George Skartados’ Post

If I had to approach any dataset today, this is the simple framework I’d follow: 1. Understand the problem → What question am I trying to answer? 2. Explore the data → What columns exist? → Any missing or unusual values? 3. Clean the data → Handle nulls → Remove duplicates → Fix inconsistencies 4. Analyze → Write queries → Find patterns and trends 5. Validate → Does the result actually make sense? → Cross-check assumptions 6. Communicate → Present insights clearly → Focus on what matters Earlier, I used to jump straight to step 4. Now I’m realizing the real work happens before and after that. Still refining this approach, but it’s already helping me stay more structured. Do you follow a similar process, or something different? 👇 (Feel free to save this if it helps) #DataAnalytics #SQL #DataThinking #Learning #DataWorkflow

🚀 I thought I understood data… until I realized I was calculating it wrong Early on, my approach was simple: If the query runs If the dashboard looks clean If the numbers seem consistent 👉 Then it must be correct Turns out, that’s a dangerous assumption. I came across a case where everything looked perfect — no missing data, no errors, clean trends. But the metric was still wrong. The issue? 👉 Aggregation at the wrong level Fixing that changed the number by ~16%. Same data. Completely different outcome. That’s when I realized: 👉 Data doesn’t fail loudly 👉 It fails silently And the scariest part? Most incorrect metrics still look correct. Since then, I’ve stopped just writing queries — and started questioning the logic behind them. Curious — what’s one mistake that changed how you look at data? #DataAnalytics #SQL #DataEngineering #AnalyticsEngineering #DataQuality #BusinessIntelligence #LearningInPublic

One thing I didn’t expect when working with data: Most problems aren’t clearly defined. There’s no perfect dataset. No exact question. No clean starting point. Earlier, that used to slow me down. I’d spend time trying to figure out: “What exactly am I supposed to find?” But now I approach it differently. Instead of waiting for clarity, I start with: → What does this system look like overall? → What could possibly go wrong here? → If something is inefficient, where would it show up first? From there, the analysis starts to take shape. Not because the data is perfect, but because the direction becomes clearer. That shift made a big difference. Because in real scenarios, you’re not given a problem statement. You’re expected to define it. And honestly, that’s the part I’ve started enjoying the most. Curious - how do you usually approach analysis when the problem isn’t clearly defined? #DataAnalytics #SQL #PowerBI #ProblemSolving #BusinessAnalytics

𝗠𝗼𝘀𝘁 𝗦𝗤𝗟 𝗺𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗵𝗮𝗽𝗽𝗲𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗲𝘃𝗲𝗻 𝘀𝘁𝗮𝗿𝘁𝘀. 𝗔𝗻𝗱 𝘁𝗵𝗲 𝗳𝗶𝘅 𝗶𝘀 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝗼𝗻𝗲 𝘄𝗼𝗿𝗱: 𝙒𝙃𝙀𝙍𝙀. If you want clean data, relevant insights, and fewer messy outputs, you need to master filtering. The 𝘞𝘏𝘌𝘙𝘌 clause helps you keep the right rows and remove the noise. Here’s why that matters:  • It isolates the data you actually care about  • It removes irrelevant records early  • It makes your analysis faster and more accurate  • It is the foundation of data clean-up Why is this so critical? Because real-world data is noisy. 𝗚𝗼𝗼𝗱 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝗱𝗼 𝗻𝗼𝘁 𝘀𝘁𝗮𝗿𝘁 𝗯𝘆 𝗮𝗻𝗮𝗹𝘆𝘇𝗶𝗻𝗴 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴.  1. They start by filtering what matters.  2. That is how you turn raw tables into useful answers. CTA: What’s the first SQL command you learned that actually changed how you worked with data? #SQL #DataAnalytics #DataCleaning #DataAnalyst #LearnSQL

