DQE, Data Quality Everywhere’s Post

An effective method for managing the quality of data leading to highest accuracy level through consistent improvements. To know more, click here : https://lnkd.in/grcaY8KP Dr Imad Syed Kevin Attard Georgios Galanakis PiLog Innovation Labs #DataQuality #DataManagement #DataIntegrity #DataGovernance #DataAccuracy #DataCleansing #DataValidation #DataVerification #DataQualityManagement #DataQualityControl #CleanData #DataScrubbing #DataQualityFramework #QualityData #DataCleaning #DataAccuracy #Leandatagovernance

One hundred years ago this month, Walter Shewhart wrote a memo that contained the first process behavior chart. In recognition of this centennial, this column reviews four different applications of the techniques that grew out of that memo. The first principle for interpreting data is that no data have any meaning apart from their context. Context tells us what type of analysis is appropriate, and how to interpret the results of our analysis. The following will illustrate this principle. Read the full piece by Donald J. Wheeler Ph.D., a fellow at the American Statistical Association - ASA and the American Society for Quality, in Quality Digest: https://lnkd.in/e7nakS5j #quality #qms #qualityassurance #qualitycontrol #qualitymanagement #iso9001 #industry40 #canvasenvision

🌟 📑 *** Streamlining Complex Queries: Meet Multi-Head RAG 💡 📈 🔍 What’s the challenge? Current Retrieval Augmented Generation (RAG) systems often struggle with complex queries that require retrieving multiple documents with significantly different content. This issue arises because the embeddings of these diverse documents can be far apart in the embedding space, making accurate retrieval difficult. 🚀 Enter Multi-Head RAG (MRAG) Developed by a team of researchers from ETH Zurich and other prestigious institutions, MRAG tackles this problem head-on. Instead of using the traditional decoder layer for embeddings, MRAG leverages the activations from the Transformer’s multi-head attention layers. Each attention head captures different aspects of the data, enabling MRAG to handle complex, multi-faceted queries with greater precision. 📈 Key Benefits: • Improved Retrieval Accuracy: MRAG shows up to a 20% improvement in relevance over standard RAG baselines. • Efficiency: It integrates seamlessly with existing RAG frameworks without requiring additional resources or storage. • Versatility: Suitable for various industry applications, from legal document synthesis to accident cause analysis in chemical plants. 🛠️ How It Works: 1. Data Preparation: Create multi-aspect embeddings for text chunks and store them in a vector database. 2. Query Execution: Generate a multi-aspect embedding for the query and use a special retrieval strategy to fetch the most relevant documents. 📊 Real-World Impact: MRAG’s effectiveness is demonstrated through robust evaluation, including synthetic datasets and real-world use cases. This makes it a game-changer for industries that deal with complex queries on a regular basis. 📚 Reference: Besta, M., Kubicek, A., Niggli, R., Gerstenberger, R., Weitzendorf, L., Chi, M., Iff, P., Gajda, J., Nyczyk, P., Müller, J., Niewiadomski, H., Chrapek, M., Podstawski, M., Hoeffer, T. (2024). Multi-Head RAG: Solving Multi-Aspect Problems with LLMs. ETH Zurich.

Quality control in #marketresearch ensures that the #data collected and analyzed is accurate, reliable, and actionable. It encompasses a range of practices designed to detect and correct errors, prevent data manipulation, and guarantee that the research findings truly represent the target population's views. #qc #qualitycontrol #data #marketresearch Read more- https://lnkd.in/gyhD9x46

