How Data Integrity Affects AI Performance

When agents start returning wrong answers, the instinct is to blame the model. Swap the LLM. Rewrite the prompts. File a ticket with the AI team. Almost always the wrong call. Here's a real example: a customer-facing support agent starts confidently giving users bad information after a product update. The AI team investigates for days. Model hasn't changed. Prompts haven't changed. What changed: a schema migration renamed three fields in the underlying product database. The pipeline feeding the agent's knowledge base was never updated. Half the retrieved context was arriving with null values. The agent filled the gap with plausible-sounding information. A data problem. Diagnosed as an AI problem. Days lost. This isn't a skills gap. It's structural. Data teams monitor pipelines for analytics SLAs. AI engineers watch model performance and latency. The space in between — where a data issue quietly degrades agent performance — belongs to no one. We surveyed 260 engineers building agents in production. Almost half have already discovered an agent accessing data it shouldn't. 54% expect to significantly rearchitect systems they've already shipped. The agents are doing their jobs. The systems around them aren't built for this. Data and AI aren't two separate problems. They never were. Link in comments 👇 #dataquality #aiobservability #agenticai

𝐃𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐢𝐬𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐈—𝐢𝐭'𝐬 𝐚𝐛𝐬𝐨𝐥𝐮𝐭𝐞𝐥𝐲 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥. AI solutions, particularly those embedded in ERP systems, are designed to deliver valuable insights and recommendations to businesses. However, the 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝐭𝐡𝐞𝐬𝐞 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐚𝐫𝐞 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲 𝐥𝐢𝐧𝐤𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐮𝐧𝐝𝐞𝐫𝐥𝐲𝐢𝐧𝐠 𝐝𝐚𝐭𝐚. In traditional ERP implementations, businesses often found themselves achieving systems that were "on time, on budget, fully functional, and disappointing." Why? Because while the system technically worked, the data feeding it wasn't accurate enough to meet real-world expectations. Incorrect customer addresses, inaccurate inventory data, or faulty financial figures significantly compromised the value of the entire system. 𝐖𝐢𝐭𝐡 𝐀𝐈, 𝐭𝐡𝐞 𝐬𝐭𝐚𝐤𝐞𝐬 𝐚𝐫𝐞 𝐞𝐯𝐞𝐧 𝐡𝐢𝐠𝐡𝐞𝐫. AI-driven recommendations depend heavily on the accuracy and quality of data. If AI bases its recommendations on inaccurate or inconsistent data, users quickly lose trust and confidence in these insights, eventually ignoring them entirely. This lack of trust diminishes the value of AI systems, no matter how sophisticated the algorithms are. 𝐓𝐡𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐧𝐨𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 "𝐀𝐈 𝐢𝐬 𝐠𝐨𝐨𝐝 𝐚𝐭 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐛𝐚𝐝 𝐝𝐚𝐭𝐚" 𝐢𝐬 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐥𝐲 𝐟𝐥𝐚𝐰𝐞𝐝. While AI may process large volumes of data quickly, poor-quality input inevitably leads to poor-quality outcomes. 𝐀𝐈 𝐚𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐬 𝐛𝐨𝐭𝐡 𝐭𝐡𝐞 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬 𝐚𝐧𝐝 𝐰𝐞𝐚𝐤𝐧𝐞𝐬𝐬𝐞𝐬 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚—meaning bad data can severely degrade your results and decision-making quality. One of the longstanding strengths of SAP systems is their reliability and trustworthiness. Businesses have confidence in SAP solutions because they know the integrity of their data is preserved and accurately managed throughout the process. This reliability is especially critical in the age of AI, where the value derived is directly proportional to the quality of data provided. 𝐒𝐢𝐦𝐩𝐥𝐲 𝐩𝐮𝐭: 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐝𝐚𝐭𝐚 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐨𝐟 𝐬𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐈. 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐭, 𝐞𝐯𝐞𝐧 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐚𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐈 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐰𝐨𝐧'𝐭 𝐝𝐞𝐥𝐢𝐯𝐞𝐫 𝐭𝐡𝐞 𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐯𝐚𝐥𝐮𝐞.