Before I analyze any dataset, I check these 3 things first: Not the charts. Not the SQL. Not the dashboard. Just these: 1. Missing values If important columns have gaps, your insights can be misleading. 2. Data types Numbers stored as text, dates formatted incorrectly, small issues can result to big problems. 3. Duplicates One duplicate row can quietly distort your entire analysis. I didn’t pay enough attention to these when I started. Now, I don’t skip them. Because good analysis doesn’t start with tools It starts with clean, reliable data. If you're learning data analytics, start here. What’s the first thing you check when you get a new dataset? #DataAnalytics #DataCleaning #LearningJourney #Upskilling #Beginners

𝐒𝐭𝐢𝐥𝐥 𝐜𝐨𝐧𝐟𝐮𝐬𝐞𝐝 𝐚𝐛𝐨𝐮𝐭 𝐒𝐐𝐋 𝐉𝐎𝐈𝐍𝐬? 𝐘𝐨𝐮’𝐫𝐞 𝐧𝐨𝐭 𝐚𝐥𝐨𝐧𝐞. 👇 Most people learn joins… But very few actually visualize them. And that’s where things click. 💡 🔹 INNER JOIN → Only matching data 🔹 LEFT JOIN → Everything from left + matches 🔹 RIGHT JOIN → Everything from right + matches 🔹 FULL JOIN → Everything from both sides Simple rule: 👉 Think in terms of data inclusion, not syntax. Because in real-world data engineering… Joins decide whether your data is accurate or misleading. Save this. You’ll need it. 📌 Image Credits : Rocky Bhatia #SQL #DataEngineering #BigData #Analytics #LearnSQL #Databricks

Lately I’ve been noticing one pattern more and more. In real projects, it keeps breaking analytics. The same dataset can produce completely different answers depending on how you define the metric. 🦄 Different filters. 🦄 Different assumptions. 🦄 Different levels of aggregation. And at some point it starts to look like a “data issue”. But it isn’t. It’s a thinking issue. SQL is rarely the bottleneck. The real challenge is defining: 🪀 what exactly you’re measuring 🪀 how consistently it’s calculated 🪀 and what business question it actually answers Feels like the hardest part in analytics is not writing queries - it’s asking the right questions first. And this applies to any tool - the only difference is where the logic breaks. #dataanalytics #businessanalytics #productanalytics #sql #analytics #datascience #datathinking

What Is Data Redundancy? Data redundancy is something we often ignore until it starts causing problems. In simple terms, redundancy means storing the same data multiple times. At first, it feels harmless. But over time: • Model size increases • Performance slows down • Data inconsistencies appear I once worked on a dataset where the same information was repeated across multiple tables. It made relationships complex and calculations unreliable. After cleaning and normalizing the data, the model became lighter, faster, and easier to manage. Sometimes, optimization isn’t about adding more. It’s about removing what’s unnecessary. #DataModeling #PowerBI #DataAnalytics #SQL #BusinessIntelligence #AnalyticsEngineering

Something I’ve been thinking about lately: In data analytics, tools change fast. Today for me it’s: SAS, SQL, Power BI Tomorrow it could be something else. But what actually stays useful? - Understanding data - Thinking logically - Knowing what question to ask - Explaining results clearly I’ve seen people very strong in tools but struggle with basic problem understanding. And I’ve seen people with average tools knowledge but excellent thinking and they stand out. So now I focus less on “which tool” and more on “how I approach problems”. Curious! what do you think matters more in the long run? #CareerGrowth #DataAnalytics #Learning

Data Analytics Fun Fact What is data analytics… really? It’s not just dashboards or coding. In simple terms: Data analytics is about answering one question: “What is this data trying to tell us?” It could be: Why did sales drop last month Why patients are waiting longer Why customers stop using a product Tools like SQL or Microsoft Excel help… But the real skill? Thinking clearly and asking the right questions. Lesson: Data doesn’t speak—you have to interpret it. Repost if you find this helpful 😍 #DataAnalytics #Learning #CareerGrowth #DataScience