The article explains how scaling laws serve as a budget guide for training large language models, comparing Kaplan’s and Chinchilla’s findings, illustrating optimal parameter‑token trade‑offs, and highlighting the impact of data quality and duplication on model performance.
The article details how Alibaba’s production‑grade Verify‑Data Agent Skill replaces manual, multi‑SQL data validation with a single natural‑language command, automating table discovery, SQL generation, execution, and review‑level reporting, achieving up to 30‑minute turnaround, comprehensive coverage, and robust risk controls for big‑data pipelines.
The article explains how the softmax activation function converts neural network outputs into probability distributions for multi‑class NLP tasks, describes its mathematical form and S‑shaped behavior, and discusses the inductive approach, data quality, training objectives, and interpretability challenges in deep learning language models.
A real‑world incident where an ADS table stopped updating triggered a three‑second root‑cause discovery and a three‑hour data‑warehouse rebuild using a Claude‑based Skill that eliminated about 70% of the manual, repetitive steps traditionally required in data development, testing, deployment, and operations.
This article examines six production trade‑offs that AI engineers face—build vs. buy LLMs, model complexity vs. maintainability, data quantity vs. quality, batch vs. real‑time inference, prompt engineering vs. fine‑tuning, and automation vs. human‑in‑the‑loop—backed by surveys, research studies, and concrete cost analyses.
Even with meticulous cleaning, residual noise, redundant legal clauses, and approximate duplicates can degrade retrieval and generation in RAG systems, while privacy risks from embedding inversion and the need for continuous, metric‑driven governance make data quality the ultimate ceiling for performance.
The article analyzes how RAG systems inherit classic data‑quality problems, explains why clean input is essential for retrieval and generation, outlines historical GIGO lessons, highlights new risks introduced by vectorization and LLMs, and reviews practical chunking and governance strategies to mitigate hidden failures.
Most enterprises can only feed AI with less than 15% of their knowledge assets because 80% of their data is hidden in PDFs, emails and chat logs, making data governance the true competitive moat and requiring a three‑step approach to turn dormant data into AI‑ready fuel.
This guide walks AI engineers and project leaders through the full lifecycle of high‑quality dataset creation—from defining requirements and setting annotation standards to data collection, preprocessing, labeling, augmentation, evaluation, and continuous iteration—providing concrete metrics, compliance rules, and tool recommendations to avoid common pitfalls.
Zhang Lu, a Silicon Valley investor, argues that AI's focus is shifting from training to inference—now accounting for half of current compute and projected to reach 70%—while communication energy, data quality, physical AI, and edge deployment become the next critical bottlenecks and opportunities across medical, space, and nano‑robotics applications.
Most data‑governance initiatives fail not because of lacking technology but because they add friction; the article explains how companies mistakenly focus on rules, platforms, and processes, and offers a step‑by‑step approach—identifying high‑value tables, minimal metadata, targeted quality rules, and fast issue diagnosis—to make governance truly useful.
This playbook provides enterprise‑level data warehouse engineers, ETL developers, data modelers, and data‑team managers with a complete, logical, and actionable set of standards, processes, and best‑practice guidelines covering architecture, development principles, role responsibilities, end‑to‑end workflow, metadata, security, performance metrics, and team collaboration.
In a rapidly growing data‑driven environment, Kuaishou’s Life Services Data Center tackled exploding demand and limited manpower by replacing traditional siloed data‑warehouse practices with AI‑driven intelligent review, DQC, and chatbot solutions, achieving up to 11.34% productivity gains and dramatically improving data quality.
This article examines how AI large models can transform data warehouse development by automating modeling, improving data cleansing and quality auditing, and enabling intelligent operations, while also highlighting five common implementation pitfalls and practical best‑practice recommendations for enterprises seeking cost, efficiency, and quality gains.
This article explains the design of a zero‑code, real‑time data quality alert platform that leverages Binlog‑based ingestion, configurable metrics, automated attribution, and LLM‑driven decision making to provide fine‑grained monitoring, rapid response, and measurable operational benefits across marketing workflows.
In February 2026, China introduced a pioneering group standard that defines executable acceptance rules for AI training datasets, linking data delivery, quality assessment, and model training through a three‑layer framework, quantitative metrics, and a pre‑negotiated quality baseline to reduce disputes and costs.
