Criteria for Making Data AI-Ready

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 - Timeliness: Content is fresh and appropriately timestamped to avoid outdated or misleading information.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

One of the most powerful uses of AI is transforming unstructured data into structured formats. Structured data is often used for analytics and machine learning—but here’s the critical question: Can we trust the output? 👉 Structured ≠ Clean. Take this example: We can use AI to transform retail product reviews into structured fields like Product Quality, Delivery Experience, and Customer Sentiment, etc. This structured data is then fed into a machine learning model that helps merchants decide whether to continue working with a vendor based on return rates, sentiment trends, and product accuracy. Sounds powerful—but only if we apply Data Quality (DQ) checks before using that data in the model. Here’s what DQ management should include at least the following: 📌 Missing Value Checks – Are all critical fields populated? 📌 Valid Value Range: Ratings should be within 1–5, or sentiment should be one of {Positive, Negative, Mixed}. 📌 Consistent Categories – Are labels like “On Time” vs “on_time” standardized? 📌 Cross-field Logic – Does a “Negative” sentiment align with a “Excellent product quality” value? 📌 Outlier Detection – Are there reviews that contradict the overall trend? For example, a review with all negative fields but field "Recommend Vendor” has “Yes". 📌 Duplicate Records – Same review text or ID appearing more than once. AI can accelerate many processes—but DQ management processes is what make that data trustworthy.

Two weeks ago, while I was off radar on LinkedIn. The concept of data readiness for AI hit me hard… Not just as a trend. But as a gap in how most professionals and organizations are approaching this AI race. I’ve been in this field for over a decade now ▸Working with data. ▸Teaching it. ▸Speaking about it. And what I’ve seen repeatedly is this: We’re moving fast with AI. But our data is not always ready. Most data professionals and organizations focus on: ✓ the AI model ✓ the use case ✓ the outcome But they often overlook the condition of the very thing feeding the system: the data. And when your data isn’t ready → AI doesn’t get smarter. → It gets scarier. → It becomes louder, faster... and wrong. But when we asked the most basic questions, ▸Where’s the data coming from? ▸Is it current? ▸Was it collected fairly? That’s when we show what we are ready for. That’s why I created the R.E.A.D. Framework. A practical way for any data leader or AI team to check their foundation before scaling solutions. The R.E.A.D. Framework: R – Relevance → Is this data aligned with the decision or problem you’re solving? → Or just convenient to use? E – Ethics → Who’s represented in the data and who isn’t? → What harm could result from using it without review? A – Accessibility → Can your teams access it responsibly, across departments and tools? → Or is it stuck in silos? D – Documentation → Do you have clear traceability of how, when, and why the data was collected? → Or is your system one exit away from collapse? AI is only as strong as the data it learns from. If the data is misaligned, outdated, or unchecked, → your output will mirror those flaws at scale. The benefit of getting it right? ✓ Better decisions ✓ Safer systems ✓ Greater trust ✓ Faster (and smarter) innovation So before you deploy your next AI tool, pause and ask: Is our data truly ready or are we hoping the tech will compensate for what we haven’t prepared?

𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐈𝐬𝐧’𝐭 𝐀𝐛𝐨𝐮𝐭 𝐁𝐢𝐠𝐠𝐞𝐫 𝐌𝐨𝐝𝐞𝐥𝐬. 𝐈𝐭’𝐬 𝐀𝐛𝐨𝐮𝐭 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐃𝐚𝐭𝐚.  𝐇𝐞𝐫𝐞’𝐬 𝐖𝐡𝐲 𝐃𝐚𝐭𝐚-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐀𝐈 𝐈𝐬 𝐭𝐡𝐞 𝐑𝐞𝐚𝐥 𝐆𝐚𝐦𝐞 𝐂𝐡𝐚𝐧𝐠𝐞𝐫. 1. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:    ↳ Focus on clean, relevant data, not just more data.    ↳ Reduce noise by filtering out irrelevant information.    ↳ Prioritize high-quality labeled data to improve model precision. 2. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:    ↳ Understand the environment your AI operates in. Tailor data accordingly.    ↳ Incorporate real-world scenarios to make AI more adaptable.    ↳ Align data collection with specific business goals for better results. 3. 𝐈𝐭𝐞𝐫𝐚𝐭𝐞 𝐨𝐟𝐭𝐞𝐧:    ↳ Continuously refine data sources to improve model accuracy.    ↳ Implement feedback loops to catch and correct errors quickly.    ↳ Use small, frequent updates to keep your AI models relevant. 4. 𝐁𝐢𝐚𝐬 𝐜𝐡𝐞𝐜𝐤:    ↳ Identify and eliminate biases early. Diverse data leads to fairer AI.    ↳ Regularly audit data for hidden biases.    ↳ Engage diverse teams to broaden perspectives in data selection. 5. 𝐄𝐧𝐠𝐚𝐠𝐞 𝐝𝐨𝐦𝐚𝐢𝐧 𝐞𝐱𝐩𝐞𝐫𝐭𝐬:    ↳ Collaborate with those who understand the data best.    ↳ Leverage expert insights to guide data annotation and validation.    ↳ Involve stakeholders to ensure data aligns with real-world needs. LinkedIn 𝐟𝐨𝐥𝐥𝐨𝐰𝐞𝐫𝐬? Share this post with your network to spark a conversation on why smarter data is the key to AI success. Encourage your connections to think critically about their data strategy. Let's shift the focus from bigger models to better data and make AI truly impactful. Smarter data leads to smarter decisions.  𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐚 𝐫𝐞𝐚𝐥 𝐠𝐚𝐦𝐞 𝐜𝐡𝐚𝐧𝐠𝐞𝐫? ♻️ Repost it to your network and follow Timothy Goebel for more. #DataCentricAI #AIInnovation #MachineLearning #ArtificialIntelligence #DataStrategy

“Garbage in, garbage out” is the reason that a lot of AI-generated text reads like boring, SEO-spam marketing copy. 😴😴😴 If you’re training your organization's self-hosted AI model, it’s probably because you want better, more reliable output for specific tasks. (Or it’s because you want more confidentiality than the general use models offer. 🥸 But you’ll take advantage of the additional training capabilities, right?)  So don’t let your in-house model fall into the same problem! Cull the garbage data, only feed it the good stuff. Consider these three practices to ensure only high-quality data ends up in your organization’s LLM. 1️⃣ Establish Data Quality Standards: Define what “good” data looks like. Clear standards are a good defense against junk info. 2️⃣ Review Data Thoroughly: Your standard is meaningless if nobody uses it. Check that data meets your standards before using it for training. 3️⃣ Set a Cut-off Date: Your sales contracts from 3 years ago might not look anything like the ones you use today. If you’re training an LLM to generate proposals, don’t give them examples that don’t match your current practices! With better data, your LLM will provide more reliable results with less revision needed. #AI #machinelearning #fciso

Data Quality is a blocker to AI adoption. If you don't know what your core data means, who is using it, what they are using it for, and what "good" looks like - it is terrifying to take AI-based production dependencies on data that might change or disappear entirely. As data engineers, ensuring the accuracy and reliability of your data is non-negotiable. Specifically, effective data testing is your secret weapon for building and maintaining trust. Want to improve data testing? Start by... 1. Understand what data assets exist and how they interact via data lineage. 2. Identify the data assets that bring the most value or have the most risk. 3. Create a set of key tests that protect these data assets. (more below) 4. Establish an alerting protocol with an emphasis on avoiding alert fatigue. 5. Utilize continuous testing within your CI/CD pipelines with the above. The CI/CD component is crucial, as automating your testing process can streamline operations, save time, and reduce errors. Some of the tests you should consider include: - Data accuracy (e.g. null values, incorrect formats, and data drift) - Data freshness - Performance testing for efficiency (e.g. costly pipelines in the cloud) - Security and compliance (e.g. GDPR) testing to protect your data - Testing assumptions of business logic. The other reason CI/CD testing is critical is because it informs data producers that something is going wrong BEFORE the changes have been made in a proactive and preventative fashion, and it provides context to both the software engineer and data engineer about what changes are coming, what is being impacted, and what expectations of both sides should be. Data Quality Strategy is not just about the technology you use or the types of tests that have been put in place, but on the communication patterns between producers and consumers put into place when failure events or potential failure events happen. Good luck!

