Hidden Problems in Aggregated Dashboards

𝗜 𝗵𝗮𝘃𝗲 𝘀𝗲𝗲𝗻 𝗮𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝗯𝘂𝗶𝗹𝗱 𝗯𝗲𝗮𝘂𝘁𝗶𝗳𝘂𝗹 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀 𝗼𝗻 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝘄𝗿𝗼𝗻𝗴 𝗱𝗮𝘁𝗮. 𝗡𝗼𝗯𝗼𝗱𝘆 𝗰𝗮𝘂𝗴𝗵𝘁 𝗶𝘁. 𝗨𝗻𝘁𝗶𝗹 𝘁𝗵𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗺𝗮𝗱𝗲 𝘁𝗵𝗲 𝘄𝗿𝗼𝗻𝗴 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻. 𝗧𝗵𝗮𝘁 𝗶𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘀𝗸𝗶𝗽 𝗘𝗗𝗔. As a Data Analyst, Exploratory Data Analysis is the first thing I do with every dataset — no exceptions. Here is what a proper EDA looks like in practice: 🔍 Profile the data — shape, types, distributions, nulls, duplicates 📉 Investigate missing values — understand WHY they are missing, then treat them accordingly 📦 Detect outliers — IQR, Z-scores, visual box plots — investigate before removing anything 🎯 Select meaningful features — not every column adds value. Filter the noise. 🧹 Wrangle and encode — clean text, fix formats, encode categoricals the right way 🧪 Test assumptions statistically — t-tests, ANOVA, Chi-square — let the data speak 📊 Visualize everything — histograms, scatter plots, heatmaps — patterns appear when you look Dirty data fed into a clean model still produces dirty results. Master EDA and you will not just be an analyst who runs queries — you will be the one the business actually trusts. 📌 Save this for your next project. 💬 What is the first thing you check when a new dataset lands in your inbox? #DataAnalytics #EDA #DataScience #Python #DataCleaning #Statistics #DataAnalyst

𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝟗𝟎 — 𝐃𝐚𝐲 13: 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬t 🔍 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝟏. 𝐃𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Descriptive analysis involves summarizing and describing the main features of a dataset, such as its central tendency, dispersion, and distribution. Descriptive statistics, charts, and graphs are commonly used to present key characteristics of the data. 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: "𝐖𝐡𝐚𝐭 𝐡𝐚𝐩𝐩𝐞𝐧𝐞𝐝?" 𝟐. 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Diagnostic analysis focuses on identifying the causes of observed patterns or outcomes in the data. It involves analyzing relationships between variables, identifying correlations, and conducting root cause analysis to understand why certain events occur. 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: "𝐖𝐡𝐲 𝐝𝐢𝐝 𝐢𝐭 𝐡𝐚𝐩𝐩𝐞𝐧?" 𝟑. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Predictive analysis involves using historical data to make predictions about future events or outcomes. It includes techniques such as regression analysis, time series forecasting, and machine learning algorithms to build predictive models and estimate the likelihood of future events. 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: "𝐖𝐡𝐚𝐭 𝐰𝐢𝐥𝐥 𝐡𝐚𝐩𝐩𝐞𝐧?" 𝟒. 𝐏𝐫𝐞𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: Prescriptive analysis involves recommending actions or decisions based on the insights gained from data analysis. It aims to optimize outcomes by identifying the best course of action given the available data and constraints. Optimization techniques, decision trees, and simulation models are commonly used. 𝐀𝐧𝐬𝐰𝐞𝐫𝐬 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: "𝐖𝐡𝐚𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐰𝐞 𝐝𝐨?" Follow Sudeesh Koppisetti for such informative content on data analytics #DataAnalytics #DataAnalysis #DataCleaning #DataQuality #DataPreprocessing #AnalyticsEngineering #BusinessAnalytics #SQL #Python #PowerBI #Tableau #DataEngineering #ETL #DataPipeline

🚀 𝗗𝗮𝘆 𝟳 : 𝗧𝗼𝗱𝗮𝘆 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲𝗱 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 — 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻 & 𝗚𝗿𝗼𝘂𝗽𝗕𝘆 𝗶𝗻 𝗣𝗮𝗻𝗱𝗮𝘀 📊 🔹 What is Aggregation? Aggregation means combining multiple data points to get summarized results. It helps in understanding patterns like total sales, average values, counts, etc.👉 Common aggregation functions: sum() → Total mean() → Average count() → Number of values max() / min() → Highest / Lowest 🔹 What is GroupBy? GroupBy is used to split data into groups based on some criteria and then apply aggregation functions on those groups. In simple words: Split → Apply → Combine 📌 Basic Syntax: df.groupby('column_name') 📌 Aggregation with GroupBy: df.groupby('column_name')['target_column'].sum() 📌 Multiple Aggregations: df.groupby('column_name')['target_column'].agg(['sum', 'mean', 'count']) 📌 Group by Multiple Columns: df.groupby(['col1', 'col2'])['target_column'].sum() ✨ Why is GroupBy important? Helps in data summarization Used in reports & dashboards Essential for business insights 📈 Learning GroupBy is a big step toward becoming a strong Data Analyst! #Day7 #DataAnalytics #Python #Pandas #LearningJourney #DataScience #GroupBy #Aggregation

