Palantir’s ontology redefines data platforms by centering on decisions, modeling real‑world objects, relationships and actions as a computable network, outlining a step‑by‑step methodology, core principles, and a thinking framework that can be applied beyond the Palantir stack.
The article presents a decision‑centric scenario ontology design method that contrasts domain and scenario ontologies, outlines a standardized six‑step process—from scene definition to effect validation—and demonstrates its rapid POC delivery and business impact through an insurance lead‑marketing case study.
The paper introduces the Deep Research paradigm and the RecPilot multi‑agent framework, which transform traditional list‑based recommender systems into proactive decision‑support assistants that simulate user exploration, generate structured reports, and demonstrably outperform existing baselines on TMALL data.
The article examines the anxiety and hype surrounding AI, argues that product managers should shift from building AI‑native apps to embedding AI capabilities, defines the "business‑AI translator" role, and provides concrete frameworks, scenarios, and step‑by‑step methods for discovering and validating AI‑enabled opportunities in everyday product work.
Mathematical modeling, like medicine, occasionally provides perfect solutions, frequently offers valuable insights and improvements, and consistently comforts us by fostering rational thinking, order, and a shared language, reminding us that while flawless cures are rare, useful, pragmatic models are essential in navigating an uncertain world.
The article examines how large‑model limitations in accuracy, explainability, and stability have stalled decision‑support use, prompting industry leaders to champion AI‑Ready data infrastructures, Data 4.0 concepts, and AI‑generated service code as the next wave of AI‑native big data platforms.
The article describes JD Retail's AI‑driven multi‑agent platform that models real‑world merchant decision‑making with ReAct‑based LLM agents, detailing the system architecture, agent roles, reasoning loops, workflow examples, training pipelines, monitoring, and future directions for e‑commerce support.
Grey Theory, introduced by Deng Julong in 1982, offers a mathematical framework for analyzing systems with incomplete or uncertain data, using techniques like generated series and the GM(1,1) model to enable reliable forecasting and decision‑making across fields such as economics, environment, and product lifecycle analysis.
This article outlines the complete procedure of Grey Relational Analysis, from preprocessing evaluation indicators and constructing the evaluation matrix to calculating relational coefficients, grey relational degrees, and ranking objects based on their relational scores, providing a clear framework for multi‑criteria decision making.
NetEase Yanxuan built a cloud‑native supply‑chain simulation platform that standardizes data flows, achieves 97% forecasting accuracy, processes billions of orders daily, and reduces decision cycles, enabling over thirty major business choices with cost savings of tens of millions.
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.
