Data Analytics and Insights

Data analytics is one of the most misunderstood fields. Many people assume it’s just about fancy dashboards and complex algorithms, but the reality is quite different. Here are some common myths that need debunking: ❌ “Data analytics is just about creating dashboards.” ✅ Dashboards are just one piece of the puzzle. The real value comes from asking the right questions, exploring data patterns, and providing actionable insights, not just visualizing numbers. A great analyst helps drive decisions, not just build reports. ❌ “Data doesn’t lie.” ✅ Data might be factual, but how it’s collected, analyzed, and presented can introduce bias. The same dataset can tell different stories depending on how you slice it. Poor sampling, misleading visualizations, or missing context can lead to completely different conclusions. ❌ “You need to know everything about data to be a pro.” ✅ No one knows it all. The best analysts aren’t the ones who memorize every function in SQL or every algorithm in Python—they’re the ones who know how to solve problems, ask the right questions, and keep learning. Being resourceful is more important than knowing everything. 💡 The truth? Data analytics is about curiosity, critical thinking, problem solving and communication. The tools and techniques will change, but the ability to break down complex problems and find meaningful insights will always be valuable.

Data analysis and data analytics aren’t the same thing. 👉 Data analysis is about looking back at what already happened. 👉 Data analytics goes further — it asks why it happened and what might happen next. Think of it like this: Analysis = reading yesterday’s news. Analytics = drafting tomorrow’s headline. 🔹 Example: In a retail shop, analysis might show sales increased last month. 🔹 Analytics would uncover why — maybe a holiday drove more customers — and suggest aligning the next promotion with the festival season. Both are valuable. But it’s analytics that truly drives growth, strategy, and smarter decisions. 🚀 If you’re starting your career, begin with analysis: master tools like Excel, SQL, Power BI, and Python. More importantly, build the habit of asking questions from data. Over time, grow into analytics — that’s where you turn numbers into insights and strategies. #DataAnalysis #DataAnalytics #Excel #SQL #PowerBI #Python

Excited to share a recent project from my 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 class with Professor Andrea Cato, where I explored Arish’s inspiring weight loss journey! This project demonstrates how data analytics and visualization can turn numbers into meaningful insights, guiding informed decisions and tracking progress effectively. Here’s what I accomplished: 🔹 𝗞𝗲𝘆 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: By tracking metrics like daily BPM, steps, calories burned, workout duration, and active energy, I mapped Arish’s cardiovascular and physical health trends, revealing his fitness progress. 🔹 𝗧𝗿𝗲𝗻𝗱 𝗦𝗽𝗼𝘁𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Using Tableau, I developed visuals that highlighted key patterns and trends, allowing insights to emerge visually. 🔹 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲𝗱 𝗩𝗶𝘀𝘂𝗮𝗹 𝗘𝗻𝗰𝗼𝗱𝗶𝗻𝗴: With red shades symbolizing vitality, the visuals became intuitive and engaging. Custom filters allowed users to view data by month or specific dates, adding flexibility in tracking. 🔹 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: I prepared raw data from Arish’s Apple Watch by handling missing values, especially for non-workout days, and normalizing metrics to ensure a smooth analysis. 𝗦𝗸𝗶𝗹𝗹𝘀 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝗲𝗱: This project incorporated data cleaning, trend analysis, feature engineering, and dashboard design—skills essential for data-driven decision-making. It underscored the importance of defining metrics, aligning insights with user needs, and translating data into actionable recommendations. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: By combining analytics, visualization, and storytelling, this project shows how data can be a catalyst for impactful change. With the right insights and user-centered approach, data drives meaningful and informed decision-making. #data #dataStorytelling #UTAustin #fitnessjourney #product

𝐀 𝐂𝐨𝐦𝐦𝐨𝐧 𝐌𝐢𝐬𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐢𝐨𝐧 𝐀𝐛𝐨𝐮𝐭 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 🚨🚨 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 is a rapidly evolving field crucial in decision-making across various industries. However, several misconceptions can hinder its understanding and effective use. When I first came across the data analytics field, I thought it was all about collecting and analyzing data, which made me overlook the critical importance of context, interpretation, and the human element in the analytics process. After diving deeper into it, I understood that while data collection and analysis are fundamental components, they are only part of a larger picture that includes: 📌 understanding the business problem, 📌 defining clear objectives, and 📌 effectively communicating findings. The following were noted throughout my learning journey: 𝐓𝐡𝐞 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 📌 The context in which data is collected and analyzed is vital for deriving meaningful insights. 📌 Without a clear understanding of the business objectives or the specific questions that need to be answered, data analytics can lead to misleading conclusions. 📌 Analysts must be able to interpret data within the framework of the business environment, industry trends, and stakeholder needs. 𝐓𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐭𝐢𝐨𝐧 📌 Data analytics requires critical thinking and domain knowledge to translate data into actionable insights. 📌 Analysts must be skilled in storytelling with data, presenting findings in a way that resonates with decision-makers and drives strategic actions. 𝐓𝐡𝐞 𝐇𝐮𝐦𝐚𝐧 𝐄𝐥𝐞𝐦𝐞𝐧𝐭 📌 I realized that the human element in data analytics cannot be underestimated. 📌 Collaboration among teams, communication of insights, and the ability to adapt to changing circumstances are essential for successful data-driven decision-making. 📌 Relying solely on automated tools and algorithms can lead to a disconnect between data insights and real-world applications.   𝐎𝐯𝐞𝐫𝐚𝐥𝐥 📌 Effective data analytics requires a comprehensive understanding of context, strong interpretative skills, and a collaborative approach. 📌 By recognizing these elements, organizations can harness the full potential of data analytics to drive informed decision-making and achieve their goals. What is another misconception you have heard or had about data analytics? Please share it in the comments section. #EdwigeSongong #ESAnalysis #DataAnalytics #DataStorytelling