Questions to Ask for Data-Driven Decisions

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

I've watched 3-week analyses get ignored in 3-minute meetings You can build the cleanest dashboard. Run the most advanced analysis. And still… nothing changes. Not because the numbers are wrong. Because the right business questions were never asked. 𝐁𝐞𝐟𝐨𝐫𝐞 𝐲𝐨𝐮 𝐬𝐭𝐚𝐫𝐭: → What decision will this influence? If no decision changes, the analysis adds no real value. → Who is the decision maker? Insights need an owner; otherwise, they die in slides. → Why does this problem matter now? Timing defines relevance more than sophistication. → What would success look like? Without clear success criteria, results are meaningless. 𝐃𝐮𝐫𝐢𝐧𝐠 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬: → What action will be taken if numbers change? Metrics without actions create dashboards, not outcomes. → What assumptions are we making? Unchallenged assumptions lead to confident mistakes. → What is the cost of being wrong? False positives and false negatives are not equal. → What data do we actually need? More data often adds noise, not clarity. 𝐁𝐞𝐟𝐨𝐫𝐞 𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐢𝐧𝐠: → What context is missing from the numbers? Seasonality, market shifts, and policy changes change everything. → How will this insight be communicated? If it can't be explained simply, it won't be used. Good analysts don't start with data. They start with decisions, actions, and consequences. For me, the most skipped question is "What decision will this influence?" I've seen entire projects die because no one asked it upfront. Which one do you see skipped most often? ♻️ Repost if someone in your network works with data — 📚 Get 150+ real interview questions (with solutions & frameworks) in our Data Analyst Interview Prep Book: https://lnkd.in/dyzXwfVp 𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 20,000+ readers here → https://lnkd.in/dUfe4Ac6

Most leaders trust the dashboard. Few can explain why it deserves that trust. That gap is where data programs quietly fail. Every KPI, forecast, or AI output is the result of choices made long before a chart appears. If leaders don’t understand those choices, they’re managing outcomes, not systems. Here’s what’s actually happening underneath  and what leaders should ask: 1. Where data starts Ask: Does this data reflect how the business really operates, or just what systems happen to capture? 2. How data gets in Ask: What breaks if this arrives late, incomplete, or duplicated? Who owns that risk? 3. Raw data storage Ask: Can we re-answer questions when strategy changes, or are we locked into old assumptions? 4. Shaping the data (ETL / ELT) Ask: Which business decisions were hard-coded into the platform without anyone realizing it? 5. Cleaning & enrichment Ask: Where are assumptions being added, and who is accountable for them? 6. Fast, trusted storage Ask: Do teams actually use this, or do they export data to spreadsheets to get work done? 7. Defining metrics Ask: If two leaders reference the same KPI, do they mean the same thing? 8. Quality checks Ask: How do we know numbers are wrong before decisions are made? 9. Dashboards & reports Ask: What decision should change because of this view? 10. AI & advanced use cases Ask: What weaknesses will AI expose the moment we try to scale it? 11. Monitoring the system Ask: How do we know the platform is drifting or degrading before results suffer? 12. Governance & access Ask: Where are we protecting the business, and where are we unintentionally slowing it down? 13. Continuous improvement Ask: Who owns evolution once the system is considered “live”? The best data platforms don’t feel complex. They make confident decisions easier and repeatable. If leaders can’t trace how insight becomes action, AI will never move beyond pilots. 🔁 Repost to help other leaders ask better questions about their data and AI 👤 Follow Gabriel Millien for clear, executive-level insights on AI, data, and transformation Image credit: Shalini Goyal. Give her a follow!

People usally start data analysis with dashboards. Good analysts start with questions. Data doesn’t create insights on its own. The quality of analysis depends on the clarity of thinking before any query is written or chart is built. This framework highlights the key questions experienced analysts ask before analyzing any dataset - ensuring analysis leads to decisions, not just reports. 👇 • Define the real business problem before touching the data, because unclear decisions lead to meaningless analysis. • Clearly understand what success looks like by identifying metrics, benchmarks, and expected outcomes. • Verify what data is actually available to avoid building analysis on incomplete or misunderstood sources. • Assess data reliability early, since poor data quality weakens even the best analytical models. • Challenge assumptions continuously to prevent bias, false correlations, and misleading conclusions. • Choose the right dimensions for segmentation to uncover patterns hidden inside aggregated numbers. • Identify the target audience so insights match the level of technical depth and business context required. • Decide the output format intentionally, because how insights are presented shapes how they are used. • Focus on the action the analysis should drive - because analysis without decisions creates no impact. Great analysis isn’t about tools or dashboards. It’s about asking better questions before searching for answers. What’s the first question you ask before starting a data analysis project? 👇

Most brands drown in data. The ones that win have learned to ask better questions. We live in the most data-rich marketing era in history. And yet, 56% of marketers say they still can't accurately identify customer buying habits. Only 39% can access basic demographic data about their own customers. The problem isn't a lack of data. It's a lack of data literacy. After 15 years of running data-driven campaigns, I've learned that the most valuable marketing skill isn't knowing how to read dashboards. It's knowing what question to ask before you open one. The 5 questions I ask before I build any marketing strategy: 1. Where does our highest-LTV customer come from? (Not most — best) 2. What's the single drop-off point that costs us the most revenue? 3. What does our top 20% of customers do that the bottom 80% doesn't? 4. What's the gap between what we think converts and what actually converts? 5. If we doubled retention, what would revenue look like? Data should narrow your decisions, not multiply them. Ask better questions. Find better answers. #DataDrivenMarketing #MarketingAnalytics #GrowthStrategy #DigitalMarketing #CMO