🔍 The Importance of Data Quality in Decision-Making In today’s fast-paced world, the quality of your data is more crucial than ever. Using inaccurate or incomplete data can lead to poor decisions, affecting not just individual outcomes but entire organizations. This is why we at WSD/SPi invest significant time and effort into ensuring our data coverage is impeccable. 1️⃣ Time Investment in Data Coverage We spend countless hours refining our processes to ensure that our data coverage is as comprehensive and accurate as possible. Perfect coverage isn’t just a goal—it’s a necessity. 2️⃣ Rigorous Data Validation Data quality isn’t just about collecting information; it’s about verifying it. We employ both automated and human validation processes to catch and correct errors before they impact our clients. This meticulous attention to detail sets us apart. 3️⃣ Continuous Improvement Through Feedback We believe that data quality is a journey, not a destination. That’s why we constantly seek feedback from our clients and partners. Every day, we’re on the lookout for errors and new ways to enhance our data, ensuring that it meets the highest standards. This dedication to data quality is why Structured Products Intelligence | SPi boasts the best #structuredproducts database in the industry. To make it evident, here’s the correct volume for the US market in H1 2024 (by issue date): 📊 Registered Notes - $81.3 billion (100% coverage) 📊 Unregistered Notes - $8.4 billion (we estimate this represents 80% of the total market) 📊 MLCDs - $1.4 billion (we estimate this represents 50% of the total market) Accurate data is the foundation of sound decisions. Let's keep striving for excellence! 💪 #DataQuality #DataValidation #ContinuousImprovement

How to Classify the types of #Data? Why this #classification is important? #Data #Dataintegrity #Integrity #Contemporaneous #ALCOA #QA #QMS https://lnkd.in/g2ThGwkD

Our Head of Product, Tereza Mlynářová, explores the limitations of relying solely on data for data quality rules creation in complex fields like transportation. ➡️ Discover why human expertise is crucial for accurate data insights! 🔗 https://bit.ly/3VNdseb #Accurity #dataquality #transportation #datamanagement

Our Head of Product, Tereza Mlynářová, explores the limitations of relying solely on data for data quality rules creation in complex fields like transportation. ➡️ Discover why human expertise is crucial for accurate data insights! 🔗 https://bit.ly/3VNdseb #Accurity #dataquality #transportation #datamanagement

Diving into the world of data quality: Data Validation vs. Verification. 📊 Understanding the distinction between these two is crucial for ensuring the accuracy and reliability of your data. Data Validation involves checking if the data meets predefined criteria and standards. It's like ensuring that the puzzle pieces fit perfectly before completing the picture. 🧩 On the other hand, Data Verification confirms the accuracy and correctness of the data by cross-referencing it with external sources or applying checksums. It's like double-checking your calculations to ensure they're error-free. ✅ Both processes are essential pillars of data quality management, each serving a distinct purpose in the quest for trustworthy data. 💡 Let's keep honing our skills in data validation and verification to ensure our insights are built on a solid foundation of reliable data! 💪 #DataQuality #DataValidation #DataVerification #DataAnalytics #DataManagement

🌟 Exciting New Publication! 🌟 📢 Thrilled to announce that our paper "Efficient and Reliable Estimation of Knowledge Graph Accuracy" w/Stefano Marchesin has been accepted to PVLDB Vol. 17, a premier conference on databases! 🎉 Join us in Guangzhou, China, where we'll be presenting our latest research. 🔍 In a world where data accuracy is paramount, especially in Knowledge Graphs (KGs), our work dives into the challenges of auditing KG accuracy. Manual evaluation of large-scale KGs is undeniably costly, spurring the need for efficient sampling techniques. Our research addresses the limitations of current methods, pushing accuracy estimation forward. 💡 Rooted in the Wilson method and tailored for complex sampling designs, our solution tackles reliability issues head-on, ensuring applicability across diverse evaluation scenarios. Our methods boost the reliability of accuracy estimates by up to two times compared to the state-of-the-art, all while maintaining or improving efficiency. Plus, this consistency holds regardless of KG size or topology. 📄 Dive into our preprint to learn more about our findings: https://lnkd.in/ezGQu7YC Join us in pushing the boundaries of KG accuracy estimation! Let's shape the future of data quality together. 🚀 #DataScience #KnowledgeGraphs #DataQuality #Research #VLDB2024