The article explains how scaling AI agents reveals fragmented, inconsistent internal documentation, and argues that high‑quality production knowledge bases require a company‑wide, role‑based process, concrete writing rules, continuous inspection, and cross‑department ownership to ensure AI answers remain accurate and user‑focused.
VisionPangu demonstrates that a compact 1.7 B‑parameter multimodal model can generate richly detailed, coherent image descriptions that rival much larger models by leveraging high‑quality dense data, a three‑part architecture, and a two‑stage deep alignment training strategy.
This article walks through the complete workflow of using EasyDataset to create high‑quality question‑answer pairs for supervised fine‑tuning, covering question generation (single and batch), three generation algorithms, answer generation (including chain‑of‑thought and multi‑turn dialogue), a hybrid quality‑assessment pipeline, and export to Alpaca or ShareGPT formats.
This article explains how DataWorks integrates data quality specifications directly into the SQL development workflow using Data Contracts, addressing governance lag, versioning gaps, and trust issues while providing a unified, version‑controlled, and automated quality assurance process for offline data pipelines.
The article explains how the newly released "AI Training Data Set Delivery and Quality Acceptance Specification" addresses gaps in existing data‑quality standards by defining a three‑layer acceptance framework, quantitative metrics, and a pre‑negotiated quality‑baseline mechanism to make dataset delivery verifiable and directly supportive of model training goals.
This checklist compiles 30 actionable items covering model design, data consistency, performance, quality, metadata governance, cost efficiency, and collaboration to help data warehouse teams build trustworthy, high‑performance, and maintainable data pipelines.
This article examines the shortcomings of naïve GraphRAG implementations on clinical data and explains how an ontology‑driven, zero‑noise GraphRAG architecture can create self‑improving, conflict‑free knowledge graphs for AI applications.
The article analyzes Surge AI’s $1.2 billion revenue, bootstrapped model, elite 100 k‑labeler network, three‑layer architecture, RLHF, AdvancedIF/RIFL benchmarks, red‑team testing, RL environments, and evaluates its competitive moat and future strategic paths.
This article explains how AI agents transform traditional data governance by introducing a four‑layer perception‑decision‑execution‑learning architecture, detailing the required technologies, tool integrations, code examples, deployment steps, team roles, security safeguards, and practical rollout strategies for enterprises seeking automated, intelligent data management.
This article walks through a real text‑classification project where noisy labels inflated the review workload to over 15,000 samples, and shows how using cleanlab’s confident‑learning framework reduced the manual audit set to 438 items, boosting efficiency by thirty‑four times while improving model performance.
This article explains why static RAG metrics don’t apply to dynamic pipelines, introduces five essential dimensions—Parseability, Deduplication, Relevance, Chunk Quality, and Freshness—and shows how to combine them into a weighted score that enables monitoring, alerts, and continuous improvement of dynamic RAG systems.
The article explains how the rapid rise of large‑model AI has shifted the focus from models to data, outlines the concept and stages of AI‑specific data governance, identifies challenges such as low‑quality data, privacy leaks, bias, and proposes a comprehensive framework of principles, processes, and technologies to ensure high‑quality, secure, and ethical AI deployment.
This article walks you through why unmanaged data warehouses fail, outlines three golden governance principles, details five practical implementation steps—from building a data lineage map to creating business‑driven quality dashboards—and shares real‑world case studies and common pitfalls to help turn your data warehouse into a trusted decision‑making engine.
Researchers from Texas A&M and UT Austin demonstrate that prolonged pre‑training on low‑quality, short‑form web content causes large language models to suffer irreversible cognitive decline—manifested as attention loss, broken reasoning chains, and personality distortion—highlighting data quality as a critical training‑time safety issue.
This article shares Ant Group’s practical experience in large-scale data governance, outlining four main topics—overall governance overview, data quality management, data storage-processing governance, and future considerations—while emphasizing the five critical aspects of architecture, security, compliance, quality, and value that drive effective big-data operations.
Ant Group shares its practical experience in big data governance, outlining five key focus areas—architecture, security, compliance, quality, and value—through four structured sections and detailed discussions on data quality and storage governance, while also exploring future challenges and the economics of data.
