本报讯(记者付奇)《江苏省制造业领域面向人工智能的数据治理工作参考指引(2026年版)》日前对外发 布。该指引由省工信厅会同国家工业信息安全发展研究中心编制，旨在指导我省制造业企业系统化开展 数据治理工作，用好面向人工智能的数据治理技术和方法。 人工智能应用的核心是依托高质量数据完成模型训练、推理与迭代，数据治理则是保障数据质量的核心 抓手。人工智能应用的深化，反向推动数据治理工作从"被动合规"向"主动价值驱动"升级。 当前，制造业领域，数据"孤岛"与"失真"、数据治理与标准化缺失、数据与应用场景脱节等痛点问题严 重制约了高质量、场景化数据集的供给。省工信厅信息化发展处负责人介绍，新版指引结合了31个人工 智能典型应用场景，面向不同水平的企业，划分数据治理入门、基础、进阶三个等级，为全省大中小企 业典型场景的人工智能应用提供可对标、可参考、可部署的数据治理适配方案。 聚焦数据采集、预处理、特征工程、数据标注、数据划分、数据增强等六大核心环节，《指引》分门别 类地给出了治理路径，制造业企业可结合自身技术基础、资源条件及实际业务痛点，针对性选取适配的 环节落地数据治理技术，最大化挖掘数据价值。比如，在数据采集中， ...

wordmmMwWLliI0fiflO&1mmMwWLliI0fiflO&1mmMwWLliI0fiflO&1mmMwWLliI0fiflO&1mmMwWLliI0fiflO&1mmMwWLliI0fiflO&1mmMwWLliI0fiflO&1 Partnership to Bring Enterprise-Ready AI to the World's Most Trusted Data PlatformNo-Headquarters/BOZEMAN, Mont.--([BUSINESS WIRE])--Snowflake (NYSE: SNOW), the AI Data Cloud company, today announced a new collaboration with OpenAI that enables global enterprises to unlock greater value from their proprietary data with AI. This multi-year, $200 million partnership agreement cements Sno ...

（文/观察者网 张志峰） 从文档工具到AI协同平台，拥有37年文档处理积累的金山办公，转型B端业务的过程中，找到了一条为千行百业数字化转型赋能的新路径。 1月27日，金山办公正式宣布，旗下一站式AI协同办公平台WPS 365构建的"企业大脑"已在华东地区组织级客户中率先落地，在业务效率提升、知识资产盘 活等方面取得多项关键成果，为金融、高端制造、航空等多个行业的数字化转型提供了坚实支撑。 "当前，企业AI应用正经历从'以模型为中心'到'以数据为中心'的关键转变。"金山办公助理总裁朱熠锷强调，数据质量已经成为决定企业AI应用效果的关 键，只有让大模型真正掌握企业知识资产，才能让AI在实际业务场景中发挥实效。 金山办公副总裁吴庆云表示："高质量数据已经成为制约今天AI能不能在现有技术水平下发挥作用的关键制约点，企业级AI建设的首要任务是完成对非结构 化数据的收集和治理。" 针对这些痛点，WPS 365提出了"三步走"实施路径：先通过一站式办公平台特性归集散落的非结构化数据，再依托知识增强生成（KAG）架构将数据治理为 可复用知识，最后将这些知识嵌入办公流、业务流与决策流，形成"企业大脑"的核心能力。 在技术支撑方 ...

Core Insights - Retail trends in 2026 are shaped by structural shifts that accelerated in 2025, driven by economic pressures, changing consumer behavior, and technological advancements [1] Group 1: Artificial Intelligence in Retail - The adoption of artificial intelligence in retail saw a significant increase in 2025, with large retailers moving beyond pilot projects to implement AI for demand forecasting, inventory management, personalized recommendations, and automated customer service [3] - AI tools are expected to become integral to everyday retail operations in 2026, influencing product discovery and cost control through AI-powered shopping assistants, dynamic pricing engines, and predictive supply chain systems [4] - Data governance and ethical use of AI are gaining importance, with regulators indicating tighter oversight of automated decision-making, making transparency around customer data and algorithmic processes a competitive issue [5] Group 2: Omnichannel Retail Strategies - Omnichannel retail has transitioned from a differentiator to a basic requirement for relevance, with consumers expecting a seamless experience across online platforms, mobile apps, and physical stores [6] - Retailers focused on deeper channel integration in 2025, investing in services like click-and-collect tied to store inventory, mobile checkout systems, and digital tools to support in-store staff [7]

