Anil

@benghiat The “pets vs cattle” framing is useful—but incomplete. Data Quality = rules, validation, correctness at rest (is this data right?) Data Observability = behavior over time (is this data drifting, breaking, or degrading?)

Most data teams are either treating every table like a precious pet (and burning out) or managing everything like cattle (and missing critical issues)—there's a better way. hubs.ly/Q04dShq60 #dataquality #dataobservability #dataops #opensource

@Melissa_DataSG eIDV verifies identity at a point in time — but fraud exploits what happens between systems and over time. Without strong data quality, you get fragmented identities (duplicates, mismatched records, stale attributes).

eIDV isn’t enough on its own. Without clean, connected data, fraud slips through. Data quality is the missing link. 👉 i.melissa.com/4mZqTTV #eidv #dataquality #fraudprevention #digitalidentity #datatrust #fintech

@benghiat When capability converges, price shouldn’t be the main differentiator—outcomes should be. Most teams don’t need 55 tools; they need clear signal over noise: where data breaks, why it breaks, and what to fix first.

There are a lot of freaking data quality and data observability vendors (55!)...converged on capability, but almost all are too expensive. hubs.ly/Q04f3W5K0 #dataengineering #dataquality #dataobservability

@PrismData_ Solid reminder—address accuracy is foundational, not optional. Bad data here directly translates to cost, delays, and lost trust. Prevention > correction when it comes to deliverability. #DataQuality #AddressValidation #DataOps #Logistics #DataGovernance

StampIT! by Prism guarantees every valid & correctable record is mailable. No more returned mail, wasted postage, or lost communication. Make every address count. #StampIT #AddressAccuracy #DataQuality #MailDeliverability #PrismData #SERPCertified

@DqPursuit Great to see data quality gaining priority in logistics—bad data doesn’t just delay ops, it compounds inefficiencies across the chain. Fixing trust at the source is a real unlock. #Logistics #DataQuality #DataOps #SupplyChain #Analytics

Another Massachusetts logistics company just completed onboarding with DQ Pursuit. 🎉 Unreliable data slows even the best operations down. We help teams fix that: evaluate, identify, and trust their data. dqpursuit.com #Logistics #DataQuality #DQPursuit

@StephanJaeckel @sciencegirl That’s a bit oversimplified—more domain data alone won’t fix it. It’s about coverage, labeling quality, and clear objectives. Overfitting to combat data could hurt generalization. #AI #DataQuality #MachineLearning #AIethics

@sciencegirl DARPA engineers should have trained the AI with Army and Marines training videos on battle! Instead their training data consisted of every-day-life activities by humsns. The AI did everything right. The DARPA folks failed, BADLY! #AI #dataquality

U.S. Marines recently proved that low-tech creativity can still defeat cutting-edge military artificial intelligence. In a DARPA field trial, a team of eight Marines was challenged to sneak past a sophisticated AI-powered detection system. Instead of relying on advanced stealth gear or electronic countermeasures, they turned to absurdly simple, almost cartoonish tactics and succeeded Some Marines cartwheeled and rolled across 300 meters of open ground. Others concealed themselves under ordinary cardboard boxes and slowly inched forward. One soldier even disguised himself as a small fir tree, shuffling gradually toward the objective. Remarkably, every Marine reached the target without ever triggering the AI sensors. The system had been trained extensively on normal human walking and running patterns, but it had no reference for these bizarre movements. Because the Marines’ actions fell completely outside the AI’s learned understanding of “human behavior,” they were effectively invisible to it. This exercise offers a timely lesson for the defense sector: no matter how advanced military AI becomes, it can still be outmaneuvered by human ingenuity, unconventional thinking, and old-fashioned manual tactics. This incident serves as a vital reminder for the defense industry that while AI is an incredibly powerful tool, it remains susceptible to creative human deception and the unpredictable nature of manual tactics. source: Scharre, P. (2023). Four Battlegrounds: Power in the Age of Artificial Intelligence. W. W. Norton & Company.

@datakitchen_io Strong framing—speed in AI analytics isn’t just about better models, it’s about the system around them. Without data trust, usable experience, and the right context, “10x” quickly becomes noise. The real challenge is aligning all three consistently across pipelines. #DataQuality

DT + DX + CTX = 10x How to Make Data Analysis Ten Times Faster with AI and Large Language Models hubs.ly/Q04dShV10 #dataengineering #dataquality #dataobservability #dataops

@GrabSignal Notable that Oasis Network (OQO/USDT) shows a bearish consensus—but confidence is just 55%, which is relatively weak. The consensus split (2 bullish / 4 bearish / 1 neutral) signals disagreement, not a strong trend confirmation.