At the 2025 Inclusion·Bund Conference forum, leading scholars and industry experts revealed how high‑quality data and AI form a dual‑engine that reshapes model training, improves performance, and drives the next evolution of intelligent systems.
The Inclusion·外滩大会 forum explored how diminishing returns from massive models demand a shift toward cognitive reasoning, autonomous evolution, multi‑agent coordination, reinforcement learning, high‑quality data, and MoE diffusion models to bridge digital AI with the physical world.
The article explains why data quality is critical for modern businesses, outlines the challenges data engineers face when monitoring hundreds of models, and provides a step‑by‑step guide to constructing an automated, AI‑driven data quality monitoring system using Strands Agents, Amazon Bedrock, dbt and Amazon Redshift, including code examples, workflow orchestration, security hardening, containerized deployment and observability.
This article explains how to integrate Dify with Alibaba Cloud's Data Management Service (DMS) and the Bailei large‑model platform to create an end‑to‑end, AI‑driven customer service conversation quality inspection solution that streamlines data handling, enhances security, and reduces operational costs.
This article details Baidu MEG’s TDS (Turing Data Studio) platform, explaining its three‑pillar governance framework—process standardization, quality controllability, and intelligent operations—along with concrete mechanisms, automation, and measurable results that dramatically improve data reliability, operational efficiency, and fault‑tolerance in large‑scale data production.
This article introduces the comprehensive design and evolution of a unified metadata lineage platform for big data, covering background, data processing chain, lineage models, system architecture, quality metrics, application scenarios, and future plans to enhance data governance, quality, and security.
This article explores the evolution of questionnaire surveys from costly offline methods to modern online panels, reveals how monetary incentives create professional respondents and data fraud, and outlines rigorous methodologies—including diversified sampling, balanced reward design, and multi‑layered quality controls—to obtain high‑quality market research data.
This article examines the essence of data governance, outlines its four core domains, proposes a strategic and technical implementation roadmap, evaluates effectiveness with the DCAM model, and explores how AI large models can enhance metadata, data quality, and compliance while highlighting practical limitations and future trends.
The article enumerates common pitfalls of Retrieval‑Augmented Generation—such as missing content, low‑rank document misses, context limits, format errors, incomplete answers, scalability bottlenecks, complex PDF extraction, data‑quality issues, domain adaptation gaps, hallucinations, and feedback‑loop deficiencies—and offers concrete mitigation strategies ranging from data cleaning and prompt design to hybrid search, hierarchical retrieval, document compression, and automated evaluation.
The article explains why high‑quality, diverse data—rather than just advanced models—has become the decisive factor for enterprise AI success, outlining key dimensions of data quality, strategies for building diverse datasets, and practical steps for establishing a data‑first AI strategy.
This guide presents a comprehensive 2025 enterprise data quality governance strategy, covering objectives, common challenges, a three‑dimensional governance model, control mechanisms, organizational structures, phased implementation roadmaps, recommended technical tools, and industry best‑practice case studies.
This article examines how artificial‑intelligence techniques such as natural‑language processing, knowledge graphs, federated learning and automated ETL are applied across six core data‑governance scenarios—standardization, asset management, master data, data‑warehouse automation, security/privacy, and real‑time quality monitoring—showing measurable efficiency gains and business impact.
This article reviews common interview questions for data development roles, covering Spark stage partitioning and optimization, criteria for evaluating data warehouses, Flink's handling of late data, and provides practical answers and resources to help candidates deliver standout responses.
The article walks through five iterative experiments using MySQL HeatWave AutoML on a golf league dataset, showing how data quality, feature selection, and over‑fitting affect predictive accuracy and emphasizing that high‑quality data is essential for reliable AI models.
This guide outlines eight concrete methods DeepSeek uses to improve data quality—including automated cleaning, validation, classification, monitoring, governance standards, anomaly detection, integration, and intelligent analysis—providing actionable steps for organizations to enhance data accuracy, completeness, consistency, and usability.
This article explains how Ctrip’s hybrid‑cloud FinOps billing system uses a custom Kafka Gatekeeper to detect, locate, and automatically remediate data‑quality issues across dozens of self‑built PaaS services, improving coverage, timeliness, and responsibility attribution while supporting high‑availability deployments.
