🚨 KI-Infografiken: Toll anzusehen, Daten oft falsch. Elbphilharmonie 110m statt 108m, Eiffelturm-Fundament 25m statt 6,8m. Problem: Kein Kontext = halluzinierte Infos. Lösung: Recherche + Fact-Check.
👉 Meine Meinung: Ohne saubere Datenbasis macht man sich angreifbar. Fact-Check ist Pflicht, nicht optional.
(Picture & Prompt Credits to TechieSA, 03.01.2026, via X, "Landmark Infographics by GPT-1.5 Image"; Social Media-Bearbeitung: Confias AI Solutions)
Die im Projekt AQinDa entwickelte Webanwendung Constrainify hilft dabei, Datenqualität zu spezifizieren und zu analysieren, ohne dass dafür fortgeschrittenes technisches Know-How erforderlich ist. Unsere KollegInnen haben dazu im Rahmen eines Workshops eine Einführung verfasst: https://doi.org/10.5281/zenodo.18430881
#dataquality #metadata #datascience #digitalhumanities #vzg #gbv #AQinDa
Want a better data stack + love fun data-pun stickers?
Join the Data Valentine Challenge Feb 9–13 🦾
Attend all 5 webinars + 1 social post = get a sticker.
Signup here: https://reccehq.com/data-valentine-week-challenge/
Next week: Fall in love with your data 💕 The Data Valentine Challenge February 9–13
💘 5 days, 5 companies, 5 real fixes for your data stack
Data teams are drowning. You don't need another course. You need quick wins.
Join us for 30-60-minute daily challenges:
💗 Fix what's broken
💗 Ship with confidence
💗 Stop firefighting
Featuring: #Recce | #Greybeam | #dltHub | #DatabaseTycoon | #Bauplan
Registration opens soon 📅
Scaling AI for the NFL and the Olympics reveals a bigger problem: data quality, not smarter prompts. Learn why vector databases, defensive data engineering, and RAG matter for reliable sports analytics and enterprise AI. Open‑source tools can help—read the full analysis. #DataQuality #VectorDB #SportsAnalytics #EnterpriseAI
🔗 https://aidailypost.com/news/ai-nfl-olympic-scale-needs-data-quality-creed-not-better-prompts
Marketers bet on AI media despite struggling with generative AI adoption: Mediaocean survey shows 54% of marketers increasing AI media spend while 42% struggle with data quality issues preventing broader AI implementation. https://ppc.land/marketers-bet-on-ai-media-despite-struggling-with-generative-ai-adoption/ #AIMedia #DigitalMarketing #MarketingTrends #GenerativeAI #DataQuality
7 Ways Data Appending Enriches Your Customer Database
Data appending services help B2B teams complete missing contact details, refresh outdated records, and improve CRM accuracy. Cleaner, enriched data supports better segmentation, targeted outreach, and more confident sales and marketing decisions.
Learn more: https://www.linkedin.com/pulse/7-ways-data-appending-enriches-your-customer-database-habile-data-pussf/
Data Manager spécialisé dans la gouvernance des données, l’intégration de systèmes et la conformité. À la recherche de nouveaux défis pour optimiser la gestion des données.
#DataManagement #DataGovernance #Compliance #DataQuality #DataManager ...
Poor data quality costs organisations millions annually, but advanced automation and human-in-the-loop approaches can significantly mitigate these losses. Effective content repair enhances decision-making, trust, and operational efficiency.
Discover more at https://smarterarticles.co.uk/the-real-cost-of-bad-data-automated-repair-and-business-impact?pk_campaign=rss-feed
#HumanInTheLoop #DataQuality #AIinBusiness #ContentRepair
Poor JSON validation and schema drift quietly cause data failures costing enterprises millions annually. Implementing layered defence, validation, and observability strategies can mitigate these risks and improve data integrity.
