Data Cleaning and Preparation

My AI was ‘perfect’—until bad data turned it into my worst nightmare. 📉 By the numbers: 85% of AI projects fail due to poor data quality (Gartner). Data scientists spend 80% of their time fixing bad data instead of building models. 📊 What’s driving the disconnect? Incomplete or outdated datasets Duplicate or inconsistent records Noise from irrelevant or poorly labeled data Data quality The result? Faulty predictions, bad decisions, and a loss of trust in AI. Without addressing the root cause—data quality—your AI ambitions will never reach their full potential. Building Data Muscle: AI-Ready Data Done Right Preparing data for AI isn’t just about cleaning up a few errors—it’s about creating a robust, scalable pipeline. Here’s how: 1️⃣ Audit Your Data: Identify gaps, inconsistencies, and irrelevance in your datasets. 2️⃣ Automate Data Cleaning: Use advanced tools to deduplicate, normalize, and enrich your data. 3️⃣ Prioritize Relevance: Not all data is useful. Focus on high-quality, contextually relevant data. 4️⃣ Monitor Continuously: Build systems to detect and fix bad data after deployment. These steps lay the foundation for successful, reliable AI systems. Why It Matters Bad #data doesn’t just hinder #AI—it amplifies its flaws. Even the most sophisticated models can’t overcome the challenges of poor-quality data. To unlock AI’s potential, you need to invest in a data-first approach. 💡 What’s Next? It’s time to ask yourself: Is your data AI-ready? The key to avoiding AI failure lies in your preparation(#innovation #machinelearning). What strategies are you using to ensure your data is up to the task? Let’s learn from each other. ♻️ Let’s shape the future together: 👍 React 💭 Comment 🔗 Share

"Garbage in, garbage out" isn't just a saying - it's a lesson Meta learned the hard way when they had to shut down their Galactica AI after just 3 days due to generating convincing but false scientific papers. The culprit? Insufficient data preprocessing. 🎓 I recently recorded a session for Andrew Brown's Free GenAI Bootcamp, where I broke down the critical steps of data preprocessing for GenAI applications. Using a Japanese Language Learning AI assistant as our case study, we explored how proper data preparation can make or break your AI system. The devil is in the preprocessing details. Consider these examples from our Japanese language AI tutor: 🎤 Audio Preprocessing: Not removing background noise or standardizing volume levels can lead your AI to focus on irrelevant patterns - like mistaking keyboard clicks for pronunciation errors. 📀 Data Quality: Imagine having thousands of audio files with inconsistent naming ("student_123_lesson1.mp3" vs "s123-l1.mp3") and missing proficiency data. Without proper standardization, your AI might end up recommending advanced pitch accent exercises to complete beginners, or mixing up different students' learning progressions. These aren't just technical hiccups - they directly impact the learning experience of real students trying to master a new language! Here are some key topics I covered during the session: ◦Data Quality Assessment: When is your data really "clean"? ◦Smart Preprocessing: Translating the data for foundational models ◦Feature Engineering: Creating meaningful AI inputs ◦Data Privacy: Protecting user information while maintaining utility 💡 Most valuable takeaway: The most sophisticated AI model can't overcome poor data preparation. Success in GenAI isn't just about the model - it's about the meticulous work that happens before training even begins. 🎯 Who should watch: ◦Data professionals diving into GenAI ◦Developers building AI applications ◦Teams working on language learning tech ◦Anyone interested in practical AI implementation The link to the full session is in the comments! #GenAI #DataScience #ArtificialIntelligence #MachineLearning #DataPreprocessing #TechEducation

