This article explores Kuashou's e‑commerce data metric system, detailing why a metric framework is essential, the business context, the challenges faced across data production, querying and usage, and the practical solutions implemented to improve decision‑making and operational efficiency.
The article examines Kuaishou’s e‑commerce data metric system, detailing why a metric framework is essential, how it was built, the product practice, management methods, and the challenges faced by data product managers, engineers, and operators across production, querying, and usage stages.
This article examines Kuaishou's e‑commerce data metric system, explaining why such a framework is essential, how it was constructed, the practical product implementation, management practices, and lessons learned, while also addressing common data production, query, and usage challenges faced by product and engineering teams.
This article examines a real‑world case where a fresh‑food e‑commerce platform attempts to fuse business metrics and data tags to streamline monthly product selection, analyzes the shortcomings of the initial solution, and proposes two improved approaches—custom tag rules and algorithmic modeling—to enhance decision‑making efficiency.
This article explains search experiments from a data‑product viewpoint, covering AB testing fundamentals, multi‑layer experiment architecture, four experiment types (ordinary AB, vocabulary, diff‑AB, interleaving), real‑world case studies, and a comprehensive FAQ addressing typical challenges and troubleshooting methods.
This presentation details the design, construction, and iteration of a management dashboard ("cockpit") for Kuaishou's e‑commerce data product team, covering data‑driven business logic, indicator design, product and interaction design, stability assurance, and common challenges with solutions, all aimed at improving business insight and decision‑making.
This article shares the design, construction, and iteration practices of a management cockpit for metric systems within Kuaishou's e‑commerce data product team, covering data‑driven business logic, product architecture, indicator design, visualization, stability assurance, and common challenges to guide effective business decision‑making.
The presentation by Tencent PCG data product manager Wang Dongxing introduces A/B testing fundamentals and shares practical experiences with various search experiment methods—including regular A/B, vocabulary, diffAB, and interleaving—while highlighting common pitfalls and offering actionable insights for practitioners.
This article explains why traditional centralized data lakes hinder modern software development, introduces the data‑mesh concept as a decentralized alternative, and walks through an e‑commerce microservice example with concrete steps, data‑API designs, and migration tactics to transition from a monolithic lake to a distributed data mesh.
The guide explains how to create an analytical data product by first defining the business scenario and KPI, selecting and abstracting an analysis framework into reusable modules, visualizing core metrics across dimensions, and continuously iterating through cold‑start, promotion, and maintenance phases to keep the product aligned with evolving business needs.
This article explains why analyzing an organization’s structure, collaboration patterns, and risks is crucial for data product success, outlines an ideal data‑centric organization model, shares real‑world examples and practical steps, and provides a Q&A on common challenges faced by product teams.
The article presents Huya's data‑self‑service product, describing the problems of traditional data services, the principles of a good data service, the MVP implementation, architectural components, project outcomes, and future evolution, while also addressing common Q&A scenarios.
This article explains why data product value assessment is essential, outlines common usage scenarios and a DBA evaluation framework, describes quantitative methods such as usage, business, and data‑driven metrics, and offers practical ways to enhance data product value through metric optimization, high‑value direction selection, and resource allocation.
This article presents a comprehensive overview of business diagnostic data products, detailing their value chain, architectural layers, thinking model, product growth stages, and the practical challenges faced when scaling from inception to mature deployment in retail environments.
This article shares practical insights on constructing an O2O industry data platform, detailing user classification, business pain points, and a three‑step strategy—monitoring, analysis, and diagnosis—to extract core metrics, implement tailored reporting, conduct operational and pricing analyses, and drive data‑driven product improvements.
The article examines why traditional centralized data lake architectures fail at scale, outlines common failure patterns, and proposes a paradigm shift toward a distributed data mesh that treats domain‑oriented data as a product, leverages self‑service platforms, and aligns engineering, product, and governance practices for modern enterprise data platforms.
This article explores how enterprises can overcome common data‑analysis frustrations, explains why quantitative data is essential for effective management, outlines methods to boost data‑driven management efficiency through analysis and productization, and presents a detailed second‑hand car platform case study illustrating these concepts in practice.
The article explains why data maturity is essential for modern enterprises, defines its three pillars—people, tools, and readiness—shows how treating data as a product follows the same principles as great products, and outlines the four S (Speed, Scale, Simplicity, SQL) that guide a mature data ecosystem.
This article explains how data has become a critical production resource, outlines the limitations of traditional data‑analysis workflows, defines data products and their components, describes their advantages and key characteristics, and shares practical case studies of data‑product implementations in a large e‑commerce environment.
The article details NetEase Yanxuan's end‑to‑end data product ecosystem and data middle platform, describing four core data products, the architecture of the data middle platform, efficient high‑quality delivery, governance practices, and key performance metrics that support data‑driven decision making.
This article details NetEase Yanxuan's business background, market characteristics, data product requirements, and the end‑to‑end design of a full‑chain marketing data product, covering attribution, metric evaluation, analysis frameworks, scenario‑based recommendations, and practical Q&A for data‑driven growth.
This article details NetEase Yanxuan's three‑year journey of constructing a comprehensive data product ecosystem—covering business‑visible metrics, data quality safeguards, CXO‑focused mobile dashboards, and scenario‑driven visualizations—while sharing the challenges faced and the solutions implemented.
This article details NetEase Yanxuan's four‑stage journey—from establishing a business‑centric BI platform to ensuring data quality, empowering CXOs with mobile dashboards, and delivering scenario‑specific data products—highlighting the challenges faced, technical solutions implemented, and key takeaways for building enterprise data products.
Effective data product development hinges on deep business understanding, clear metric decomposition, rigorous model evaluation, and translating technical performance into business impact, while algorithm engineers are best assessed by publication quality, problem significance, algorithmic contribution, and practical interview questions on model tuning and improvement.
This article explains the concept of user profiling, outlines an eight‑dimensional tag architecture, describes step‑by‑step methods for constructing a robust profile system from scratch and expanding it, and shows how these profiles support statistical analysis, targeted marketing, and recommendation algorithms.
The article explains how data products create business value through application, data, and algorithm layers, using examples like 5G infrared temperature screening and ImageNet, and discusses the roles of experimental design, causal inference, and reinforcement learning in building effective AI‑driven strategies.
The article explains the concept of a data middle platform, why it matters for rapid demand response and resource integration, and outlines the distinct responsibilities and required skill sets of data product managers and data platform product managers within such ecosystems.
The article examines how data middle platforms transform raw, fragmented enterprise data into valuable data products through a supply‑chain approach, outlining their origins, core processes, deep‑processing techniques, and the essential capabilities needed for successful implementation.
This article examines the gap between raw data analysis and actionable insight, describes the ConnectedInsight project that turns scattered reporting into a unified data‑product covering business description, diagnosis, forecasting and decision support, and demonstrates how productizing analytics drives efficient, data‑driven operations.
