🗽 Heading to New York! iceDQ will be at the QA Financial & E-commerce Forum on May 12, 2026, at the Harvard Club NYC. Attending? Let’s connect—drop a comment, DM us, or meet us there! 👇 qafinancial.zohobackstage.eu… #QAFinancialForum #iceDQ #DataQuality #DataTesting #NewYorkCity

🧪 Quality Engineer @ CGI! 🌆 Bangalore/Chennai 🎓 CS | 0-3 YOE 🔥 SQL PySpark 💰 INR 7–14 LPA Big Data QA! 📊 #CGI #QAJobs #BangaloreJobs #DataTesting #Jobformore Apply: tr.ee/GVyHgi

Data issues rarely announce themselves. They slip into dashboards. They distort reports. They quietly influence the decisions that move your business forward. By the time someone notices, the impact is already real. Today’s enterprises don’t just manage massive volumes of data. They manage complex pipelines, layered transformations, and evolving business rules. And when visibility is limited, trust erodes fast. That’s where Qyrus Data Testing comes in. It brings AI-powered, no-code validation across your entire data lifecycle so you can detect issues early, validate logic confidently, and scale testing without adding complexity. With Qyrus Data Testing, teams can: • Validate data from source to target • Test transformation logic with precision • Verify business rules automatically • Support compliance and audit readiness • Execute up to 60% faster with significantly lower manual effort The result? Cleaner pipelines. Faster releases. Stronger decision-making. Because reliable data is not just a technical requirement. It is the foundation of digital trust. Explore how modern enterprises are transforming data quality: qyrus.com/solutions/data-tes… #DataTesting #DataQuality #EnterpriseData #AITesting #DigitalTrust #Qyrus

Data quality isn’t breaking because teams lack dashboards. It’s breaking because data is moving faster than static testing can keep up. By 2026, most enterprise data won’t even touch a centralized warehouse. It will be created, processed, and acted on at the network edge. APIs. Microservices. Real-time decisions. That’s where traditional ETL validators start to show their limits. In our latest blog, we break down Qyrus 𝐃𝐚𝐭𝐚 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐯𝐬. 𝐃𝐚𝐭𝐚𝐠𝐚𝐩𝐬 𝐄𝐓𝐋 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐨𝐫 and the difference is simple: Datagaps helps you see your data once it’s in motion. Qyrus helps you trust it before it even gets there. Datagaps shines in large-scale ETL audits and Informatica-heavy cloud migrations. Qyrus takes a 𝐮𝐧𝐢𝐟𝐢𝐞𝐝 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡, using 𝐀𝐈 𝐭𝐨 𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐝𝐚𝐭𝐚 𝐚𝐭 𝐭𝐡𝐞 𝐬𝐨𝐮𝐫𝐜𝐞 and 𝐜𝐨𝐧𝐧𝐞𝐜𝐭 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 across 𝐖𝐞𝐛, 𝐌𝐨𝐛𝐢𝐥𝐞, 𝐀𝐏𝐈, 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 𝐢𝐧 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐓𝐞𝐬𝐭𝐎𝐒. Because the real question today isn’t “𝑫𝒊𝒅 𝒕𝒉𝒆 𝒅𝒂𝒕𝒂 𝒎𝒐𝒗𝒆 𝒄𝒐𝒓𝒓𝒆𝒄𝒕𝒍𝒚?” It’s “𝑪𝒂𝒏 𝒘𝒆 𝒕𝒓𝒖𝒔𝒕 𝒕𝒉𝒆 𝒊𝒏𝒕𝒆𝒍𝒍𝒊𝒈𝒆𝒏𝒄𝒆 𝒅𝒓𝒊𝒗𝒊𝒏𝒈 𝒆𝒗𝒆𝒓𝒚 𝒅𝒆𝒄𝒊𝒔𝒊𝒐𝒏?” If you’re evaluating visual ETL validation versus AI-driven, shift-left data quality, this comparison will help you choose the right path. 👉 Read the full blog here: qyrus.com/qyrus-data-testing… #DataTesting #DataQuality #ETL #AIinTesting #ShiftLeft #DataOps #Qyrus