Cleaning data isn't just removing blanks. It is about making intentional choices that shape your analysis. In my recent churn analysis for a fitness app, here is how I prepped 3 messy tables for merging: What I found & how I handled it: 1. account_info table - 72% of churn_status cells were empty → Replaced with 'N' (assuming no churn unless recorded). - Why? Preserved all customer records while ensuring binary consistency for analysis. 2. customer_support table - 95% of comments column empty --> Column removed (not needed for analysis). - 39 rows with '-' in channel → Replaced with 'unknown'. - Renamed state to ticket_state to avoid merge confusion, as we already had a state column in another table. - Changed user_id to TEXT for consistent merging later. 3. user_activity table - Clean at ingestion. I only changed user_id to TEXT for merging later. Outcome: --> 3 clean tables, ready for merging and analysis, without losing meaningful records or introducing bias. Lesson: --> Data cleaning is where analysis succeeds or fails. Small choices (like filling vs. dropping blanks, renaming columns early, and consistent ID types) set the stage for reliable insights. Tools used: Python (Pandas) for validation and cleaning. Check the attached screenshot to have a glimpse of how I started the data cleaning processes. P.S. Have you faced a tricky data cleaning decision recently? How did you handle it?

Most FP&A teams don’t struggle with modeling. They struggle with inputs. Messy source files. Inconsistent labels. Different naming conventions from every region or business unit. And every month, someone has to manually clean it all before the forecast can even begin. There’s a better way — and Excel’s MAP + LAMBDA combo is quietly becoming the fastest method to automate this cleanup work. MAP allows you to run a function across each item in an array. LAMBDA lets you define what that function is. Together, they create a “cleanup engine” that standardizes inputs before they ever touch your model. A simple example: You receive cost center names with random spacing, inconsistent casing, and the occasional typo. You can build a LAMBDA to fix the text, then wrap MAP around it: =MAP(A2:A200, LAMBDA(x, PROPER(TRIM(x)))) Every item is cleaned instantly. No helper columns. No manual transformations. No repetitive monthly work. Key FP&A use cases: • Standardizing vendor names or account descriptions from AP extracts • Cleaning SKU labels or product hierarchies for margin models • Normalizing department names across regions • Fixing inconsistent formatting in assumptions sheets • Preparing raw ERP exports for driver-based models Why it matters: • Reduces human error • Makes models more repeatable • Speeds up every monthly/quarterly cycle • Allows analysts to focus on analysis, not preparation What’s one recurring data-cleanup task you wish Excel could handle automatically? I’d love to hear real examples from your team. If you want help building MAP/LAMBDA workflows into your FP&A toolkit, I share advanced Excel techniques tailored specifically for finance teams — follow along for more.

Data cleaning is a challenging task. Make it less tedious with Python! Here’s how to use Python to turn messy data into insights: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗣𝗮𝗻𝗱𝗮𝘀: Pandas is your go-to library for data manipulation. Use it to load data, handle missing values, and perform transformations. Its simple syntax makes complex tasks easier.     2. 𝗛𝗮𝗻𝗱𝗹𝗲 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮: Use Pandas functions like isnull(), fillna(), and dropna() to identify and manage missing values. Decide whether to fill gaps, interpolate data, or remove incomplete rows.     3. 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗲 𝗮𝗻𝗱 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺: Clean up inconsistent data formats using Pandas and NumPy. Functions like str.lower(), pd.to_datetime(), and apply() help standardize and transform data efficiently.     4. 𝗗𝗲𝘁𝗲𝗰𝘁 𝗮𝗻𝗱 𝗥𝗲𝗺𝗼𝘃𝗲 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀: Ensure data integrity by removing duplicates with Pandas drop_duplicates() function. Identify unique records and maintain clean datasets.     5. 𝗥𝗲𝗴𝗲𝘅 𝗳𝗼𝗿 𝗧𝗲𝘅𝘁 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Use regular expressions (regex) to clean and standardize text data. Python’s re library and Pandas str.replace() function are perfect for removing unwanted characters and patterns.     6. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗦𝗰𝗿𝗶𝗽𝘁𝘀: Write Python scripts to automate repetitive cleaning tasks. Automation saves time and ensures consistency across your data-cleaning processes.     7. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮: Always validate your cleaned data. Check for consistency and completeness. Use descriptive statistics and visualizations to confirm your data is ready for analysis.     8. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗰𝗲𝘀𝘀: Keeping detailed records helps maintain transparency and allows others to understand your steps and reasoning. By using Python for data cleaning, you’ll enhance your efficiency, ensure data quality, and generate accurate insights. How do you handle data cleaning in your projects? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #python #datacleaning #careergrowth

