Core Data Quality Principles for Artificial Intelligence

AI is only as smart as its data. Bad data breaks everything. Good data builds the future. AI in healthcare is not magic. It is math, logic, and trust—stacked on a backbone of clean, connected data. Here’s the truth: • AI can’t fix broken data. • Automation fails if the data is a mess. • Connected care needs a solid data foundation. Think of data as the bones of a body. If the bones are weak, nothing stands. If the bones are strong, you can build muscle, move fast, and stay healthy. To build smarter AI and real connected care, start with these pillars: 1/ Data Quality:   Garbage in, garbage out.   Every record, every field, every update must be right.   No duplicates. No missing info. No errors.   Clean data is the first rule. 2/ Interoperability:   Systems must talk to each other.   Break down silos.   Use standards like HL7, FHIR, and APIs.   If your data can’t move, your care can’t connect. 3/ Privacy and Security:   Trust is everything.   Encrypt data.   Control access.   Follow HIPAA and GDPR.   Patients own their data—protect it. 4/ Governance:   Set the rules.   Who can see what?   Who can change what?   Audit trails, clear roles, and strong policies keep data safe and useful. 5/ Infrastructure Flexibility:   Cloud, on-prem, or hybrid—pick what fits.   Scale up as you grow.   Don’t get locked in.   Your data backbone must bend, not break. 6/ Continuous Improvement:   Data is never “done.”   Check, clean, and update all the time.   Train your team.   Make data quality a habit, not a project. When you get these right, you unlock: • Smarter automation • Real-time insights • Scalable AI that learns and adapts • Seamless patient care across systems The best AI in the world can’t save bad data. But with the right data backbone, you build care that connects, scales, and lasts. Start with better data. Build the future of healthcare—one clean record at a time.

Sanjeev Mohan dives into why the success of AI in enterprise applications hinges on the quality of data and the robustness of data modeling. Accuracy Matters: Accurate, clean data ensures AI algorithms make correct predictions and decisions. Consistency is Key: Consistent data formats allow for smoother integration and processing, enhancing AI efficiency. Timeliness: Current, up-to-date data keeps AI-driven insights relevant, supporting timely business decisions. Just as a building needs a blueprint, AI systems require robust data models to guide their learning and output. Data modeling is crucial because: Structures Data for Understanding: It organizes data in a way that machines can interpret and learn from efficiently. Tailors AI to Business Needs: Customized data models align AI outputs with specific enterprise objectives. Enables Scalability: Well-designed models adapt to increasing data volumes and evolving business requirements. As businesses continue to invest in AI, integrating high standards for data quality and strategic data modeling is non-negotiable.

💡 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐫𝐮𝐬𝐭𝐰𝐨𝐫𝐭𝐡𝐲 𝐀𝐈 𝐬𝐭𝐚𝐫𝐭𝐬 𝐰𝐢𝐭𝐡 𝐚𝐝𝐞𝐪𝐮𝐚𝐭𝐞 𝐚𝐧𝐝 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐝𝐚𝐭𝐚 👁️🗨️When talking about responsible AI, we often think about transparency or explainability of AI outputs. But everything starts one step earlier for the training data needs to be compatible with the principles of data adequacy and the principle of data reliability. ➡️Whether by compliance for high-risk systems with Article 13 or for Generative AI used in support of human decision-making, data quality is necessary for compliant AI systems. In my PhD thesis I highlighted the cardinal importance of ensuring, cumulatively, both these principles during both the selection (regarding the source of collection) and the data chosen (regarding the information it contains) ➡️Adequacy means the data must truly fit the purpose it serves. For example, if an AI system is trained to detect suspicious transactions, using data from one precise sector such as investment cases of AML, applying it elsewhere without it being tailored to the sector it is deployed in (Private banking for instance) it is more limey to produce misleading results. The data simply might not be adequate for this new context. ➡️Reliability means data must be accurate, verified, and consistent over time. Thus, even if the data is adequate it also must be reliable. For instance, in the context of AI systems used in the criminal justice field, if criminal offenders profiles contain outdated or biased information, the model will quietly embed those errors into its predictions, no matter how advanced the model is. It can lead to harsher sentences and measures taken against this person ⚙️Adequacy and reliability must work together, Adequacy being the first step and reliability being the second during the selection of training data. ⚙️AI systems become not only more efficient but also more likely compliant with the sector of deployment. On the side, they are more ethical, accountable, and compatible with human values and fundamental rights, by preventing discrimination in some sectors.