The Baker's Dozen of Questions to Ask Before Beginning Any Significant Analysis Project Preparation is vital to success in any analysis project. As highlighted by Babette Bensoussan, MBA, in our book, Business and Competitive Analysis 2e (2015, Pearson), here are 13 essential questions to ask before starting a complex and high-value project for others: 1. Why is this project being proposed? 2. Has anyone attempted it before? If so, what were the results? 3. Are there any barriers to performing the analysis process that I should be aware of? Could any of these barriers halt progress? 4. What data or information has already been gathered on this topic? What additional information is needed? 5. What analysis processes and systems will be required? 6. Who else in the organization has a stake in the outcomes influenced by this insight work? 7. What Plans, Choices, Actions, and Decisions (PCADs) will be made based on my requested insights? 8. How quickly is an answer needed or desired? What critical factors might impact this timing? 9. What are the client's or customer’s expectations of me? How will they define my success? 10. What does the customer want, need, or not want/need to hear? 11. What resources are available to support me? 12. Can I accomplish what is being asked of me? 13. Is the potential PCAD more valuable than the effort and resources required for analysis? By effectively managing expectations, analysts can foster mutual respect and trust with decision-makers, leading to a clearer understanding of the challenges involved. This proactive approach can help prevent disconnects between the analysis planning process and the decision-making that follows, ultimately benefiting the enterprise. #Preparation #ProjectManagement #Analysts #SettingExpectations #ManagingExpectations

🚀 A Life-Changing Lesson I Learned at Google — That Every Analyst Needs to Hear At Google, I learned the fastest way to generate impact isn't writing code. It's mastering conceptual reasoning before you touch a tool. Let's take Exploratory Data Analysis (EDA). 🙅♀️ Most analysts treat it as a technical race. A checklist of commands to run. 💡 But EDA isn't a coding competition. It's a framework for thinking. It’s not about the commands you run; it’s about the questions you ask. Here’s the framework we used 👇 Notice how the "So What?" is built in from the very beginning. 1. Find the Shape (Observe, Don't Analyze) Before you run a single command, get the 30,000-foot view. Ask: What's the scale (thousands or millions)? What are the extremes? Is the data skewed by a few massive values? Purpose: To understand the landscape before you get lost in the details. 2. Understand the Components (Univariate) Now, zoom in on one variable at a time. Ask: How is this metric distributed? Is it stable, volatile, or clustered? Are outliers mistakes, or are they your most valuable insights? Purpose: To understand the behavior of each individual character in the story. 3. Connect the Dots (Bivariate) Step back and see how the characters interact. Ask: When one metric goes up, what does another do? Which relationships are worth paying attention to — and which are noise? Are you seeing signs of dependency (e.g., engagement rises, then conversions follow)? Purpose: To identify potential cause-and-effect patterns—not to prove them, but to know where to look deeper. 4. Add Context (Time & Segments) Data doesn't exist in a vacuum. Ask: How has this changed over time? What's driving it (seasonality, a product launch)? Which segments (geographies, demographics) behave differently? Purpose: To connect abstract patterns to real-world business decisions. 5. Deliver the "So What" (The Decision) This is the only step that matters. An analysis is useless until it forces a decision. Ask: What does this mean for the business? What should we do next? Purpose: To move from description ("what")->>> interpretation ("so what") ->>> action ("now what"). 💬 The Takeaway: You don’t need a complex tool to master analytics. You need to learn how to observe, connect, and reason. Tools can compute. Analysts must interpret. Comment 👍 if you need my full EDA framework guide

Way too many data projects fail. Not because the analysis was wrong but because the goal was never clear to begin with. Before you dive into the data, make sure you understand what problems you actually try to solve and for whom. 𝗔𝘀𝗸 𝘁𝗵𝗲𝘀𝗲 𝟴 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝗮𝗻𝘆 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗷𝗲𝗰𝘁: 1. What is the actual business question? 2. Who are the stakeholders? 3. What decisions will this analysis support? 4. What data is available? 5. What pieces are missing? 6. What format is expected? 7. What does success look like? 8. What is the timeline and urgency? Answering these upfront can save hours of rework and ensure your results will get used. What’s the one question you wish you had asked before your last data project? ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find these questions helpful. ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field. #dataanalytics #datascience #dataproject #stakeholdermanagement  #careergrowth

Read this book before you start your next analytics project. Because it teaches you one lesson that will save you hundreds of hours and hundreds of thousands of dollars: Your first question matters more than your first dataset. Most failed analytics projects start long before the dashboards do. They start with vague, unanswerable questions: “How do we grow?” “What’s working in marketing?” “Can we be more data-driven?” These aren’t questions. They’re weather forecasts with a KPI attached. A real analytics question is specific, falsifiable, and tied to a decision: → “Which acquisition channel produces customers with the highest 90-day LTV?” → “Which step in the funnel creates the biggest revenue leak?” → “Which product segment is worth prioritizing next quarter?” Ask better questions → get better insights → make better decisions. Every transformation starts there. This principle is so fundamental that it’s the backbone of my new book, Your Fractional CDO. Not tools, not architectures, not AI dashboards with glowing neon charts. Just decision-making clarity. In the book, I break down how to rewrite messy business questions into clean, analytics-ready ones — the exact process I use with founders, CMOs, and product teams. Once you do this, everything else gets easier: You know what data you actually need, you stop building dashboards “just in case”, you avoid 3-month detours into perfect ETLs that solve nothing, and you finally get insights that drive action instead of confusion. Check out Your Fractional CDO on Amazon → https://lnkd.in/dU-kiEUf