Data cleaning is 80% of a data analyst’s job. Yet, many aspiring analysts rush through it—leading to messy reports, incorrect insights, and bad decisions. If your data is wrong, everything built on top of it is wrong. Here are 6 steps to clean your data properly: 1. Understand the Data Before Cleaning It Before jumping into fixing errors, ask: → Where is the data coming from? → What types of values does it contain? → Are there obvious quality issues? A quick exploration helps you spot problems early. 2. Handle Missing Values the Smart Way → Missing data is common—but how you handle it matters. → Remove missing values if they’re insignificant → Impute values using the mean, median, or mode if they matter → Investigate whether missing data reveals a deeper issue 3. Get Rid of Duplicates Duplicate data skews analysis and makes insights unreliable. → Identify duplicate records → Keep only the unique entries → Verify that removing them doesn’t impact the dataset 4. Standardize Formatting & Structure → Messy formats = Confusing results. → Convert dates to a consistent format (DD-MM-YYYY or YYYY-MM-DD) → Ensure text labels follow the same structure (e.g., "USA" vs. "U.S.") → Keep units consistent (e.g., km vs. miles) 5. Detect and Handle Outliers → Not all extreme values are errors—but some are. → Use statistical methods (like Z-score, IQR) to identify them → Decide whether to remove, adjust, or analyze them further 6. Validate Before You Use the Data Before you analyze, double-check: → Does the cleaned data match the original intent? → Are there still inconsistencies? → Does it align with expected business rules? Good data analysts don’t rush this step—because bad data leads to bad decisions. P.S. What’s been your biggest struggle with data cleaning? P.P.S. Wanna learn the basic of data cleaning, hop on the call here → https://lnkd.in/dHAGPBii -- 👋 I’m Jayen T. , Dedicated to helping aspiring data analysts thrive in their careers. ➕ Follow MetricMinds.in for more tips, insights, and support on your data journey!

90% of data projects fail because of bad data, not bad models. (Learnt it the hard way!) 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐭𝐡𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠: Everyone talks about fancy algorithms and cutting-edge models. But your analysis is only as good as your data. And most data? It's a mess. Duplicates. Missing values. Inconsistent formats. Different time zones. 𝐓𝐡𝐞 4-𝐬𝐭𝐞𝐩 𝐝𝐚𝐭𝐚 𝐜𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐈 𝐮𝐬𝐞 𝐟𝐨𝐫 𝐞𝐯𝐞𝐫𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭: 𝟏. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐚𝐤𝐞 & 𝐀𝐮𝐝𝐢𝐭 → Check schema, completeness, and validity first → Hunt for duplicates and PII data → Visualize missing patterns (they tell a story) → Master this: Your foundation determines everything 𝟐. 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 – 𝐅𝐢𝐱 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 & 𝐄𝐫𝐫𝐨𝐫𝐬 → Standardize labels (yes/Yes/YES → yes) → Merge duplicates the smart way → Fix units and time zones NOW, not later → Pro tip: Document every transformation 𝟑. 𝐈𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧, 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠 & 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐏𝐫𝐞𝐩 → Handle missing data based on business logic → Encode categoricals without data leakage → Scale numerics appropriately → Engineer features that actually matter 𝟒. 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞, 𝐒𝐩𝐥𝐢𝐭 & 𝐏𝐚𝐜𝐤𝐚𝐠𝐞 → Recheck data integrity post-cleaning → Split datasets properly (no leakage!) → Version your outputs → Generate validation reports 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: ↳ Clean data = trustworthy insights ↳ Proper prep saves weeks of debugging ↳ Stakeholders trust consistent, validated data ↳ Your models actually work in production Remember: Great models start with great data. Not the other way around. Master data cleaning. Build analyses that actually deliver value. Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp ♻️ Save this framework for your next data project 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 18,000+ readers here → https://lnkd.in/dUfe4Ac6