📊 Understanding Data Analytics As I continue building my skills, I started with the foundation — understanding what Data Analytics really means. Data Analytics is the process of turning raw data into meaningful insights that help in better decision-making. From business growth to user experience, data plays a key role everywhere. There are four main types of data analytics: 🔹 Descriptive – What happened? 🔹 Diagnostic – Why did it happen? 🔹 Predictive – What might happen? 🔹 Prescriptive – What should be done? Learning this made me realize how powerful data can be when used correctly. Looking forward to exploring more concepts and applying them in real-world scenarios. #DataAnalytics #LearningJourney #AspiringDataAnalyst #DataScience #SQL #Python #CareerGrowth

Global Superstore Data Analytics Project I recently developed a comprehensive data analytics project using the Global Superstore dataset, designed to transform raw business data into actionable insights with improved clarity and decision-making support. The project follows a systematic workflow: • Data Inspection: Understanding dataset structure and data types using .info() • Statistical Analysis: Generating descriptive statistics to uncover initial patterns • Data Cleaning: Handling missing values, duplicates, and inconsistencies • Exploratory Data Analysis: Identifying trends in sales, profit, and customer behavior • Outlier Detection: Detecting and managing anomalies in the dataset • Correlation Analysis: Evaluating relationships between variables for deeper insights • Dashboard Development: Building an interactive dashboard using Python and Streamlit. 🌐 Live Application: https://lnkd.in/dQk9QfXS 💻 Source Code: https://lnkd.in/dD7wSvw5 This project highlights the importance of data analysis and visualization in understanding business performance and reflects my ability to design clean, scalable, and interactive data solutions. I look forward to applying these techniques to more advanced analytics and machine learning projects. #DataAnalytics #Python #Streamlit #Dashboard #DataScience #BusinessAnalytics #LearningJourney

🧹 Day 4: Data Wrangling (The "Dirty Work") Analytics is 20% insights and 80% washing the dishes. 🧼 If Data Analytics were a cooking show, wrangling is the two hours spent washing, peeling, and chopping. It’s not the "glamorous" part, but it is the most vital. 🔍 What is Data Wrangling? It’s the art of taming messy, "wild" data. Raw data is chaotic—wrangling turns that chaos into a structured format ready for analysis. 🛠️ The "Big Three" of the Wrangle: ● Cleaning: Fixing missing values, stripping duplicates, and correcting typos. ● Formatting: Converting data types (the never-ending battle with Date Formats 😅). ● Merging: Joining different tables so they finally speak the same language. 🚀 Why it matters: Raw Data is a Liability. Clean Data is an Asset. 💎 If you skip the wrangle, you aren't providing insights—you’re providing misinformation. You cannot build a high-performance engine on dirty fuel. The Bottom Line: You can’t build a masterpiece on a messy canvas. Wrangling is where real value is created. 💬 Fellow analysts: What is the one thing that always "breaks" your workflow? For me, it’s definitely inconsistent date formats. Let’s commiserate below! 👇 #DataAnalytics #DataWrangling #LearningInPublic #SQL #Python #Day4 #DataSeries