This article explains the data resourceization stage, outlines seven core data management functions, introduces the DataOps paradigm to overcome traditional inefficiencies, and showcases real‑world case studies and a panoramic data architecture view for modern enterprises.
The article emphasizes that while AI can assist data governance, organizations must first build a solid, high‑quality data asset knowledge base through sustained effort, otherwise they will face the inevitable problem of garbage‑in‑garbage‑out.
DeepSeek, the popular Chinese large‑language model, boosts data‑warehouse engineers' productivity by offering free, open‑source AI assistance across code writing, model design, metadata management, data quality monitoring, and governance, ultimately maximizing enterprise data asset value.
This article describes the challenges of data quality in Ctrip’s hybrid‑cloud FinOps billing system and presents the design, implementation, and high‑availability deployment of a custom Kafka Gatekeeper proxy that performs pre‑validation, configurable rules, self‑service dashboards, and automated alerts to improve coverage, timeliness, and responsibility attribution.
This article introduces Douyin Group’s end‑to‑end data asset management platform, explains the evolution and architecture of its large‑scale data lineage system, presents quality metrics and ecosystem components, and outlines practical applications and future directions for data governance, development, and security.
This article details Huolala's challenges in risk‑control testing amid rapid business growth, outlines the inefficiencies of manual configuration verification, and explains how a suite of automated tools and a full‑scope interception strategy dramatically improved testing efficiency, data quality assurance, and cross‑team collaboration.
From the excitement of first encountering mathematical modeling to becoming a seasoned practitioner, this guide outlines five progressive stages, reveals typical misconceptions at each level, and offers practical advice to help learners avoid common traps and develop both technical and soft skills.
The article explains how breakthroughs in artificial intelligence depend on high‑quality, large‑scale, and diverse training data, outlines the data‑centric AI movement, details a six‑step workflow for building datasets, and surveys the data industry ecosystem supporting large language model development.
This guide explains when to rely on prompt engineering versus SFT fine‑tuning for Vision‑Language Models, emphasizing data quality, appropriate dataset sizes, training epochs, hyper‑parameter tuning, and practical steps to build robust VLM pipelines.
This article explains how Douyin Group tackles massive data volume, quality, and efficiency issues by building a four‑layer intelligent platform, standardizing metric management, automating metric decomposition, and creating reusable metric services that boost agility, stability, and cross‑team collaboration.
This article surveys and categorizes instruction data selection techniques for large language model fine‑tuning, explaining metric‑based, trainable‑LLM, powerful‑LLM, and small‑model approaches, detailing representative papers, their pipelines, and empirical findings on data quality and diversity.
This article surveys data management for large language model training, covering an overview, pre‑training data composition, scaling‑law‑driven quantity control, quality filtering, deduplication, harmful‑content removal, instruction fine‑tuning strategies, dynamic data selection, and emerging research challenges such as bias mitigation, multimodal data handling, and synthetic‑data filtering.
This article explores how the rise of massive AI models reshapes data governance, detailing model fundamentals, architectural types, emerging challenges, a five‑domain governance framework, and practical AI‑driven applications for data standards, metadata, quality, and security, while also looking ahead to future trends.
This article presents Ant Group's recent explorations and practices in real-time data warehousing, detailing its architecture, data quality assurance, stream‑batch integration, and future data lake implementation, while highlighting the use of Flink, ODPS, and Paimon for scalable, low‑latency analytics.
This article explains the concept of data standards, outlines the three main categories of data standards, describes a four‑stage implementation process, and provides a real‑world bank case study to illustrate how enterprises can establish and apply data standards for better data quality and value.
This article explains how to construct a full‑chain data lineage system for e‑commerce, covering the concepts of data lineage, the design of a lineage foundation, quality measurement, application‑level lineage, and practical use cases such as table migration, field‑level tracing, and automated metric decomposition.
Event‑tracking governance ensures accurate, consistent user‑behavior data by managing the full lifecycle of logging points through defined quality standards, a digitized workflow, and supporting tools such as rule editors, real‑time testing, and compliance monitoring, while the platform’s page‑scene tree model and metrics improve visibility, reduce duplication, and drive business insight.