Investment Rating - The report emphasizes that the highest return on investment in 2026 will come from modern data infrastructure rather than the latest AI models [11][14]. Core Insights - The gap between AI ambitions and actual operations is widening across industries, with organizations needing to focus on foundational work to achieve transformative wins [6][5]. - Companies are shifting from broad experimentation to specific, high-value use cases, moving AI from proof-of-concept to enterprise-level deployment [19][15]. - Successful organizations are prioritizing data lifecycle management, modernization, and human capabilities to shape their AI-driven transformation strategies [48][59]. Summary by Sections Overview - The report highlights a significant disconnect between organizational expectations of AI and the foundational work required for successful implementation [6][5]. - Many companies are still relying on outdated systems and manual processes, which hinders their ability to leverage AI effectively [6][9]. 2026 AI Trends - AI success in 2026 will be driven by data infrastructure rather than new models, with a focus on creating accessible and real-time data management systems [11][12]. - Organizations are expected to transition from broad AI experiments to targeted applications that deliver measurable business value [15][17]. Industry-Specific Trends - The report outlines predictions for various sectors, including: - Airlines will require rapid experimentation due to competitive pressures [65]. - Retail will see AI operating behind the scenes, influencing pricing and supply chain decisions [66]. - Healthcare will experience regulatory advancements that promote AI-driven innovations [69]. Preparing for 2026 - Companies need to invest in data management and governance to support AI initiatives effectively [48][51]. - A cultural shift is necessary for organizations to embrace AI as a core component of their business strategy rather than a standalone project [30][60]. Conclusion - The foundation laid in data infrastructure and governance will determine the success of AI initiatives in 2026, with companies that prioritize these areas likely to thrive [87][89].

KPMG has announced a partnership with UK company Aiimi to launch a workplace AI platform for data governance across its operations. The collaboration is designed to manage sensitive information and extract insights from the digital estate of KPMG, supporting the firm’s aim to govern data at scale and encourage adoption of AI. The move comes shortly after the Financial Reporting Council’s annual review was published. Aiimi’s platform leverages AI to assist organisations in locating, governing, and unlock ...

As you probably know, AI is already redefining the world economy from financial services to healthcare to automotive, energy, manufacturing, public sector. We are really starting to see great new AI applications come and this is all in partnerships with Nvidia, our great partner who has been pushing us on the envelope of innovation. So what we are talking about here is yes we've been here for many years 27 years in fact but the last 10 years has been amazing in 2015 almost 10 years ago we were supercharging ...

Core Insights - Data governance is often viewed as an ancillary project rather than a core component of data initiatives, leading to questions about its actual value in the short to medium term [1] - The shift from centralized management to a more decentralized approach in data governance has been driven by advancements in low-cost storage, cloud computing, and artificial intelligence [2] - The need for seamless access to accurate data through various means has led to the rapid adoption of data mesh architecture, which emphasizes business ownership over data rather than IT [2] - Successful data governance now requires a shift in strategy for technical project managers, focusing on business initiatives and mapping them to data products [2][3] - The integration of data governance with business objectives is essential for achieving effective outcomes and realizing short-term benefits [3][4] Summary by Sections - **Data Governance Perception**: Traditionally seen as a supplementary function, data governance lacks immediate value perception, leading to its relegation in overall data strategy [1] - **Evolution of Data Governance**: The last decade has seen a transition to decentralized data governance models, driven by technological advancements and the need for diverse data access [2] - **Decentralized Data Mesh**: The data mesh architecture promotes business department ownership of data, enhancing understanding, traceability, and interoperability [2] - **Strategic Shift for Implementation**: Technical project managers are encouraged to adopt a right-to-left approach, prioritizing business stakeholder involvement in data governance initiatives [2][3] - **Business Alignment**: Effective data governance must align closely with business goals to be successful, moving away from fragmented approaches [3][4] - **Implementation Roadmap**: A viable roadmap for building a business-oriented decentralized data governance framework is suggested [5][6] - **Frequent Value Realization**: The focus is on achieving business value and data governance goals frequently in the short term, contrasting with traditional methods that aim for one-time achievements [8]

Core Insights - Cloudera has announced a significant platform update integrating Trino, Cloudera SDX, and Cloudera Octopai to enhance data access, control, governance, and traceability across data assets [1][2] - The update aims to address the challenge of data accessibility for AI projects, with only 9% of IT leaders reporting all data is available and 38% indicating most data is usable for AI [1][2] - Cloudera's platform leverages AI-driven automation to unify data silos and streamline governance processes without the need to move data, thereby improving efficiency and trustworthiness [1][2] Company Overview - Cloudera is positioned as a trusted data and AI platform for large enterprises, enabling them to integrate AI with data stored anywhere [3] - The company differentiates itself by providing a consistent cloud experience through a validated open-source infrastructure, unifying public cloud, on-premises data centers, and edge environments [3] - Cloudera empowers enterprises to harness AI and gain comprehensive control over all forms of data, enhancing security, governance, and real-time predictive insights [3] Product Features - The Cloudera platform automates key data architecture operations such as data quality checks, classification, and profiling, facilitating efficient data management [2][4] - It offers natural language access to enterprise data, allowing both technical and non-technical teams to utilize data equally [2][4] - Cloudera Octopai's data source tracking feature provides end-to-end visibility and credibility for data transformations, including those from outside the Cloudera ecosystem [2][4]

Core Insights - The 2025 Finance and Accounting Technology Expo highlighted the evolving role of CFOs and the finance tech landscape, focusing on modernization, data governance, and AI adoption [1][2][3] Group 1: Technology Vendor Interactions - Decision makers are demanding clearer and more concrete answers from technology vendors regarding data extraction, integration failure rates, and reporting liabilities, indicating a shift towards higher transparency and due diligence in tech stacks [4] - Trust in cloud-based systems is increasingly dependent on the surrounding technology environment, with organizations favoring a single system of record to minimize complications from multiple tools [5] Group 2: ERP Implementation and Legacy Systems - A recurring theme in discussions was the integration of new technologies with existing systems like NetSuite, reflecting a reliance on legacy vendors for flexibility in finance functions [6]