ Do this: Quantify the downside, "If we skip the groundwork, we risk burning $200K on a model that breaks in production." ❌ Not that: Don't assume vague warnings like "this isn't scalable" will motivate change. 4. Maturity Models ✅ Do this: Show where you are on a crawl-walk-run spectrum, "Right now, we're barely in the 'descriptive' phase; if you ask a question like "How many subscribers did we lose last month due because they had credit cards expire, we wouldn't be able to tell you." ❌ Not that: Don't just say "we're not ready" without context, it sounds like you're saying "We can't" instead of "Here's what comes first." 5. Real-Life Examples ✅ Do this: Share stories of companies that wasted time or money chasing AI too soon. ❌ I guess I don't really know what the opposite is here… Hopefully this was helpful, and let me know if you've used any of these or other techniques to help get on the same page with the business!

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

In #datastorytelling, a lot of the emphasis is placed on communicating an insight to an audience in a clear and effective manner. If you’ve done a good job explaining your insight, the next thing your audience will wonder is the following: 🙋🏻 What should we do about it? 🙋🏻♀️ How do we move forward? 🙋🏾♂️ How does this change things? Today, many analysts and data scientists don’t always attempt to answer these questions and fail to provide recommendations. 👉 They feel it isn’t their job to tell decision-makers what to do. 👉 They feel they lack the business knowledge to make meaningful recommendations. 👉 They don’t want to bias the audience on any specific course of action. 👉 They don't have time to explore and develop solid recommendations. In my view, if you don’t offer recommendations to your audience, your data story is incomplete. Your data story will be less effective because, without a recommended action, it is less likely to inform or influence a decision. No action, no value. Frequently, for each insight, there will be more than one potential course of action and the different options need to be weighed against each other. To me, that sounds like important analysis work and still a data professional’s responsibility. If you don’t feel it’s your job to tell decision-makers what to do, I’ve found many executives welcome suggestions from analysts who are knowledgeable about the data. Just because you’re making a recommendation, that doesn’t mean managers must accept it. It also doesn’t mean they can’t modify what you recommend as they see fit. If you’re worried that you lack the domain expertise to make solid recommendations, that’s an opportunity to partner with the business side before you present your findings. The more you learn about the business, the better your analysis will become too. If you’re nervous about biasing the audience on what they should do, you can mitigate that concern by exploring different options and then recommending the best one based on some objective criteria. If you only have time for the analysis and not for forming recommendations, you need to reevaluate how you’re spending your time. If you continue throwing insights over the wall without accompanying recommendations, you’re not going to see many of your analyses translate into action and value. Don’t prioritize being efficient over being effective. Data stories should drive action and influence positive change. Providing recommendations is a crucial component of effective data storytelling. If you don’t steer your audience to a potential solution and next steps, you can’t expect your data stories to have much impact. Yes, in some cases, it may be difficult to come up with recommendations on your own. That’s why I’ve always viewed #analytics as a team sport with the #data and business teams working together. How have your data stories benefited from having solid recommendations? #businessanalytics #businessintelligence #dataanalytics