I’m obsessed with one truth: 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 is AI’s make-or-break. And it's not that simple to get right ⬇️ ⬇️ ⬇️ Gartner estimates an average organization pays $12.9M in annual losses due to low data quality. AI and Data Engineers know the stakes. Bad data wastes time, breaks trust, and kills potential. Thinking through and implementing a Data Quality Framework helps turn chaos into precision. Here’s why it’s non-negotiable and how to design one. 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗿𝗶𝘃𝗲𝘀 𝗔𝗜 AI’s potential hinges on data integrity. Substandard data leads to flawed predictions, biased models, and eroded trust. ⚡️ Inaccurate data undermines AI, like a healthcare model misdiagnosing due to incomplete records.   ⚡️ Engineers lose their time with short-term fixes instead of driving innovation.   ⚡️ Missing or duplicated data fuels bias, damaging credibility and outcomes. 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗮 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 A data quality framework ensures your data is AI-ready by defining standards, enforcing rigor, and sustaining reliability. Without it, you’re risking your money and time. Core dimensions:   💡 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆: Uniform data across systems, like standardized formats.   💡 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: Data reflecting reality, like verified addresses.   💡 𝗩𝗮𝗹𝗶𝗱𝗶𝘁𝘆: Data adhering to rules, like positive quantities.   💡 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗻𝗲𝘀𝘀: No missing fields, like full transaction records.   💡 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀𝘀: Current data for real-time applications.   💡 𝗨𝗻𝗶𝗾𝘂𝗲𝗻𝗲𝘀𝘀: No duplicates to distort insights. It's not just a theoretical concept in a vacuum. It's a practical solution you can implement. For example, Databricks Data Quality Framework (link in the comments, kudos to the team Denny Lee Jules Damji Rahul Potharaju), for example, leverages these dimensions, using Delta Live Tables for automated checks (e.g., detecting null values) and Lakehouse Monitoring for real-time metrics. But any robust framework (custom or tool-based) must align with these principles to succeed. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲, 𝗕𝘂𝘁 𝗛𝘂𝗺𝗮𝗻 𝗢𝘃𝗲𝗿𝘀𝗶𝗴𝗵𝘁 𝗜𝘀 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 Automation accelerates, but human oversight ensures excellence. Tools can flag issues like missing fields or duplicates in real time, saving countless hours. Yet, automation alone isn’t enough—human input and oversight are critical. A framework without human accountability risks blind spots. 𝗛𝗼𝘄 𝘁𝗼 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 ✅ Set standards, identify key dimensions for your AI (e.g., completeness for analytics). Define rules, like “no null customer IDs.”   ✅ Automate enforcement, embed checks in pipelines using tools.   ✅ Monitor continuously, track metrics like error rates with dashboards. Databricks’ Lakehouse Monitoring is one option, adapt to your stack.   ✅ Lead with oversight, assign a team to review metrics, refine rules, and ensure human judgment. #DataQuality #AI #DataEngineering #AIEngineering

Serious question: Which of these 12 foundations is missing in your current AI architecture? Very few talk about what actually makes AI Agents work in production. It’s not prompts. It’s not models. It’s data foundations. Agentic AI systems don’t run on magic. They run on ingestion pipelines, governed datasets, vector retrieval, streaming events, and reliable storage layers. Without strong data infrastructure, agents hallucinate, break workflows, and make unsafe decisions. This guide breaks down the 12 data foundations every production-grade agentic system needs: 1. Data Ingestion – Brings data from apps, APIs, and files into unified raw storage. 2. ETL / ELT Pipelines – Cleans, validates, and transforms raw inputs into analytics-ready datasets. 3. Feature Stores – Centralize reusable features for consistent training and real-time inference. 4. Vector Pipelines – Power RAG by chunking documents, generating embeddings, and enabling semantic retrieval. 5. Metadata Management – Captures schemas, ownership, and tags so agents understand available data. 6. Data Governance – Enforces policies, access controls, audits, and compliance across all data assets. 7. Data Quality Checks – Detect anomalies early and prevent bad data from silently breaking agents. 8. Data Lineage – Tracks data from source to consumption for traceability and impact analysis. 9. Data Warehouses & Lakes – Provide centralized analytical storage queried by humans, models, and agents. 10. Streaming Data – Enables real-time ingestion so agents can react instantly to events. 11. Data Labeling – Converts raw samples into training-ready datasets through human and AI feedback. 12. Data Versioning – Makes experiments reproducible and production rollbacks possible. Together, these form the operating backbone of Agentic AI. Models reason. Agents act. But data determines whether they succeed in the real world. If your agent stack lacks even a few of these layers, you don’t have Agentic AI yet - you have demos.

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?

If your data is a mess, your AI will lie to you…confidently. And that’s the part of AI transformation no one wants to headline. Everyone wants to talk about agents, copilots, and automation at scale. But the least sexy part of AI is actually the most important: data quality, process discipline, and governance. MIT Sloan, McKinsey, and BCG all point to the same root cause when AI underdelivers: Most failures start with inconsistent data and fragmented workflows, not the model itself. Inaccurate inputs → biased processes → hallucinated outputs. AI simply scales whatever foundation you give it. In transforming HR at TD SYNNEX, we spent lots of time on foundation first. 👉🏼 Clean, connected data. Simplified and standardized processes. Clear ownership. Trustworthy governance. Co-built with our teams, not pushed on them. I sleep better knowing we invested in this critical step. Get the fundamentals right, and AI becomes a force multiplier. Ignore them, and it becomes a risk multiplier. Not flashy. But absolutely essential to unlocking real enterprise value.

How to Build AI That Actually Delivers Results (Bad data = bad AI. It’s that simple.) AI isn’t a guessing game — it learns from patterns in your data. If that data is messy, outdated, or biased, your AI will be too. The difference between AI that works and AI that fails? A rock-solid data strategy. Here’s how to get it right: ↳ Collect high-quality data: AI is only as good as the information it’s trained on. ↳ Clean and organize it: Errors, duplicates, and inconsistencies lead to faulty predictions. ↳ Diversify your datasets: Avoid bias by including different perspectives and sources. ↳ Keep it fresh: AI needs real-time, relevant data to stay accurate. ↳ Secure it: Protect sensitive data and comply with privacy regulations. Most AI failures aren’t tech failures — they’re data failures. Fix your data, and your AI will follow. Is your business making data quality a priority? ______________________________ AI Consultant, Course Creator & Keynote Speaker Follow Ashley Gross for more about AI

Andrew Ng (founder of Deeplearning AI) “Garbage in, garbage out—if your data is bad, your AI won’t perform.” He highlights one of the biggest pitfalls of AI integration: Poor data management. AI is only as good as the data it’s fed. Incomplete, outdated, or messy data cripples performance and undermines results. It’s a mistake that can derail your entire integration. Clean, structured, and reliable data should always be the foundation. So, regularly audit your datasets. Eliminate inconsistencies. And make quality a non-negotiable standard. Without good data, even the most advanced AI will fail to deliver.