The article explains event tracking, its data‑quality challenges, and presents a logging platform that enforces quality standards, an end‑to‑end online workflow, and specialized design, testing, and validation tools—including extended field types—to govern, monitor, and improve tracking point compliance across applications.
This article presents Ant Group's comprehensive data governance experience, covering data quality management, storage governance, architectural design, operational strategies, case studies, and forward‑looking thoughts on integrated lake‑warehouse governance, data value realization, and AI‑driven automation.
This article presents a comprehensive data lineage system (Data-Lineage) for big data platforms, addressing challenges in heterogeneous data sources, multiple execution engines, and complex dependencies through hook-based architecture and modular design.
This article explains how to assess and improve data quality by moving from simple compliance checks to deeper reasonableness analysis, using visual dashboards, a comprehensive quality‑review tool suite, intelligent judgement rules, self‑diagnosis utilities, and key technical components such as sample libraries and a three‑layer architecture.
This article explains common data‑quality challenges when rebuilding business systems, compares manual SQL‑based validation with a dedicated data‑comparison product, and walks through practical steps for configuring, executing, and reviewing automated data‑matching tasks in a big‑data environment.
Enterprises facing fragmented systems like OA, HR, CRM, and ERP must adopt a unified master data management framework that defines standards, governance, organizational structures, and integrated platforms to ensure data consistency, accuracy, and real‑time availability, thereby reducing maintenance costs and supporting digital transformation.
This article presents Bilibili's practical data governance operational framework, introducing the DAMA‑Bok methodology, detailing two real‑world cases on storage‑level risk and data‑loss post‑mortem, and outlining the organizational, metadata, and embedded governance mechanisms that drive cost and quality improvements.
This article explains how a comprehensive data governance framework—covering data quality, metadata, master data, asset management, security, and standards—can ensure high‑quality, safe, and traceable business data while outlining implementation steps, organizational roles, platform features, and assessment methods.
This article outlines ten practical principles for designing applications with large language models, emphasizing a model‑first mindset, precision through interactive disambiguation, clear division of code and model responsibilities, data quality, handling uncertainty, and recognizing the limits of LLMs to build robust, maintainable software.
This article explains how the CaR (Clustering and Ranking) approach evaluates data quality with a scoring model and selects diverse samples via PCA‑reduced sentence embeddings and K‑Means clustering, achieving comparable or superior large‑model performance while using just 1.96% of the original dataset.
This article presents a comprehensive, scenario-driven data governance practice at Guoxin Securities, covering the industry's pain points, a three‑layer governance framework, detailed implementations for data standards, metadata, data quality, data modeling, and data security, and outlines future directions for intelligent and measurable governance.
This article explains how Tubi monitors the quality of its recommendation system by identifying potential failure points, tracking key data streams such as model input, final recommendation output, and training data, and designing a scalable, real‑time monitoring solution with clear protocols and extensible metrics.
This article provides an in‑depth comparison of the open‑source SQL quality management platforms SQLE and Yearning, covering their architecture, supported data sources, UI design, SQL workbench capabilities, user management, ticket workflow, system settings, and overall suitability for different database environments.
This article outlines the securities sector's data‑governance background, vision, common pain points, and a comprehensive service‑oriented framework covering data models, quality, standards, metadata, and security, and demonstrates the resulting business value and future outlook.
This presentation by Zhou Jie, a senior data R&D expert at Ant Financial, outlines the scope of data governance, examines the challenges of ensuring high‑quality financial data, and shares practical architectures, solutions, and case studies to help attendees understand data quality risks and mitigation strategies.
This article describes the design and implementation of a platform‑wide Data Quality Center for offline big‑data pipelines, covering research of existing solutions, design goals, system architecture based on DolphinScheduler, rule definition language, binding and execution mechanisms, and future enhancements such as lineage monitoring and real‑time checks.
This presentation by senior data warehouse engineer Huang Luo details Yipay's end‑to‑end data warehouse build, covering background challenges, governance framework, platform development, layered architecture, naming standards, monitoring, and future plans, offering practical insights for data engineers, architects, and business stakeholders.