A data-first approach is crucial in model development as it ensures the foundation of the model is built on high-quality, diverse, and well-prepared data. By prioritizing data collection, augmentation, and preprocessing, we enhance the model’s ability to generalize effectively, reducing the risk of bias and overfitting. This approach also emphasizes the importance of feature engineering, outlier detection, and data normalization, all of which play a vital role in capturing the true essence of the problem. By focusing on data integrity and quality from the outset, we set the stage for a robust, accurate, and interpretable model that can be reliably tuned, validated, and maintained over time. 1) High quality Data: The Golden Rule 2) Data Augmentation: Generate Synthetic Data 3) Feature Engineering: Enhance Predictive Power 4) Regularization: Control Model Complexity 5) Hyperparameter Tuning: Optimize Model Settings 6) Early Stopping: Prevent Overfitting 7) Ensemble Methods: Boost Model Performance 8) Cross-Validation: Reliable Performance Estimation 9) Algorithm Selection: Match Algorithm to Problem 10) Error Analysis: Understand Model Weaknesses 11) Model Interpretation: Explainability 12) Data Preprocessing: Cleaning and Normalization 13) Post-Modeling Evaluation: Monitoring and Maintenance 14) Outlier Detection: Identify and Handle Outliers #datafirst, #AI, #ML, #featureengineering, #dataquality, #dataengineering, #modelexplanability, #syntheticdata, #modeloptimization, #overfitting

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

The $100M AI decision every company is getting wrong: Two paths to AI: • 95% choose: Buy AI → Fail → Repeat • 5% choose: Fix Data → Then AI → Win Real disasters I've witnessed: Fortune 500 Retailer: • Spent: $40M on AI transformation • Problem: Inventory data in 12 different systems • AI result: Confidently wrong predictions • Fix needed: Basic data unification Global Bank: • Hired: McKinsey's AI team ($15M) • Problem: Customer data full of duplicates • AI result: Sent offers to dead people • Fix needed: Data cleaning, not AI Healthcare Giant: • Built: ML prediction engine ($25M) • Problem: Medical records inconsistently formatted • AI result: Dangerous false diagnoses • Fix needed: Standardized data entry The brutal truth: The companies winning with AI aren't using fancier models. They're the ones with boring, clean, accessible data. The unsexy AI readiness checklist: • Can anyone find last quarter's data? • Do your systems talk to each other? • Are your data definitions consistent? • Can new hires access what they need? While your competitors announce flashy AI partnerships, quietly spend 6 months fixing your data foundation. When they're explaining expensive failures, you'll be explaining actual results. #AIReality #DataFirst #NoBS P.S. The most dangerous person in your company? The one who says 'Our data is ready for AI' without checking.

Something you can really do right now to get ready for AI that doesn't require new skills or people: Add some additional criteria around AI Readiness to an existing (larger scale) data work stream. Are you uniting some disparate data sources in one warehouse? Consider how you can leverage labeling, organizing, and documenting that data so that humans and AI can understand it more easily. Are you helping teams centralize business logic (instead of locking it away in reports)? If you create shared documentation (non-confidential) on this data, then anyone with access to AI can copy and paste the documentation, and be given a starter SQL query to pull the answers they need. Think about how you explore data for the first time. If you need to filter some data down to just "AMER", are you ready to write that WHERE statement right away? Or do you run a SELECT DISTINCT geo first to see if it's 'AMER', 'Amer', or 'amer', and then write your query? AI has the same problem, but in a typical chat it only gets one try to write the query, and so it has to guess how to write a WHERE statement. This may sound like a small thing, but clear documentation across all of your key business columns like descriptions and sample values can help humans and AI.