Data quality has changed. But most strategies have not. It is 2026, and nearly 75 percent of enterprise data is now created and processed at the edge. Data is born in APIs, devices, and transformation layers, not in warehouses. Decisions are made in milliseconds, often before data ever reaches production systems. Yet many teams are still optimizing for detection, not prevention. Traditional data quality tools focus on auditing what is already in production. They are excellent at scanning billions of records and flagging issues after the data has landed. That model still has a place. But in a world of zettabytes and real-time decisions, it is no longer enough. Modern data quality has to move left. Qyrus 𝐃𝐚𝐭𝐚 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐢𝐬 𝐛𝐮𝐢𝐥𝐭 𝐟𝐨𝐫 𝐭𝐡𝐢𝐬 𝐫𝐞𝐚𝐥𝐢𝐭𝐲. Instead of reacting downstream, it uses Generative AI to create test cases during development. Logic flaws are caught at the source, before dirty data enters pipelines, before latency amplifies risk, and before bad data drives bad outcomes. Qyrus 𝐚𝐥𝐬𝐨 𝐫𝐞𝐟𝐥𝐞𝐜𝐭𝐬 𝐡𝐨𝐰 𝐦𝐨𝐝𝐞𝐫𝐧 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐞 𝐛𝐮𝐢𝐥𝐭. Data starts at the API and edge layers, not the warehouse. With a unified TestOS, teams can validate web, mobile, API, and data workflows in one platform, without slowing delivery or adding more tools. 𝐈𝐧 2026, 𝐭𝐡𝐞 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐢𝐬 𝐧𝐨𝐭 𝐰𝐡𝐞𝐭𝐡𝐞𝐫 𝐲𝐨𝐮 𝐦𝐨𝐧𝐢𝐭𝐨𝐫 𝐝𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲. 𝐈𝐭 𝐢𝐬 𝐰𝐡𝐞𝐫𝐞 𝐲𝐨𝐮 𝐝𝐫𝐚𝐰 𝐲𝐨𝐮𝐫 𝐥𝐢𝐧𝐞 𝐨𝐟 𝐝𝐞𝐟𝐞𝐧𝐬𝐞. 𝐀𝐭 𝐭𝐡𝐞 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞, 𝐨𝐫 𝐚𝐭 𝐭𝐡𝐞 𝐬𝐨𝐮𝐫𝐜𝐞? Read the full breakdown of Qyrus Data Testing vs iCEDQ here 👉qyrus.com/post/qyrus-data-te… #DataQuality #ShiftLeft #DataTesting #AIinTesting #EnterpriseData #Qyrus

"Manual testing is like catching rain ☔ with a colander. You might catch something, but most of it slips through.” #Oracle teams, without end-to-end #datatesting, you're risking your #Oracle environment and falling behind on #AI. 🤩Here's how to fix it: bit.ly/4n9wyXe

🔎Vietnamese word but fit every language 👀 #Vaix #Vai #Vi #Word #PiX #PiXityBot #AIProject #DataTesting #ProductDevelopment #PiXity #AITravel #DaNang #TechCommunity #TravelTech #StartupVietnam

😎 Eat like a local, not like a lost tourist ... And yes, crypto enthusiast, we didn’t forget you 👀. See you soon 😉 #PiX #PiXityBot #AIProject #DataTesting #HumanTouch #ProductDevelopment #PiXity #AITravel #CryptoOnTheGo #DaNang #TechCommunity #TravelTech #StartupVietnam

“ETL Testing with Azure"-Demo Video youtu.be/WANfaKLhxuI Enrolments are still in progress Regular sessions:23rd April @ 8 PM (IST) The course Price: 6,900 INR / 99 USD pls call/WhatsApp @ 91-9133190573/8977922427 #ETLTesting #AzureData #AzureETL #DataTesting #ETLPipelines

The webinar kicks off in a few minutes! Discover how #GenerativeAI can automate your #datatesting and simplify #ETL and #BIprojects. Tune in for actionable tips and real-world demos. 👉testguild.com/webinar-automa… #DatagapsDataOpsSuite #GenAI #AgenticAI @datagaps

Super Dica: Pandera - Framework Open-source Para Testes de Dados com Alta Precisão em Linguagem Python pandera.readthedocs.io/en/st… #pandera #datatesting #python #datascienceacademy

