How to Prevent AI Model Collapse From Poor Data Quality

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 Incomplete or outdated datasets Duplicate or inconsistent records Noise from irrelevant or poorly labeled data Data quality The result?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

If all you're monitoring is your agent's outputs, you're fighting a losing battle. Beyond even embedding drift, output sensitivity issues, and the petabytes of structured data that can go bad in production, AI systems like agents bring unstructured data into the mix as well — and introduce all sorts of new risks in the process. When documents, web pages, or knowledge base content form the inputs of your system, poor data can quickly cause AI systems to hallucinate, miss key information, or generate inconsistent responses. And that means you need a comprehensive approach to monitoring to resolve it. Issue to consider: - Accuracy: Content is factually correct, and any extracted entities or references are validated. - Completeness: The data provides comprehensive coverage of the topics, entities, and scenarios the AI is expected to handle, where gaps in coverage can lead to “I don’t know” responses or hallucinations. - Consistency: File formats, metadata, and semantic meaning are uniform, reducing the chance of confusion downstream. - Timeliness: Content is fresh and appropriately timestamped to avoid outdated or misleading information. - Validity: Content follows expected structural and linguistic rules; corrupted or malformed data is excluded. - Uniqueness: Redundant or near-duplicate documents are removed to improve retrieval efficiency and avoid answer repetition. - Relevance: Content is directly applicable to the AI use case, filtering out noise that could confuse retrieval-augmented generation (RAG) models. While a lot of these dimensions mirror data quality for structured datasets, semantic consistency (ensuring concepts and terms are used uniformly) and content relevance are uniquely important for unstructured knowledge bases where clear schemas and business rules don't often exist. Of course, knowing when an output is wrong is only 10% of the challenge. The other 90% is knowing why and how it resolve it fast. 1. Detect 2. Triage. 3. Resolve. 4. Measure. Anything less and you aren't AI-ready. #AIreliability #agents

What Happens When You Feed Junk to AI? Imagine you are transforming an AI in ITSM with millions of records. But here’s the catch: Mandatory fields filled with noise: “Issue reported.” “Need help.” “Not working.” - Descriptions missing or vague. - Similarity models underperforming. - Semantic embeddings delivering low-confidence predictions. The AI isn’t broken. The #data is. We’ve all heard “Garbage in, garbage out.” But here’s the modern version: - “Feed your AI junk, and it becomes a guessing machine.” So hit pause. Do this: Perform data analysis and solution design as part of data readiness - Enforce description quality. - Use scoped metadata like CI, category, assignment group etc. - Train humans to speak machine. - Make data quality a cultural commitment, not a checkbox. Because AI isn’t magic. And like any formula, it’s only as smart as what you put in it. Want #AI to predict, recommend, and resolve? Start by feeding it real signals, not static noise. #ServiceNow #AIinITSM #ITSM #Technology #Data #PredictiveIntelligence #Automation #DigitalTransformation