Data Cleansing Best Practices for AI Projects

You wouldn't cook a meal with rotten ingredients, right? Yet, businesses pump messy data into AI models daily— ..and wonder why their insights taste off. Without quality, even the most advanced systems churn unreliable insights. Let’s talk simple — how do we make sure our “ingredients” stay fresh? Start Smart → Know what matters: Identify your critical data (customer IDs, revenue, transactions) → Pick your battles: Monitor high-impact tables first, not everything at once Build the Guardrails: → Set clear rules: Is data arriving on time? Is anything missing? Are formats consistent? → Automate checks: Embed validations in your pipelines (Airflow, Prefect) to catch issues before they spread → Test in slices: Check daily or weekly chunks first—spot problems early, fix them fast Stay Alert (But Not Overwhelmed): → Tune your alarms: Too many false alerts = team burnout. Adjust thresholds to match real patterns → Build dashboards: Visual KPIs help everyone see what's healthy and what's breaking Fix It Right: → Dig into logs when things break—schema changes? Missing files? → Refresh everything downstream: Fix the source, then update dependent dashboards and reports → Validate your fix: Rerun checks, confirm KPIs improve before moving on Now, in the era of AI, data quality deserves even sharper focus. Models amplify what data feeds them — they can’t fix your bad ingredients. → Garbage in = hallucinations out. LLMs amplify bad data exponentially → Bias detection starts with clean, representative datasets → Automate quality checks using AI itself—anomaly detection, schema drift monitoring → Version your data like code: Track lineage, changes, and rollback when needed Here's the amazing step-by-step guide curated by DQOps - Piotr Czarnas to deep dive in the fundamentals of Data Quality. Clean data isn’t a process — it’s a discipline. 💬 What's your biggest data quality challenge right now?

𝟰𝟯% 𝗼𝗳 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 Yet most organizations spend 80% on models and 20% on data. Your AI is only as smart as your data is clean. The pattern repeats across industries 👇 📊 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗿𝗶𝘀𝗶𝘀 Informatica's 2025 CDO survey found: ➜ 43% cite data quality as #1 obstacle to AI success ➜ 57% report data is NOT AI-ready ➜ Only 5% of organizations have comprehensive data governance 📉 𝗪𝗵𝗮𝘁 𝗕𝗮𝗱 𝗗𝗮𝘁𝗮 𝗟𝗼𝗼𝗸𝘀 𝗟𝗶𝗸𝗲 The data exists but: → Lives in 47 different systems with no integration → Uses inconsistent formats and definitions → Contains unknown biases that propagate through AI → Lacks lineage—nobody knows where it came from → Has quality issues discovered only after deployment Gartner predicts 30% of GenAI projects abandoned by end of 2025 due to poor data quality. 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Organizations achieving production AI allocate 50-70% of timeline and budget to data readiness. Here's what they build: 1. 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 Completeness: Do you have sufficient volume? Accuracy: Is the data correct? Consistency: Do definitions match across systems? Timeliness: Is data current enough for decisions? Validity: Does data conform to business rules? 2. 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 & 𝗣𝗿𝗼𝘃𝗲𝗻𝗮𝗻𝗰𝗲 For every data point: Where did it originate? How was it transformed? What systems touched it? When was it last validated? You can't trust AI you can't trace. 3. 𝗕𝗶𝗮𝘀 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗠𝗶𝘁𝗶𝗴𝗮𝘁𝗶𝗼𝗻 identify: Sample bias (unrepresentative training data) Historical bias (past discrimination baked in) Measurement bias (flawed data collection) Aggregation bias (combining incompatible data) Then engineer mitigation before deployment. 4. 𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 requires: Model-specific data requirements documentation Continuous data quality monitoring Automated drift detection Regular revalidation cycles 5. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 Build platforms that enable: Extraction from source systems Normalization and transformation Quality dashboards with real-time monitoring Retention controls meeting compliance requirements API access for AI consumption Data readiness is NEVER "complete." It's continuous discipline requiring dedicated ownership. The Data Excellence Test: Ask yourself these questions: ✓ Can you trace any data point from source to consumption? ✓ Can you explain its quality metrics and bias profile? ✓ Do you have automated systems detecting data drift? ✓ Can you demonstrate data governance to regulators? ✓ Do you spend more on data infrastructure than AI models? If you answered "no" to any of these, you're building on quicksand. ♻️ Repost if you've seen AI fail due to data problems ➕ Follow for Pillar 4 tomorrow: Governance & Risk 💭 What percentage of your AI budget goes to data readiness?

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

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

Integrity in AI/ML: Validating and Sanitizing Data When it comes to Artificial Intelligence and Machine Learning, the quality of your data determines the success of your models. Data validation and sanitization can lead to skewed results and compromised model performance. The importance of understanding and implementing effective data validation and sanitization techniques cannot be overstated. Understanding Data Validation and Sanitization Data validation involves verifying the accuracy and quality of the source data before using it in a model. In contrast, sanitization refers to the process of making sure data is free of corruption and safe to use. Security and integrity of data are interdependent. Validating data effectively: steps to follow Data Type and Range Checks: I will ensure that each data input matches its expected type (e.g., numbers, dates) and falls within a reasonable range. This prevents anomalies like negative ages or dates in the future. Consistency and Accuracy Checks: I will verify data across multiple sources for consistency, highlighting discrepancies for further investigation. Format Validation: I will ensure that data adheres to predefined formats, such as using standard date formats or consistent capitalization. Data Sanitization Techniques Removing Sensitive Information: I will carefully identify and remove sensitive or personal data to maintain privacy and comply with regulations. Handling Missing or Incomplete Data: I will use strategies like imputation to fill in missing values or flag them for review, ensuring completeness without bias. Data Transformation: I employ methods such as normalization and encoding to standardize data, making it more uniform and easier to analyze. The automation of validation and sanitization: Automating data validation and sanitization can greatly increase efficiency. I use tools like data validation libraries and custom scripts to streamline these processes, while still maintaining manual checks for complex scenarios. Monitoring and updating on a continuous basis Data quality isn't a one-time task. I continuously monitor data sources and update my validation and sanitization processes to adapt to new data patterns or changes in the data source. Best Practices and Common Pitfalls Key practices include keeping a detailed log of data issues and resolutions, regularly training team members on data quality and importance, and staying updated with the latest in data security. Common pitfalls include overlooking data source changes and underestimating the importance of manual checks. AI/ML requires rigorous data validation and sanitization. By implementing these practices, we ensure our models are built on reliable, high-quality data. Looking forward to sharing more on this and similar topics. #DataScience #MachineLearning #AI #DataQuality #DataValidation #DataSanitization