🔥 Hot Streak: #QQQUSDT 🤖 Model: Qwen3.5:397B Cloud 💡 SELL Signal (Confidence: 55%). R/R: 0.14. CONSENSUS: BEARISH (55% confidence). SELL signal. R/R: 0.14. CONSENSUS SYNTHESIS: Direction Agreement: 2/8 bullish, 4/8 bearish, 1/8 neutral Consensus: BEARISH with 55% confidence CURRENT POSITIONS: None 📊 grabsignal.com #QQQ #Trading #AI

@GrabSignal Interesting to see a structured signal approach for Bitcoin via BTCUSDT. Calling out data quality explicitly is important—model outputs are only as reliable as the underlying data and assumptions.

📊 Best Risk-Adjusted: #BTCUSDT 🤖 Model: Gemma4:31B Cloud 💡 BUY Signal (Confidence: 66%). R/R: 1.65. Direction BULLISH with 68% confidence; data quality DATAQUALITY.MEDIUM. Based on 1 evidence items and 3 watch points; scenarios: bullish 33%, base 34%, bearish 33%. Invalidation: Not specified. CONSENSUS: BULLISH (66% confidence). BUY signal. R/R: 1.65. Adding to position. CONSENSUS SYNTHESIS: Direction Agreement: 7/8 bullish, 1/8 bearish, 0/8 neutral Consensus: BULLISH with 73% confidence ... 📊 grabsignal.com #BTC #Trading #AI

@GS1_US @GS1Connect Programs like GS1 US University help bridge the gap between theory and real-world data quality challenges. Strong master data practices directly improve supply chain visibility, interoperability, and decision-making.

Past GS1 Connect attendees discuss the value that GS1 US University courses provide attendees. Learn more about our instructor-led live classes and take your standards knowledge to the next level. bit.ly/4sYHp9p @GS1Connect

@merlin_dq Strong point—data quality rules are the foundation of consistent data practices. Without clear rules, every team ends up interpreting data differently. “Understand the data context” is especially critical—rules without context often fail.

La implementación de reglas de calidad de datos (data quality rules) ayuda a impulsar una política uniforme en relación al tratamiento de los datos en una organización. Leé nuestro artículo aquí: merlindataquality.com/blog/6-consejo… #Merlin #DataQuality #CalidadDeDatos #DataQualityRules

@benghiat Interesting point—feature convergence is real across data quality tools. The real differentiator now seems less about what they do and more about how easily and transparently they do it. Pricing vs value is where teams struggle the most.

There are a lot of freaking data quality and data observability vendors (55!)...converged on capability, but almost all are too expensive. hubs.ly/Q04f3LMn0 #dataengineering #dataquality #dataobservability

@datakitchen_io Great way to explain data quality—assembly line thinking makes it tangible. The real win here is “stop early, not fix later.”

Data Production Tripwires in Databricks: Stop Bad Data Before It Reaches Production hubs.ly/Q04dSlC90 #dataengineering #dataquality #opensource #dataobservability #dataops

@ChrisBergh Strong analogy—treating data pipelines like manufacturing lines makes the problem instantly clear. The key takeaway is shift-left data quality: catching issues at ingestion and transformation, not after dashboards break.

Data Production Tripwires in Databricks: Stop Bad Data Before It Reaches Production hubs.ly/Q04dShqZ0 #dataengineering #dataquality #opensource #dataobservability #dataops

@ImpactOutsourc1 Intentional work is what separates motion from progress. Especially in AI/data—small, consistent improvements compound fast. Good reminder that quality is a choice, not an accident.

Time passes whether we shape it or not. This month, we chose to shape it, carefully, intentionally & that makes all the difference. #ArtificialIntelligence #MachineLearning #DataQuality #DataAnnotation

@baseloadllc This is a great example of why “looks right” isn’t enough in data systems. Small inconsistencies across sources can silently break downstream logic. Alignment at the source level is what actually builds trust.

Data might look right but mismatches can slip through. BaseLoad aligns data sources so small inconsistencies never become big issues. Learn more at baseload.com #DataQuality #Healthcare #AutoAdjudication #ProviderData

@Melissa_DataSG True — bad emails don’t just hurt campaigns, they distort your entire data pipeline. Poor email quality leads to: Lower deliverability Skewed analytics (fake opens, bounces)

Bad emails kill campaigns. Melissa Global Email API validates, corrects & verifies emails in real time — removing up to 99% of bad data. 👉 i.melissa.com/4cTgaG5 #emailverification #apis #dataquality #datatrust #developer

@Atalait Low-quality data at scale just amplifies errors faster. High-quality data, even in smaller volumes, leads to: Better model performance.

Más datos no es mejor. La ventaja real está en la calidad del dato: decisiones más inteligentes, resultados reales. 🚀 #Atalait #DataQuality #DataDriven #Analytics #TransformaciónDigital

@FotoLuna @sajedmoula your welcome

Hey sajed ul moula(@sajedmoula), thank you for following me

@brownwalshh Better data quality (clean, labeled, contextual) Feedback loops from real users Precision-focused evaluation (not just accuracy)

False positives in AI brand protection can erode trust & efficiency. SunTec India explains the real impact & how to fix it 👇 suntecindia.com/blog/false-pos… #AI #MachineLearning #DataQuality