IBM’s 2025 Chief Data Officer study, based on surveys of over 1,700 CDOs across 19 industries, shows that the real AI advantage lies in delivering decision‑ready data, breaking data silos, and leveraging proprietary data as a competitive moat, while highlighting a shift in the CDO’s strategic role.
The article outlines a six‑step framework for turning an AI idea into a marketable product, covering goal definition, metric selection, data acquisition, model design, MVP creation, and growth‑loop engineering, while emphasizing practical trade‑offs and real‑world examples.
This article outlines a comprehensive data strategy framework, explaining how business and data discovery intersect, the pressures and values driving data initiatives, and the essential questions leaders must ask to align data management, analytics, and business outcomes.
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.
The 2024 Inclusion·Bund conference brought together academia and industry leaders to discuss how data technologies are evolving and aligning with AI, covering trends in large‑model storage, synthetic data generation, AI‑enhanced databases, and Ant Group's emerging AI‑centric data ecosystem.
The 2024 Inclusion·Bund Conference forum brought together leading academics and industry experts to examine how data value is shifting in the AI era, covering large‑model storage challenges, the rise of synthetic data, AI‑enhanced databases, and Ant Group’s next‑generation intelligent data architecture.
The article explains the Chief Data Officer (CDO) role, its evolution, key responsibilities, compensation ranges, reporting structures, required skills, and how organizations across industries are adopting the position to drive data‑centric business value.
This article presents a comprehensive overview of Taobao Live's (DianTao) user growth practice, detailing business background, industry challenges, data‑driven acquisition and retention strategies, lifecycle segmentation, and concrete capability designs to boost acquisition, activation, and recall in a competitive e‑commerce live‑streaming market.
The article presents a comprehensive case study of Taobao Live’s DianTao app, detailing business background, industry challenges, and a multi‑stage user growth framework that includes data‑driven strategies, lifecycle data systems, and specific capabilities for acquisition, retention, activation, and recall.
This article outlines a complete data governance framework, detailing the five managerial domains—control, process, governance, technology, and value—along with strategies for data strategy, organizational structure, policies, processes, standards, quality, security, and platform tools, and highlights AI’s pivotal role in enhancing governance efficiency.
Lean Data Methodology combines lean thinking, design thinking, the Cynefin framework, and agile principles to create a data‑driven digital transformation system that defines value, eliminates waste, and equips enterprises with strategic, product, governance, collaboration, platform, and cultural capabilities for building lean digital enterprises.
The article explains how a well‑defined technical roadmap, encompassing architecture, data governance, storage, analytics, and automation, aligns tools and technologies with business goals to create a results‑driven enterprise data strategy and avoid common pitfalls.
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 explores how a clear enterprise data strategy—distinguishing strategic goals from tactical steps, emphasizing clean and governed data, and integrating analytics with business missions—drives reliable outcomes and supports the experience economy through coordinated CXM platforms and data products.
The article explains how a well‑defined technology roadmap, encompassing architecture, data governance, storage, analytics, and automation, is essential for aligning tools and techniques with business goals to achieve a successful, outcome‑driven enterprise data strategy.
The article explores the rapid rise of data middle platforms in China, tracing their historical roots, explaining their core purpose of unifying data across legacy and new systems, showcasing Shulan Technology’s real‑world implementations, and analyzing market dynamics and future opportunities for enterprises and startups alike.
The article outlines a comprehensive data‑strategy framework for executives, highlighting business pressures, value, management, and data‑management considerations, and provides key questions to guide data discovery, business discovery, and the continual evolution of a practical, actionable data strategy.
The article outlines eight key principles that enterprises should follow to harness big data responsibly, covering goal definition, strategic partnership, source identification, continuous communication, agile iteration, technology evaluation, cloud alignment, and talent development with security considerations.
The article explains the strategic importance of the Chief Data Officer role, outlining how CDOs drive data‑driven innovation through a four‑stage data supply chain—data supply, logistics, science, and execution—to create competitive advantage and business growth.
The article examines whether all enterprises should establish dedicated big‑data departments, weighing the hype, actual data needs, cost considerations, and expert viewpoints, and concludes that small firms are better off leveraging open‑source tools or outsourcing rather than building costly in‑house teams.
A recent Dresner survey reveals that while most enterprises claim big data is critical, only a minority actually deploy it, and many are already using big‑data techniques without labeling them as such, highlighting a gap between perception and practice.