If you’re not collecting the right data, AI will only accelerate your mistakes. Because AI doesn’t make you smarter. It just scales whatever assumptions you feed it good or bad. Everyone loves to talk about model performance, neural networks, and GenAI breakthroughs. But in the real world in industries like transport, logistics, healthcare, and public safety AI is only as powerful as the data foundation it sits on. If your data is noisy, biased, or incomplete your AI will be too. And when decisions are automated, those mistakes happen at speed and scale. As per Global context: – $100B+ lost annually due to bad data across industries (IBM) – 85% of AI projects fail to move beyond proof-of-concept (Gartner, 2023) – 96% of companies face data quality issues that impact AI performance (MIT Sloan) – In safety-critical domains, a false prediction from bad data can mean human lives at risk, not just business inefficiency While in Saudi Arabia: – As Vision 2030 accelerates AI adoption across sectors, from SDAIA to smart city infrastructure - clean, localized, context-aware data is becoming the bottleneck – Most imported datasets don’t reflect the unique driving patterns, heat, culture, or behavioral nuances of KSA’s real-world environments – Without investment in local data ops, many AI tools here will remain impressive in demo - and risky in deployment So, what can be done? – Map the right problem first. Don’t collect “big data,” collect the right data. – Label for local context. Your AI can’t make sense of behavior it wasn’t trained to see. – Build feedback loops. Your AI should learn and evolve from real-world conditions. – Govern your data like its product. Because it is. Garbage in, garbage out still applies. AI isn’t dangerous because it’s too powerful. It’s dangerous when it’s trusted too early on the wrong data. If you're not disciplined about your data today, your AI won’t be intelligent tomorrow. 📌 Do you use AI? If yes, for which purpose. ♻️ Repost to share insights with your network.

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

10 of the most-cited datasets contain a substantial number of errors. And yes, that includes datasets like ImageNet, MNIST, CIFAR-10, and QuickDraw which have become the definitive test sets for computer vision models. Some context: A few years ago, 3 MIT graduate students published a study that found that ImageNet had a 5.8% error rate in its labels. QuickDraw had an even higher error rate: 10.1%. Why should we care? 1. We have an inflated sense of the performance of AI models that are testing against these datasets. Even if models achieve high performance on those test sets, there’s a limit to how much those test sets reflect what really matters: performance in real-world situations. 2. AI models trained using these datasets are starting off on the wrong foot. Models are only as good as the data they learn from, and if they’re consistently trained on incorrectly labeled information, then systematic errors can be introduced. 3. Through a combination of 1 and 2, trust in these AI models is vulnerable to being eroded. Stakeholders expect AI systems to perform accurately and dependably. But when the underlying data is flawed and these expectations aren’t met, we start to see a growing mistrust in AI. So, what can we learn from this? If 10 of the most cited datasets contain so many errors, we should assume the same of our own data unless proven otherwise. We need to get serious about fixing — and building trust in — our data, starting with improving our data hygiene. That might mean implementing rigorous validation protocols, standardizing data collection procedures, continuously monitoring for data integrity, or a combination of tactics (depending on your organization’s needs). But if we get it right, we're not just improving our data; we're setting our future AI models to be dependable and accurate. #dataengineering #dataquality #datahygiene #generativeai #ai

You can’t automate what you don’t understand. And #AI can’t optimize what it can’t trust. A lot of organizations are chasing AI in IT operations… But here’s the unspoken truth: AI isn’t failing you. Your data is. Most IT tickets are filled with: - Vague or missing short descriptions - Empty detailed descriptions - Copy-paste resolution notes - Blank or outdated implementation, testing, or blackout plans And yet we expect AI to: - Predict incident resolution - Recommend similar tickets - Cluster top issues - Detect anomalies - Auto-route and auto-resolve It’s like asking a GPS to navigate with broken satellites and incomplete maps. AI learns from your historical data but what if your past is noisy, incomplete, or misleading? Here’s the deal: The quality of your AI is only as good as the quality of your foundational data. That includes: - Description and short descriptions - CI ownership and relationships - Support & approval groups and categories - Resolutions - Implementation plan, backout plan - Accurate historical ticket data etc. Before you buy another AI tool, ask: “Is our data ready for intelligence?” Clean data isn’t a checkbox. It’s the fuel for AI precision, performance, and trust. Want real ROI from AI in IT Operations? Start with a data integrity audit, not a chatbot. #ITSM #AIOps #Data #CMDB #IncidentManagement #Automation #AgenticAI #ServiceNow #DigitalTransformation #ITLeadership

Can AI models get "Brain Rot"? New research says, Yes! A recent paper on the 'LLM Brain Rot Hypothesis' presents findings that are crucial for anyone involved in AI development. Researchers have discovered that continuous exposure to low-quality web content leads to lasting cognitive decline in large language models (LLMs). The key impacts identified include:- - 17-24% drop in reasoning tasks (ARC-Challenge). - 32% decline in long-context understanding (RULER). - Increased safety risks. - Emergence of negative personality traits (psychopathy, narcissism). What defines "junk data"? Two dimensions are significant:- - Engagement-driven content (short, viral posts). - Low semantic quality (clickbait, conspiracy theories, superficial content). The most concerning finding is that the damage is persistent. Even scaling up instruction tuning and clean data training cannot fully restore baseline capabilities, indicating deep representational drift rather than mere surface-level formatting issues. This research highlights that as we develop autonomous AI systems, data quality transcends being a mere training concern; it becomes a safety issue. We need to implement:- - Routine "cognitive health checks" for deployed models. - Careful curation during continual learning. - A better understanding of how data quality affects agent reliability. The paper emphasizes that data curation for continual pretraining is a training-time safety problem, not just a performance optimization. For those building production AI systems, this research should fundamentally alter our approach to data pipelines and model maintenance. Link to paper: https://lnkd.in/drgjvt8a #AI #MachineLearning #AgenticAI #DataQuality #AIResearch #LLM #AIEthics

Many companies believe that implementing Artificial Intelligence is the final step to generating value. But they forget that without data governance, AI results lose credibility. Some common symptoms of weak governance include: • Duplicate customer records across different systems. • Critical fields left blank or marked as optional. • Outdated records that linger for months without review. • Conflicting definitions of what counts as “active” or “inactive.” • Parallel spreadsheets becoming the “real source” of information. What happens then? AI suggests misleading paths, misclassifies data, inflates numbers, and explains KPIs that don’t reconcile. Leaders lose confidence, and the technology that should bring clarity only creates more noise. The bottom line: without data governance, there is no trustworthy AI. Governance is not bureaucracy, it is what ensures quality, consistency, and trust so that AI can truly deliver value. Is data governance ready for the age of AI?

AI agents are only as good as the data behind them. That’s why I talked to Cam Ogden, SVP of Product Management at Precisely, to learn more about the challenge many organizations are underestimating: whether their data is actually Agentic-Ready Data. As more companies move from AI experimentation to enterprise agents making decisions on their behalf, the bar for data trust rises dramatically. Cam shares why “AI-ready” and “agentic-ready” are not the same thing, and what leaders need to do now to close that gap. Here’s what you’ll learn: 1. Why data trust matters more in the age of AI agents When dashboards fail, teams lose confidence. When agents act on flawed data, the business takes on real operational risk. Agentic AI requires a much higher standard of trust, completeness, and confidence in enterprise data. 2. Where the AI readiness disconnect really comes from According to the 2026 State of Data Integrity and AI Readiness report (https://lnkd.in/dMm72RbM), co-developed with the Drexel LeBow Center for Applied AI and Business Analytics, many leaders say their organizations are AI ready because they have roadmaps, cloud investments, and executive support in place. But at the operational level, the picture often looks very different, creating a gap between strategy and execution. That’s also why so many organizations are still struggling with data quality, context, and integration. 3. The hidden measurement problem in AI adoption A major issue is not just whether companies are investing in AI, but whether they are measuring success in a meaningful way. Too few organizations have clear metrics tied to AI outcomes, which makes it harder to prove impact and scale what works. 4. How to close the agentic AI data integrity gap Leaders need to address six core challenges, spanning siloed data, missing context, stale data, incomplete records, weak governance, and rising cost from untangling these issues. Start with one use case, strengthen the data foundation, prove value, and then scale across the business. If you’re exploring AI agents in the enterprise, this conversation will help you understand why trusted, well-governed data is becoming a competitive requirement. Watch and listen how leaders can build the Agentic-Ready Data foundation needed for real AI scale. #ArtificialIntelligence #DataStrategy #AgenticAI #AgenticReadyData #Precisely

Everyone’s talking about AI models, but here’s the truth most overlook: Your AI is only as smart as your data. As the founder of DataGardener, I’ve seen AI transform how #businesses operate—but I’ve also seen promising models fall flat because the data wasn’t good enough. Why Data is the Real Power Behind AI Algorithms don’t work magic. They learn patterns from data. So if your data is: ✔️ Outdated ✔️ Incomplete ✔️ Inaccurate …you’ll get flawed predictions and risky decisions. No matter how advanced the model. #AI learns from patterns. The more diverse and representative your #dataset, the better your models can generalise to real-world scenarios. Two Things Every Business Needs: 1. Accuracy "Garbage in, garbage out" is real. Clean, correct data is the only way to get trustworthy insights. Insufficient data doesn’t just mean bad business—it can lead to bias, compliance risks, and lost revenue. 2. Data Volume More data = better pattern recognition. Large datasets make models more robust, less prone to overfitting. #Diversity in data ensures insights reflect reality—not just a narrow view. How Key Data Attributes Impact AI Quality: #Accuracy → Produces trustworthy, actionable results #Volume → Enables richer insights and model resilience Real-World Impact Real-World Impact At DataGardener, our clients use AI built on verified, comprehensive company data. That’s how they: Make smarter credit decisions Uncover leads others miss Mitigate risks before they become costly The difference? It’s the data. Takeaway for Business Leaders Treat your data like an asset—not a byproduct: invest in data collection, cleaning, and validation. Before chasing the next AI model, fix your foundation. Remember: AI is only as good as the data it learns from. In the age of AI, data stewardship isn’t just IT’s job—it’s a boardroom priority. Curious how high-quality data can power better AI decisions in your business? Let’s talk. Let’s build smarter—starting with the right data. #SmartData #AIDrivenDecision #Data #BusinessLeader #ComplianceRisks #CreditDecisions #AIDecisions