Ever wondered why two dashboards tell two different stories from the same data? It’s usually not the model. It’s not the BI tool. It’s dirty SQL tables. Clean data isn’t a “nice-to-have” — it’s the baseline for honest analytics. Here’s a sharper, no-fluff breakdown of 13 SQL data-cleaning moves every data engineer & analyst should master: 🔹 Handle missing values → Use COALESCE to replace NULLs with safe defaults 🔹 Drop irreparable records → Delete rows with critical NULLs 🔹 Normalize text casing → Convert strings to lowercase for reliable matching 🔹 Detect duplicates → Use GROUP BY to surface repeated records 🔹 Remove duplicates (intelligently) → Keep one valid row, delete the rest 🔹 Trim whitespace → Remove leading/trailing spaces that break joins 🔹 Split composite fields → Extract first/last names (basic cases) 🔹 Unify date formats → Convert messy date strings into a standard format 🔹 Clean special characters → Strip symbols, keep meaningful values 🔹 Spot outliers → Identify values beyond logical thresholds 🔹 Remove invalid extremes → Delete outliers when they corrupt analysis 🔹 Fix inconsistent labels → Correct typos and fragmented categories 🔹 Standardize phone numbers → Keep digits only for uniformity 📌 Bottom line: Bad data quietly sabotages decisions. A few disciplined SQL cleanup steps can dramatically improve dashboards, models, and business trust. Clean tables → clean insights → better decisions. CC:  Greg Coquillo

Day 2 – Data Cleaning & Data Quality in PySpark (Retail Domain – In Depth) What is Data Cleaning? Data Cleaning is the process of identifying, correcting, or removing inaccurate, incomplete, duplicate, or invalid data to ensure data quality before analytics or reporting. #Interview: Data cleaning ensures data is accurate, consistent, and reliable for business decisions. #Why Data Cleaning Is Important (Very Common Interview Question) Without data cleaning: Revenue reports become incorrect Dashboards show wrong KPIs Business decisions fail Trust in data is lost #Interviewline to remember: Bad data leads to bad decisions, no matter how good the analytics is. #DataCleaning in Retail Context Retail data comes from: POS systems Online orders Third-party vendors. Main Data Cleaning Activities (Core Notes) 1. Handling Null Values What Are Nulls? Null means missing or unknown data. #InterviewExplanation I first identify critical and non-critical columns. Critical columns must not be null. #Example (PySpark) sales_df.dropna(subset=["price", "quantity"]) 2. Removing Duplicate Records Why Duplicates Occur? Network retries Reprocessing Kafka data POS failures #InterviewExplanation Duplicates can inflate revenue and must be handled carefully. #Example: sales_df.dropDuplicates(["txn_id"]). 3. Validating Business Rules Retail Rules: Quantity > 0 Price > 0 Order date ≤ today #InterviewExplanation Validation ensures only business-meaningful data is processed. #Example : sales_df.filter((col("quantity") > 0) & (col("price") > 0)) 4. Standardizing Data Why? Different systems → different formats. #InterviewExplanation Standardization avoids join mismatches and reporting errors. #Example: from pyspark.sql.functions import upper, trim sales_df.withColumn("store_id", upper(trim(col("store_id")))) 5. Handling Invalid Data Invalid Data Examples Negative price Future order dates Corrupt records #InterviewExplanation: Invalid records are either rejected or quarantined for auditing. Data cleaning is the process of ensuring data accuracy, completeness, and consistency before analysis. In retail pipelines, I handle null values, remove duplicates, validate business rules, standardize formats, and reject invalid records. This ensures reliable revenue and inventory analytics. Karthik K. #DataEngineering #PySpark #ApacheSpark #DataCleaning #DataQuality  #RetailAnalytics #ETL #SparkSQL #InterviewPreparation #VineshDataEngineer #RetailBusinessContext Sales data comes from thousands of POS systems and online channels. Due to retries, partial loads, and vendor issues, the data often contains errors. END-TO-END PYSPARK CODE: