Data Quality for AI

According to Gartner, AI-ready data will be the biggest area for investment over the next 2-3 years. And if AI-ready data is number one, data quality and governance will always be number two. But why? For anyone following the game, enterprise-ready AI needs more than a flashy model to deliver business value. Your AI will only ever be as good as the first-party data you feed it, and reliability is the single most important characteristic of AI-ready data. Even in the most traditional pipelines, you need a strong governance process to maintain output integrity. But AI is a different beast entirely. Generative responses are still largely a black box for most teams. We know how it works, but not necessarily how an independent output is generated. When you can’t easily see how the sausage gets made, your data quality tooling and governance process matters a whole lot more, because generative garbage is still garbage. Sure, there are plenty of other factors to consider in the suitability of data for AI—fitness, variety, semantic meaning—but all that work is meaningless if the data isn’t trustworthy to begin with. Garbage in always means garbage out—and it doesn’t really matter how the garbage gets made. Your data will never be ready for AI without the right governance and quality practices to support it. If you want to prioritize AI-ready data, start there first.

Before You Obsess Over MCP or A2A… Fix Your Data Everyone’s talking about agent protocols—MCP, A2A, interoperability, orchestration layers… and yes, those are important. But don’t miss the most important part. It doesn’t matter how smoothly your agents talk to each other if they’re all speaking garbage. Protocols help agents communicate and/or interact with tools, but data is what they think with. An AI agent is only as good as the data it operates on. Feed it incomplete, outdated, or inconsistent data, and 𝐢𝐭 𝐰𝐢𝐥𝐥 𝐟𝐚𝐢𝐥—𝐟𝐚𝐬𝐭, 𝐚𝐧𝐝 𝐰𝐢𝐭𝐡 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞. Some common pitfalls? ▪️Agents disconnected from real-time operational data (hello, CRM silos). ▪️Structural errors and inconsistent formats. ▪️Snapshots of old data trying to guide dynamic decisions. And yet, most teams spend more time wiring up protocols than cleaning their inputs. Data quality isn’t a nice-to-have—it’s the foundation. Want to build smart agents? ✔️Standardize and clean your structured data. ✔️Integrate real-time sources. ✔️Create feedback loops to refine over time. ✔️Prioritize data engineering over just protocol engineering. In short: MCP might make your agents sound smart. Clean data makes them actually smart. Build agents that reason, not just talk. #ai #data #genai #agents #mcp

Many people, especially tech journalists and bloggers, don't understand the purpose of benchmarks in AI. Performing well on a benchmark doesn't prove anything but the fact that your AI works well on the types of problems this benchmark covers. If you work on a general-purpose AI, then it could perform well on one benchmark by chance. This is why we usually have multiple benchmarks, each focusing on a specific type of problem. Performing well on all benchmarks, made by different people testing different capabilities, is possible by chance, but this chance is very slim, close to 0.00000(...)0001. This is why we need many benchmarks. As a consequence, when our general-purpose AI performs well on multiple benchmarks made by different people to test different capabilities, we can safely extrapolate that it also performs well on other types of problems, not covered by benchmarks, because the chance that our choice of benchmarks and the range of problems our AI is capable of solving coincide exactly is very slim too. However! If you take a neural network and finetune it on the data from the benchmarks (or on a synthetic dataset that resembles the benchmark examples), then your model will perform well on all the benchmarks. But now we are not talking about performing well by chance; we are talking about performing well by design. And this defeats the whole purpose of the benchmarks. Now we cannot (and must not) extrapolate the performance of our neural network to other domains. This would be (and is) anti-scientific.

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?

NVIDIA surveyed 3,200 companies across five industries for its 2026 State of AI report. 88% said AI increased their annual revenue. 87% said it reduced costs. Those are big numbers. But then you read further. 48% of the same respondents said data problems are still their biggest challenge. The companies reporting strong returns got there by sorting out their data foundations before they started scaling models. Clean pipelines and data that's actually accessible to the teams building on it. The ones still stuck in pilots skipped that part and went straight to the exciting stuff. I've sat in rooms where the AI budget doubled year over year, but nobody could tell me where the training data was stored or who owned it. That's a priorities gap. The ROI numbers in this report are encouraging, but they belong to companies that treated data readiness as the first investment, not an afterthought to be addressed later. PepsiCo built physics-level digital twins of their manufacturing facilities with Siemens and NVIDIA. They achieved a 20% increase in throughput and a 10-15% reduction in capital expenditure. That didn't start with buying GPUs. It started with getting the data right. #EnterpriseAI #AIStrategy #DataGovernance #AIROL #DigitalTwins #Manufacturing #CIO #BusinessStrategy #AIAdoption #OperationalExcellence

𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗚𝘂𝗶𝗱𝗲: 𝘞𝘩𝘢𝘵 𝘞𝘰𝘳𝘬𝘴 𝘪𝘯 2025 Data isn’t just an asset—it’s a 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲. However, without the right architecture, it becomes a liability. Scalability issues, governance failures, and poor data quality cripple AI, analytics, and business agility. ❯ 𝗪𝗵𝗮𝘁 𝗗𝗲𝗳𝗶𝗻𝗲𝘀 𝗮 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗗𝗮𝘁𝗮 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺? ✓ 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 – It integrates operational databases, files, and IoT devices. ✓ 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 – Provides a structured landing zone with persistent storage. ✓ 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 – Blends the flexibility of a data lake with the discipline of a warehouse. ✓ 𝗢𝗽𝘁𝗶𝗺𝗶𝘀𝗲𝗱 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 – Enables fast, structured analytics with dedicated data marts. ✓ 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗦𝘁𝗼𝗿𝗲 𝗳𝗼𝗿 𝗔𝗜 – Guarantees consistency and reliability of ML model inputs. ✓ 𝗘𝘃𝗲𝗻𝘁 𝗕𝘂𝘀 & 𝗦𝘁𝗿𝗲𝗮𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 – Powers real-time analytics and automated decision-making. ✓ 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 – 𝗔𝗜 𝗶𝘀 𝗼𝗻𝗹𝘆 𝗮𝘀 𝗴𝗼𝗼𝗱 𝗮𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗳𝗲𝗲𝗱𝗶𝗻𝗴 𝗶𝘁. A strong data platform must enable scalable model training, ensure 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆, and support 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 without data drift. Without this, AI is just hype. ❯ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 & 𝗗𝗮𝘁𝗮 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆: 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗼𝗿𝘀 A modern data platform is not just about pipelines—it’s about 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆, 𝗰𝗼𝗻𝘁𝗿𝗼𝗹, 𝗮𝗻𝗱 𝘁𝗿𝘂𝘀𝘁. The best platforms include: ✓ 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 – 𝗧𝗿𝘂𝘀𝘁 𝗶𝗻 𝗔𝗜, 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆. Inaccurate, inconsistent, or biased data leads to bad models, poor insights, and costly mistakes. Quality isn’t optional—it’s the foundation. ✓ 𝗗𝗮𝘁𝗮 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 – Full traceability to track data flow, transformations, and accountability. ✓ 𝗗𝗮𝘁𝗮 𝗖𝗮𝘁𝗮𝗹𝗼𝗴 – A structured inventory that makes data easily discoverable. ✓ 𝗗𝗮𝘁𝗮 𝗠𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲 – Self-service access to curated, trusted datasets. ❯ 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲: 𝗧𝗵𝗲 𝗡𝗼𝗻-𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲𝘀 To ensure compliance and prevent breaches, a future-proof platform must have strict access control, enterprise-grade encryption, automated backup strategies, and real-time monitoring. ❯ 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Data-driven companies 𝘄𝗶𝗻 because they move faster, automate better, and predict outcomes with precision. Those who neglect 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱, 𝗮𝗻𝗱 𝗔𝗜-𝗿𝗲𝗮𝗱𝘆 𝗱𝗮𝘁𝗮 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 will struggle to compete. 𝗜𝘀 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝘄𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁?

The next AI bottleneck is not model intelligence. It is data readiness. We already know models can reason, write, search, summarize, and call tools. The harder problem now is what happens when those models are connected to enterprise data. Because in production, AI does not fail only because the model is weak. It fails when the data is: stale duplicated incomplete poorly governed spread across too many systems missing context not trusted by the business And once AI starts taking action, bad data does not just create bad dashboards. It creates bad decisions at machine speed. That is why the conversation around enterprise AI is shifting from “Which model should we use?” to: “Is our data ready for AI to act on it?” Informatica World 2026 on Salesforce+ has some useful on-demand sessions around AI-ready data, trusted data foundations, governance, and the future of enterprise data management. For architects, data leaders, and AI builders, this is the layer that matters most. Because agentic AI will not scale on top of messy data. It will scale on top of trusted data. Watch it here: https://lnkd.in/dCDDMFuX

One of the hardest parts of fine-tuning models? Getting high-quality data without breaching compliance. This Synthetic Data Generator Pipeline ia built to solve exactly that, and it is open-sources for you to use! You can now generate task-specific, high-quality synthetic datasets without using a single piece of real data, and still fine-tune performant models. Here’s what makes it different: → LLM-driven config generation Start with a simple prompt describing your task. The pipeline auto-generates YAMLs with structured I/O schemas, filters for diversity, and LLM-based evaluation criteria. → Streaming synthetic data generation The system emits JSON-formatted examples, prompt, response, metadata at scale. Each example includes row-level quality scores. You get transparency at both data and job level. → SFT + RFT with evaluator feedback We use models like DeepSeek R1 as judges. Low-quality clusters are automatically identified and regenerated. Each iteration teaches the model what “good” looks like. → Closed-loop optimization The pipeline fine-tunes itself, adjusting decoding params, enriching prompt structures, or expanding label schemas based on what’s missing. → Zero reliance on sensitive data No PII. No customer data. This is purpose-built for enterprise, healthcare, finance, and anyone who’s building responsibly. And it works: 📊 On an internal benchmark: - SFT with real, curated data: 79% accuracy - RFT with synthetic-only data: 73% accuracy That’s huge, especially when your hands are tied on data access. If you’re building copilots, vertical agents, or domain-specific models and want to skip the data wrangling phase, this is for you. Built by Fireworks AI 🔗 Try it out: https://lnkd.in/dXXDdyuM

We built an AI benchmark that measures real work. Today we're releasing it to everyone. AI evals tell you whether a model can do complex reasoning or generate code. Useful, but usually not the question our customers ask. They want to know: can this model find the right CRM record, send the right follow-up, and not break anything along the way? We went looking for a benchmark that tested that. Nobody had built one, so we did. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻𝗕𝗲𝗻𝗰𝗵 drops AI models into realistic business environments across six domains (Sales, Marketing, Ops, Support, Finance, HR) and checks whether the work actually got done. The tasks include live CRM data, inbox threads with ambiguous context, and multi-step tool chains where one wrong call cascades. Scoring is deterministic: either the right records were updated and the right messages were sent, or they weren't. Zapier processes over 2B tasks a month across 3.7M businesses…we see automation workloads at a scale no one else does. AutomationBench was built on those patterns so we could decide which models to roll out across Zapier. It’s useful enough that we're releasing it publicly today. Open task set, open methodology, open leaderboard. Everyone should have access to this. No model has cracked 10%. Yet. Try it here: https://lnkd.in/gNaXsuDj

Stanford researchers recently surfaced something called “Semantic Collapse.” The idea: many RAG systems start to break down as the knowledge base grows toward enterprise scale. Right now, if you're an enterprise trying to figure out whether an AI system will actually work on your data, you basically have three options: - spend 4 weeks on a PoC - commit an internal team to build and benchmark it yourself - spend a lot of time figuring out whether vendor demos reflect your real data environment None of these are great. This is why Onyx's new benchmark caught my attention (GitHub link below). It's called 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞𝐑𝐀𝐆-𝐁𝐞𝐧𝐜𝐡, and it's built specifically for information retrieval over enterprise-style internal knowledge. Other benchmarks often use clean wiki articles or neat datasets with obvious answers. EnterpriseRAG-Bench contains 500k+ documents that actually look like the mess inside a real company: → hundreds of thousands of Slack messages with key decisions buried in threads  → Drive and Confluence docs that are years out of date  → contradictory “authoritative” sources  → huge variation in volume, formatting, and quality In other words, the actual reality of unstructured enterprise data. The benchmark uses 500 QA pairs to test: • the retrieval mechanisms inside an AI platform  • the model's ability to reconcile messy, conflicting information  • whether the end result is actually useful and actionable for a team I took a look at the repo, and what stood out is that it's more than just a benchmark. 𝐓𝐡𝐞𝐲 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞𝐝 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐝𝐚𝐭𝐚𝐬𝐞𝐭. So if you're building agents for knowledge work, you can now: → test your system against enterprise-style data at real scale  → tune and experiment with your retrieval pipeline  → optimize for real business contexts instead of toy examples For anyone evaluating AI coworking systems, or building them, this is the kind of resource the space has been missing. 📍GitHub link: https://lnkd.in/gS8mwWB2 As models keep getting better, the bottleneck increasingly isn't the model. It's whether your retrieval layer can survive contact with real company data.