This article details how MaShang Consumer Finance leverages large language models for sales, collection, and customer service, covering company background, AI research achievements, model training infrastructure, data‑quality and compliance challenges, prompt engineering, inference acceleration, evaluation methods, and lessons learned from real‑world deployment.
Many enterprises struggle with fragmented master data spread across isolated systems, but implementing a master data management platform enables unified lifecycle control, consistent standards, and reduced maintenance costs, improving data quality across departments.
This article details how a risk‑control team built a global feature relationship graph, precise testing platform, and full‑domain interception to dramatically reduce regression testing time, improve data quality, and boost overall testing efficiency across the organization.
This article explains why data quality is vital for business insight, outlines the PDCA cycle for quality improvement, lists common causes of data issues, presents two real‑world management scenarios, and showcases a practical tool for tracking and resolving data quality problems.
The article explains how combining traditional monitoring with modern observability, supported by data quality practices and unified workflows, enables more reliable, scalable, and insightful application performance management in agile and cloud‑native environments.
Enterprise data governance faces challenges like information silos, departmental walls, and unclear responsibilities; adopting a three‑dimensional “business‑technology‑organization” framework—setting standards, optimizing processes, and innovating structures—helps eliminate these obstacles, enhance collaboration, improve data quality, and drive cost‑saving, efficiency, and innovation.
This article details practical methods for testing BI reports built on Hive and StarRocks, covering the report creation workflow, testing characteristics, SQL writing techniques, impact analysis, data warehouse simplification, and the application of data quality tools to ensure accurate and efficient reporting.
This article outlines a real‑world enterprise master data project, detailing the definition of master data, the four critical data‑quality challenges faced, the comprehensive solution framework with executive backing, and the six measurable outcomes that improved data governance, efficiency, and decision‑making across the organization.
This article details how XCMG Mining Machinery leveraged the DCMM framework to drive digital transformation, improve data governance, address data quality and security challenges, and establish a sustainable data-driven culture across the organization, highlighting the background, implementation steps, lessons learned, and future outlook.
This article presents Bilibili's data quality assurance system, detailing its evolution across four stages, the architectural framework, core capabilities such as a quality data warehouse, monitoring, collaborative safeguards, digital-driven optimization, and efficient incident handling, along with practical case studies and future outlooks.
This article explains why enterprises need a data asset framework, outlines its key components such as catalog management, policy support, and development trends, and provides a step‑by‑step guide with visual diagrams for constructing and operating a comprehensive data asset management system.
This article presents a comprehensive overview of Zhihu's event‑tracking system, covering its evolution from early Hadoop‑based pipelines to cloud‑native architectures, detailing toolsets for requirement management, validation, data collection, querying, and service design, and concluding with a practical Q&A on best practices and optimization.
This article systematically outlines the e‑commerce product team's responsibilities, the users and consumer pain points it addresses, the core technical challenges such as high‑concurrency reads/writes and AI‑driven automation, and the innovative solutions the team has implemented to keep the product domain healthy, efficient, and intelligent.
This article explains the challenges of data asset lineage in large data warehouses, presents a comprehensive approach using business‑level instrumentation, SQL interceptor plugins, and ETL script parsing to generate fine‑grained lineage graphs, and demonstrates measurable improvements in coverage and zombie‑table cleanup.
This article examines why data governance has become a critical pillar for Chinese financial institutions, outlining external regulations and internal business drivers, describing a comprehensive governance architecture, and presenting a detailed case study of a securities company's data‑asset inventory, platform implementation, and quality management.
This article explains the concept, framework, common data problems, three-stage integration model, implementation methods, and the strategic significance of business‑finance data governance for improving data quality, managing data assets, and accelerating enterprise financial digital transformation.
The article explains why continuous, proactive data maintenance is essential for an outcome‑driven enterprise data strategy, outlines the risks of poor data quality, and provides practical steps—including business rules, service agreements, KPIs, and ownership—to establish an always‑on data‑maintenance process.
The article examines a real‑world Kafka record error that surfaced after 8 am, outlines how to quickly locate and correct the issue by 10 am while minimizing impact, and presents comprehensive strategies for timeliness, data quality, and stability in real‑time data pipelines.