𝑻𝒖𝒓𝒏𝒊𝒏𝒈 𝑹𝒂𝒘 𝑫𝒂𝒕𝒂 𝒊𝒏𝒕𝒐 𝑹𝒆𝒂𝒍 𝑰𝒏𝒔𝒊𝒈𝒉𝒕𝒔: 𝑴𝒚 𝑬𝒏𝒅-𝒕𝒐-𝑬𝒏𝒅 𝑨𝒑𝒑𝒓𝒐𝒂𝒄𝒉 𝒘𝒊𝒕𝒉 𝑷𝒐𝒘𝒆𝒓 𝑩𝑰 Have you ever built a dashboard and still felt like it didn’t really answer anything? I’ve been there. When I started working on data projects, I believed creating dashboards was the end goal. But over time, I realized that dashboards only matter if they actually help someone make a decision. Here’s how I now approach turning raw data into meaningful insights: 𝗙𝗶𝗿𝘀𝘁, 𝗜 𝘁𝗿𝘆 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. Before I even open a dataset, I ask myself—what exactly am I trying to solve? Without a clear question, even the best visuals won’t help. 𝗦𝗲𝗰𝗼𝗻𝗱, 𝗜 𝘄𝗼𝗿𝗸 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗿𝗮𝘄 𝗱𝗮𝘁𝗮 Most of the time, the data isn’t clean. I spend time cleaning, transforming, and structuring it properly. Honestly, this takes more effort than expected. 𝗧𝗵𝗶𝗿𝗱, 𝗜 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Using Python and SQL, I explore trends and relationships to find what really matters in the data. 𝗙𝗼𝘂𝗿𝘁𝗵, 𝗜 𝗯𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 While using Power BI, I focus on keeping things simple and clear. I try to make sure anyone looking at it can quickly understand what’s going on. Finally, I focus on insights This is the most important part for me. I don’t just look at numbers, I try to understand what they mean and how they can help in decision-making. 𝗧𝗵𝗶𝘀 𝘀𝗵𝗶𝗳𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗺𝘆 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆. I stopped thinking about dashboards as outputs and started treating them as tools to solve real problems. For me, Data Science is not about tools. It’s about making data useful. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝘂𝗿𝗻𝗶𝗻𝗴 𝗿𝗮𝘄 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀? #Datascientist #PowerBI #SQL #Python #MachineLearning

Data is everywhere—but insight is rare. As I continue my journey in data science, one thing has become clear: the real value isn’t in collecting data, it’s in interpreting it to drive better decisions. Through my work with Python, SQL, and Tableau, I’ve learned that the most impactful analyses aren’t always the most complex—they’re the ones that clearly answer: 👉 What is happening? 👉 Why does it matter? 👉 What should we do next? From building predictive models to developing dashboards that reduce reporting time by 80%+, I’ve seen how the right data strategy can transform uncertainty into clarity and action. I’m especially excited about the growing role of data in industries like healthcare, finance, and real estate—where insights don’t just improve performance, they improve lives and outcomes. As I continue to grow in this field, I’m focused on strengthening my ability to: • Turn complex data into clear business narratives • Apply statistical thinking to real-world problems • Deliver insights that drive measurable impact If you’re working on interesting data challenges or exploring how to better leverage your data, I’d love to connect and exchange ideas. #DataScience #Analytics #DataDriven #MachineLearning #BusinessIntelligence #SQL #Python #CareerGrowth

Not all data issues are obvious. Some hide in plain sight. I recently worked on a dataset where everything looked correct at first glance. No errors. No missing values. Dashboards were loading fine. But something felt off. The numbers didn’t fully align across reports. After digging deeper, I found the issue wasn’t in the dashboard… it was in how the data was being processed upstream. Here’s what was happening: • A join condition was unintentionally duplicating records • Aggregations were being applied after duplication • Result → inflated metrics in reporting To fix it, I focused on the pipeline logic: • Validated row counts at each stage of transformation • Reworked join conditions to prevent duplication • Applied aggregations at the correct level (before joins) • Added SQL validation checks to catch similar issues early The result? Accurate metrics. Consistent reporting. Restored trust in the data. What’s the most subtle data issue you’ve encountered in your analytics work? #DataAnalytics #SQL #DataEngineering #DataQuality #ETL #DataPipelines #BusinessIntelligence #AnalyticsEngineering #Python #BigData #DataValidation #TechCareers #DataModeling #DataScience #DataGovernance

Your raw data is never ready. Staring at a raw dataset is like looking at a 1,000-piece puzzle without the box. 🧩 Without a framework, you just waste time writing code that leads nowhere. Here is the exact 5-step playbook to turn chaotic data into clear decisions. 1️⃣ Define the Question 🎯 Start with the business problem. If you don't know the destination, no tool will save you. 2️⃣ Data Wrangling 🧹 The "dirty work" (and 80% of the job). Handle missing values, fix date formats, and merge tables so the data is actually usable. 3️⃣ Exploratory Data Analysis (EDA) 🔍 The sandbox phase. Use Pandas or SQL to find outliers, spot early trends, and see how variables interact. 4️⃣ Deep Analysis ⚙️ The heavy lifting. This is where you segment users, apply statistical tests, and uncover the actual "So What?" 5️⃣ Storytelling 🎨 Stakeholders want answers, not Python scripts. Translate your findings into clear, actionable dashboards using Power BI or Tableau. The Bottom Line: Great analysis isn't about complex math; it's about a logical, repeatable process. 💬 Which step takes up the most time in your workflow? For me, it's definitely the Data Wrangling! Let me know below 👇 #DataAnalytics #DataScience #DataStrategy #Python #SQL #Day7 #LearningInPublic