When you are working on a product or a feature, you cannot push it to all the customers/users at once. You need to test its performance first. 𝗕𝘂𝘁 𝗵𝗼𝘄 𝘄𝗶𝗹𝗹 𝘆𝗼𝘂 𝗱𝗼 𝘁𝗵𝗮𝘁? Here comes A/B Testing. 𝗟𝗲𝘁 𝗺𝗲 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗶𝘁 𝗶𝗻 𝗲𝗮𝘀𝗶𝗲𝘀𝘁 𝘄𝗮𝘆 𝗽𝗼𝘀𝘀𝗶𝗯𝗹𝗲. 𝗛𝗲𝗿𝗲, 𝘄𝗲 𝘄𝗶𝗹𝗹 𝗷𝘂𝘀𝘁 𝘁𝗼𝘂𝗰𝗵 𝘁𝗵𝗲 𝘀𝘂𝗿𝗳𝗮𝗰𝗲 𝗼𝗳 𝗔/𝗕 𝘁𝗲𝘀𝘁𝗶𝗻𝗴. • So, when you have a feature, you simply take a percentage of customers as a sample, let's say 5-10% of total customers. Also ensure that the sample is divided randomly among control and experimental sets. • Then you divide this sample into two sets - control set and experimental set. • Here, experimental set has the new feature while control group doesn't. • Then you run the test so that the data collected is statistically significant (appropriate p-value or confidence intervals). Predefine your significance level, often with a p-value < 0.05 and confidence intervals. • Then you check key metrics of both the sets such as customer engagement, conversion rate, customer retention and more. Key metrics depend upon the industry, feature and more. • Now, if you are uncertain about the result, you can perform A/B testing again with bigger sample, for ex: 25-30% of total customers as sample. This will allow you to have better statistical power if you had high margin of error in your initial test. • Now, if you find that experimental set is performing better than control set, rollout the feature for all the customers. Also, I have excluded a lot of things which might make this explanation complex like you should use power analysis to decide how much customers to select in the sample. Enjoy. Follow for more! #ABTesting #DataAnalysis #DataAnalytics #ProductTesting #DataDriven #CustomerInsights #AnalyticsCommunity #DataScience #TestingStrategies #DataMetrics #DataTesting #CustomerEngagement #AnalyticsJobs #DataProfessionals #GlobalData #TechCareers #AnalyticsHiring #StatisticalAnalysis #DataCareers #ProductAnalytics #AnalyticsExpert #BusinessIntelligence #DataExploration #DataInsights #TechCommunity #DataAnalyticsUSA #DataAnalystUSA #TechJobsUSA #USDataScience #DataCommunityUSA #AnalyticsUSA #DataAnalyticsUK #DataAnalystUK #TechJobsUK #UKDataScience #AnalyticsUK #DataCommunityUK #DataAnalyticsEurope #DataAnalystEurope #TechJobsEurope #EUDataScience #AnalyticsEurope #DataCommunityEurope #GlobalDataAnalytics #DataAnalyticsGlobal #WorldTechJobs #DataScienceWorldwide #AnalyticsWorldwide #GlobalDataAnalyst

'Datatesting: co-developing computational methods for socially evaluating public data infrastructures' investigates the extent to which datasets from open data initiatives address questions related to urgent societal issues. #digitalsociety #digitalgood

We are happy to share that @SilvanavF & @shrapnelofme will be part of the Datatesting team working to co-develop computational methods for socially evaluating public data infrastructures. The project is funded by @digitalgoodnet. Learn more: digitalgood.net/dg-research/…

Datatesting: co-developing computational methods for socially evaluating public data infrastructures led by @jwyg digitalgood.net/dg-research/… @esimperl @kclinformatics @shrapnelofme @SilvanavF @link_digital

Check out this video! Learn how to identify and eliminate duplicate records in your database using iceDQ. Watch now: bit.ly/3MhzmjH Subscribe to our YouTube channel: youtube.com/@iceDQ #iceDQ #RethinkDataReliability #DRE #DataTesting #ETLTesting

Data testing on approaches #greenkeeping #greenkeeper #golf #golfcourse #agronomy #agronomylife #golfmaintenance #datatesting

Explore the future of data #qualityengineering with Infosys. Our platform offers intelligent solutions powered by AI and cloud-native technologies. Discover how you can elevate your data testing workflows. Read more. infy.com/3UmDzGq #InfyTesting #DataTesting

Deep Dive: will synthetic data change QA testing forever? Synthetic enterprise data, powered by the relatively young #GenAI technology, may have the ability to change QA testing forever. Read here why -> qa-financial.com/deep-dive-w… #QA #syntheticData #DataTesting #Data #QATesting

#Blog | In the world of AI, advanced analytics, and big data, the quality of your data directly influences your business decisions and strategies. Read our latest blog to learn how data testing helps businesses reduce errors. testingxperts.com/blog/data-… #datatesting #AI