Dave Kaplan’s Post

🚀 The Art of Data Storytelling aka “checking and explaining” In today's data-driven landscape, one of the essential skills highlighted in job descriptions is the ability to “tell a story” with data. A crucial aspect of this is thorough data verification. This involves comparing current datasets with previous versions to identify differences and changes. Once you've pinpointed these changes, it's vital to articulate why they occurred and what factors influenced them. I often reflect on two guiding principles: “Don’t put your hands where your eyes have not checked” and “If you divide X by Y, always ensure that Y is not zero.” Adopting a habit of meticulous data checking not only enhances your analytical skills but also fosters a deeper understanding of how data can narrate the story of a business. In my next post, I will delve into the significance of “words and meanings” in data interpretation. #DataStorytelling #DataAnalysis #DataDriven #Analytics #BusinessIntelligence #DataVerification #StorytellingWithData #ProfessionalDevelopment #ContinuousLearning

Excited to dive into the world of data analysis? 🤩 Before you embark on your journey, it's crucial to steer clear of common pitfalls that could derail your insights. Check out our latest article, "Major Data Analysis Mistakes You Should Avoid," where we explore practical tips to help you navigate the complexities of data analysis with confidence and accuracy.☝️ Whether you're a student, analyst, or business professional, mastering these principles will elevate your skills and ensure your analyses deliver meaningful results. https://hubs.la/Q02lGSqh0 #data #dataanalysis #dataanalytics #analytics #datamistakes #analysismistakes

𝘔𝘰𝘳𝘦 𝘥𝘢𝘵𝘢 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘮𝘦𝘢𝘯 𝘣𝘦𝘵𝘵𝘦𝘳 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯𝘴. As data analysts, one of our biggest challenges is cutting through the noise to find the insights that truly matter. It’s not about how much data you can gather but what you do with it. I’ve found that the real power lies in asking the right questions: 📈 What business problem are we trying to solve? 📈 Which data points provide the clearest picture? 📈 How can we turn these insights into actionable strategies? It’s easy to get caught up in fancy visualizations or large datasets, but at the end of the day, it all comes down to clarity. The clearer the insight, the more impactful the decision. Here’s a tip: next time you’re working with a massive dataset, focus on simplicity. 𝐒𝐭𝐫𝐢𝐩 𝐚𝐰𝐚𝐲 𝐭𝐡𝐞 𝐝𝐢𝐬𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐚𝐧𝐝 𝐝𝐫𝐢𝐥𝐥 𝐝𝐨𝐰𝐧 𝐭𝐨 𝐭𝐡𝐞 𝐤𝐞𝐲 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐭𝐡𝐚𝐭 𝐰𝐢𝐥𝐥 𝐝𝐫𝐢𝐯𝐞 𝐚𝐜𝐭𝐢𝐨𝐧. 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐞 𝐦𝐚𝐠𝐢𝐜 𝐡𝐚𝐩𝐩𝐞𝐧𝐬. PS: How do you handle data overload? What strategies do you use to cut through the noise and find the insights that count? Drop your thoughts in the comments!👇 #DataOverload #DataInsights #BusinessStrategy #DataAnalysis #SimplifyTheData #DataDrivenDecisions #AskTheRightQuestions

Want to get started in Data Analysis III? I have shared few guidelines on getting started with data analysis in my previous post, so let just wrap it up with this 3 extra point. 8. Explore Descriptive #Statistics - Descriptive stats summarize data sets, so you can say things like, “Our average customer spends X” or “The most common complaint is Y.” 9. #Document Your Steps - Always keep a record of what you did and why. It’ll help you explain your findings and improve accuracy in future analyses. 10. Experiment and Keep #Practicing - Practice with free datasets, and learn by experimenting. - Each analysis teaches you a new skill, deepening your understanding. Mastery is in the practice, not the arrival. Every step forward, no matter how small, sharpens your skills and brings you closer to your best. Any question or concern? I'm open to answer them all. You find this helpful, like and repost for others to learn. #Keepgoing #data #dataanalytics #analytics

Becoming a data analyst requires merging art and science. I'd add that cultivating a 'curious mindset' is key. Embracing uncertainty, asking unconventional questions, and leveraging storytelling techniques can elevate analysis from informative to transformative.