Discover more at https://smarterarticles.co.uk/the-quiet-catastrophe-how-bad-json-costs-enterprises-millions?pk_campaign=rss-feed
#DataQuality #SchemaValidation #DataGovernance #HumanInTheLoop
Việc phân khúc đối tượng không chính xác? Nguyên nhân có thể là do dữ liệu người dùng không hoạt động. Bằng cách loại bỏ các tài khoản không hoạt động trước khi xây dựng phân khúc, độ chính xác và hiệu quả của chiến dịch đã được cải thiện đáng kể. Phân tích dữ liệu cũng trở nên đáng tin cậy hơn.
#Marketing #DataAnalysis #DataQuality #AudienceSegmentation #SaaS #PhanTichDuLieu #ChatLuongDuLieu #PhanKhucDoiTuong
https://www.reddit.com/r/SaaS/comments/1qcirmu/improved_audience_segmentation_accura
Top 10 Ways to Clean Your CRM Data for Better Performance
Messy CRM data can slow down your sales, marketing, and customer experience. Discover the top 10 practical ways to clean, organize, and maintain high-quality CRM data, eliminate duplicates, improve accuracy, and unlock better insights for smarter decision-making.
Know More: https://peerlist.io/jagadishthakar/articles/top-ways-to-clean-your-data-in-crm
#CRM #DataCleaning #DataQuality #CustomerData #SalesAutomation #DataCleaningServices #CRMDataServices
Bad data secretly slows development. Learn why data quality APIs are becoming core DX infrastructure in API-first systems and how they accelerate teams. https://hackernoon.com/why-data-quality-is-becoming-a-core-developer-experience-metric #dataquality
@techglimmer
I completely agree with the shift in focus from the size of the model to the systems around it.
But the key question goes deeper: what is the basis for confidence in the correctness of these orchestrated systems' actions?
Without 100% verifiable data, clear ontologies, and reproducible evaluation criteria, any "agent-based architecture" will only scale up errors—even if it does so in a socially acceptable manner.
#SystemsThinking #DataQuality #Verification #AI
We've found recruiting people for online #research via #onlineAdvertising yielded good results on overt and covert #dataQuality measures (perhaps because participation incentives aren't financial):
Attention checks passed ≅ 2.6 out of 3
ReCAPTCHA (v3) ≅ 0.94 out of 1.0
Sample size > 5000 (from six continents)
https://doi.org/10.1017/S0034412525000198
#surveyMethods #cogSci #psychology #xPhi #QualityControl #econ #marketing
Strong data catalogs transform the way teams think. They shift work from guesswork to clear action. What’s your take? #DataCatalogs #SingleSourceOfTruth #DataQuality #DataTrust #DataOps #DataStrategy #AIReadyData #DataFuture
https://www.linkedin.com/pulse/building-data-catalogs-quiet-power-behind-single-truth-mohindroo--icosc
Strong data catalogs transform the way teams think. They shift work from guesswork to clear action. What’s your take? #DataCatalogs #SingleSourceOfTruth #DataQuality #DataTrust #DataOps #DataStrategy #AIReadyData #DataFuture
https://www.linkedin.com/pulse/building-data-catalogs-quiet-power-behind-single-truth-mohindroo--icosc
#MeetTheExperts #mte #KODAQS #DataQuality
New #video on #YouTube: Meet our expert Fabienne Kramer who highlights #Resquin, a tool designed to assess measurement errors and response biases in multi-item survey scales.
https://youtu.be/cnYADy7hhZg
Customer Data Enrichment vs Data Cleaning: Understanding the Real Differences
Explore how customer data enrichment differs from regular data cleaning. Learn how enrichment adds valuable insights like demographics and behavior, while data cleansing services remove errors to improve data quality and smarter decision-making.
Know More: https://froodl.com/what-makes-customer-data-enrichment-different-from-regular-data-cleaning
#CustomerData #DataEnrichment #DataCleaning #DataQuality #MarketingAnalytics #CRMData
How to Find Join Duplication in a Data Pipeline
A join-audit function that catches row explosions before they hit production.
One “innocent” join can silently multiply rows, inflate metrics, and break downstream models.
This post shows a simple audit approach (keys, cardinality checks, before/after counts) to detect duplication early.
#DataEngineering #SQL #Python #DataQuality #dataScience
@chartrdaily @pythonclcoding @programming @towardsdatascience
