How to Justify Data Science Work to Business Teams

I pitched a LOT of internal data infrastructure projects during my time leading data teams, and I was (almost) never turned down. Here is my playbook for getting executive buy-in for complex technology initiatives: 1. Research top-level initiatives: Find something an executive cares about that is impacted by the project you have in mind. Example: We need to increase sales by 20% from Q2-Q4 2. Identify the problem to be overcome: What are the roadblocks that can be torn down through better infrastructure? Example: We do not respond fast enough to shifting customer demand, causing us to miss out on significant selling opportunities. 3. Find examples of the problem: Show leadership this is not theoretical. Provide use cases where the problem has manifested, how it impacted teams, and quotes from ICs on how the solution would have greatly improved business outcomes. Example: In Q1 of 2023 multiple stores ran out of stock for Jebb Baker’s BBQ sauce. We knew the demand for the sauce spiked at the beginning of the week, and upon retroactive review could have backfilled enough of the sauce. We lost an expected $3M in opportunities. (The more of these you can provide the better) 4. Explain the problem: Demonstrate how a failure of infrastructure and data caused the issue. Clearly illustrate how existing gaps led to the use case in question. Example: We currently process n terabytes of data per day in batches from 50 different data sources. At these volumes, it is challenging to manually identify ‘needle in the haystack’ opportunities, such as one product line running low on inventory. 5. Illustrate a better world: What could the future world look like? How would this new world have prevented the problem? Example: In the ideal world, the data science team is alerted in real-time when inventory is unexpectedly low. This would allow them to rapidly scope the problem and respond to change. 6. Create requirements: Define what would need to be true both technologically and workflow-wise to solve the problem. Validate with other engineers that your solution is feasible. 7. Frame broadly and write the proposal: Condense steps 1-5 into a summarized 2-page document. While it is essential to focus on a few use cases, be sure not to downplay the magnitude of the impact when rolled out more broadly. 8. Get sign-off: Socialize your ideal world with potential evangelists (ideally the negatively impacted parties). Refine, refine, refine until everyone is satisfied and the outcomes are realistic and achievable in the desired period. 9. Build a roadmap: Lay out the timeline of your project, from initial required discovery sessions to a POC/MVP, to an initial use case, to a broader rollout. Ensure you add the target resourcing! 10. Present to leadership alongside stakeholders: Make sure your biggest supporters are in the room with you. Be a team player, not a hero. Good luck! #dataengineering

🟠 Most data scientists (and test managers) think explaining A/B test results is about throwing p-values and confidence intervals at stakeholders... I've sat through countless meetings where the room goes silent the moment a technical slide appears. Including mine. You know the moment when "statistical significance" and "confidence intervals" flash on screen, and you can practically hear crickets 🦗 It's not that stakeholders aren't smart. We are just speaking different languages. Impactful data people uses completely opposite approach. --- Start with the business question --- ❌ "Our test showed a statistically significant 2.3% lift..." ✅ "You asked if we should roll out the new recommendation model..." This creates anticipation and you may see the stakeholder lean forward. --- Size the real impact --- ❌ "p-value is 0.001 with 95% confidence..." ✅ "This change would bring in ~$2.4M annually, based on current traffic..." Numbers without context are just math. They can be in appendix or footnotes. Numbers tied to business outcomes are insights. These should be front and center. --- Every complex idea has a simple analogy --- ❌ "Our sample suffers from selection bias..." ✅ "It's like judging an e-commerce feature by only looking at users who completed a purchase..." --- Paint the full picture. Every business decision has tradeoffs --- ❌ "The test won", then end presentation ✅ Show the complete story - what we gained, what we lost, what we're still unsure about, what to watch post-launch, etc. --- This one is most important --- ✅ Start with the decision they need to make. Then only present the data that helps make **that** decision. Everything else is noise. The core principle at work? Think like a business leader who happens to know data science. Not a data scientist who happens to work in business. This shift in mindset changes everything. Are you leading experimentation at your company? Or wrestling with translating complex analyses into clear recommendations? I've been there. For 16 long years. In the trenches. Now I'm helping fellow data practitioners unlearn the jargon and master the art of influence through data. Because let's be honest - the hardest part of our job isn't running the analysis. It's getting others to actually use it.

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