How do I know my analysis is done? 👆 This is a problem I still struggle with and it makes sense. I want the most accurate, most comprehensive, most up-to-date analysis possible but this is what I've learned. It can be easy to get lost in the numbers of your analysis. Don't lose focus of what actually matters. ↳Who is your analysis for? ↳When do they need the analysis? ↳What problem are they trying to solve? ↳How can your insights help them solve the problem? I don't think an analysis can ever be fully "complete." There are always additional data points that you could include but... ↳Will they drastically change your analysis? ↳Does the data directly get at the problem at hand? ↳Is the extra analysis worth the amount of time it will take? ↳Do those data points change your insights or recommendations? If the answer is yes, then by all means, include them! But if not, maybe reconsider. Knowing when the analysis is done "enough" is a skill that takes practice. It's also a skill that can increase your value and contributions. #data #dataanalytics #dataanalysis

An analyst’s job is to transform information into insight. But there’s a catch that needs to be addressed, and that even the most gifted analysts need to be reminded of: skills on their own don’t guarantee impact. That’s ultimately up to the data. And the thing is? Not all data has meaning. ... and that’s OK. Part of being a good analyst is knowing when to take a deep breath, acknowledge the fact that there is nothing of value in the dataset, and move on. It's better than finding false meaning in the data and inspiring bad decision-making! Want to read more on the subject? FULL ARTICLE: --> https://lnkd.in/gyfWxX5 #data #analytics #businessintelligence

The most successful data people I've worked with are not the ones who know the most formulas. They are the ones who know how to ask the right questions. "The right questions lead to insights. Insights lead to better decisions. Better decisions lead to growth." Check this guide about what distinguishes top-notch data/analytics people from average data people. #career #data #analytics

𝗧𝗵𝗲 𝟴𝟬/𝟮𝟬 𝗥𝘂𝗹𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 Have you heard of the 80/20 rule in data? In my career, I’ve noticed that: 80% of value comes from 20% of the insights. 80% of the work is spent cleaning or preparing data, leaving only 20% for analysis. The key is to focus on the 20% 𝙤𝙛 𝙞𝙣𝙨𝙞𝙜𝙝𝙩𝙨 that matter most. That’s where we make the greatest impact. How do you prioritise your time in data projects? Let’s discuss—how do you maximize impact in your work? #DataEfficiency #Analytics #ParetoPrinciple #DataInsights #LeadersInData #Data #Insights

Four Key Questions Every Data Analyst Must Master for Meaningful Results Donald J. Wheeler's profound simplification of data analysis through four pivotal questions: [1.] Description: How do we meaningfully summarize data? Consider deciding whether to calculate an average, median, or proportion for a mix of black and white beads. It’s about making data comprehensible. [2.] Probability: What predictions can we make about future samples? Picture a bowl with 1,000 black beads and 4,000 white beads. Probability helps us foresee outcomes like the chance of drawing five black beads from a sample of 50. [3.] Inference: What can a sample tell us about the entire population? From a ratio of black to white beads, we might estimate the proportion of black beads in the total population. [4.] Homogeneity: Are our observations from a single source or multiple? This determines whether our data are comparable across different sets. These questions are not just about analyzing data; they're about interpreting it to derive clear, actionable insights. Wheeler’s emphasis on simplicity through Process Behavior Charts (PBCs) proves that sometimes, the simplest tools are the most effective. As we dive into our data, let’s leverage these foundational questions to ensure our analysis reveals true insights. How do you ensure your analysis cuts through the noise? #DataAnalysis #DataScience #StatisticalThinking #ProcessBehaviorCharts