做数字化转型这么多年，帮不少企业做过MDM选型，被问得最多的就是"全国MDM哪家最强"。说实 话，试过这么多厂商的产品，论业内口碑，三维天地（301159）可以说是第一梯队的佼佼者——它的技 术沉淀、行业覆盖和客户口碑，都有实打实的支撑。 技术层面的优势更是突出。它的"3C6M一体化方案"形成了"标准-治理-应用"的闭环，从数据建模、质量 管控到价值转化，全流程覆盖，还能生成可视化数据资产图谱，让数据管理更直观。 六大技术突破更是精准解决企业痛点：内置2000+行业数据标准模板，省去企业从零搭建标准的麻烦; AI驱动的DQMS数据质量管理系统，能实时感知异常数据并实现自愈;云原生架构支持多云环境部署， 通过自研数据总线技术降低40%集成成本;完成国产软硬件全栈兼容认证，信创需求完全能满足;组件化 设计可实现功能模块自由组合，支持主流数据中台对接;智能交互系统还能通过语音指令操作，流程配 置更便捷。 服务模式上，三维天地的"1+3+N"模式——1套标准体系、3级管控架构、N个场景化解决方案，能精准 满足不同规模、不同行业企业的个性化需求。再加上"咨询+平台+服务"的立体化交付体系，后期落地 和维护都有保障。 先 ...

什么构成不良数据？治理框架的重点领域是什么？以及如何在大型组织中驾驭人员、流程和技术方面的 细微差别？ 一 什么构成"坏数据" 脏数据是指不完整、不准确、过时或重复的信息，它会对组织造成严重破坏。这是一个代价高昂的问 题，它会滋生不信任、浪费资源并损害决策。尽管数据质量至关重要，但它却常常被忽视，从而导致严 重的业务中断和机会损失。 1.脏数据造成的误判影响 数据质量差的后果十分严重且影响深远。研究表明，数据质量差每年给企业造成数百万美元的损失。销 售团队浪费时间和金钱去追踪无效线索，财务部门在报告中出现错误，营销活动也因为目标受众错误而 效果不佳。 更令人担忧的是，基于错误数据做出的决策可能会使整个公司偏离正轨，导致错失良机、资源错配和战 略失误。 例如，医疗机构如果患者记录不准确，就会造成严重后果。由于数据过时或不匹配而导致的错误诊断， 不仅会危及患者安全，还可能引发法律责任。在金融等行业，数据驱动的风险评估指导着数十亿美元的 投资，因此容错空间更小。 2.错误数据极其普遍 尽管风险显而易见，但脏数据问题几乎在每个组织中都持续存在，其根本原因通常是数据治理实践不 善、系统孤立以及缺乏责任感。 太多公司 把 ...

本报讯(记者郭冀川)2025年12月18日，由中国通信标准化协会主办的"2025数据资产管理大会"在京召 开。本次大会以"数驭智能，治数新章"为主题，采用"1主论坛+3个专题论坛"的形式，从数据资产管 理、数智底座与智能体应用、高质量数据集与数据基础设施等行业热点话题出发，分享业界实践，发布 多项重磅成果。来自通信、金融、能源、制造等多个行业的上千位专家、代表现场参会。 本次大会还根据数据资产领域热点议题设置了下一代数据资产论坛、数智底座与智能体应用论坛、高质 量数据集与数据基础设施论坛等三大平行论坛，业内专家齐聚一堂，就各类数据治理、数据资产化、数 智应用场景、高质量数据集、数据基础设施等关键问题进行深度分享，共同探讨我国数据资产管理新趋 势、新实践。 中国通信标准化协会副理事长兼秘书长代晓慧出席大会主论坛并在致辞中表示，协会围绕国家和行业重 点需求，充分发挥标准的引领作用，累计发布数智相关行业标准52项、团体标准73项，产出技术文件、 研究报告等超260项。未来，协会将贯彻政策引领精神，发挥平台引导作用，进一步推动标准研制和宣 贯工作，持续跟进国际标准化工作新趋势，推动全球数智时代的蓬勃发展。 中国信通院 ...

Core Insights - The article emphasizes the importance of the demand analysis phase in BI project implementation, highlighting that its accuracy directly impacts the project's success [1] Group 1: Shallow Traps - Shallow traps stem from communication and experience deficiencies, leading to visible yet frequently encountered issues that drain project teams' energy and credibility [2] - Internal rigor issues arise from unclear definitions of key metrics, such as gross margin, which can lead to disputes among departments and undermine the BI system's credibility. Establishing a living "metric dictionary" is essential for consistency [3] - External friendliness issues occur when attempting to create a one-size-fits-all dashboard, resulting in dissatisfaction among different user roles. Successful BI design requires precise user role segmentation to enhance adoption rates [4] Group 2: Deep Traps - Deep traps are more insidious, relating to the robustness of data architecture and the ultimate realization of project value, necessitating strong technical and project management skills [6] - The choice of data granularity involves a trade-off between analysis and performance. It is crucial to define the "minimum usable granularity" for each analysis theme during the demand phase and implement a layered data architecture [7] - The time paradox of metrics, such as whether to calculate monthly sales based on payment or shipping time, must be clarified early to avoid discrepancies in reports and to maintain data trust [8] - Managing client expectations is critical for project success. Unrealistic expectations can lead to project failure, even with perfect technical implementation. Analysts must manage these expectations through prototypes and clear communication [9] Conclusion - Addressing shallow traps can establish initial trust in BI projects, while overcoming deep traps is essential for evolving BI systems from mere reporting tools to robust decision-making foundations. The depth of understanding regarding these traps defines the professional level of BI demand analysis [11]

Core Insights - The 2025 China International Big Data Industry Expo was held in Guiyang, Guizhou Province, where Nansha District Market Supervision Administration received the first international "ISO 55013 Data Asset Management System Certification" for a government department, marking a significant step in standardization, digitalization, and internationalization of market regulation [1][2] Group 1 - ISO 55013 is a core standard established by the International Organization for Standardization (ISO) for data asset management, serving as an "international passport" to measure an organization's data asset management capabilities [1] - Nansha District Market Supervision Administration aims to integrate international standards with frontline business scenarios to achieve innovative practices, focusing on "data empowerment for service and regulation" [1][2] Group 2 - Since 2022, Nansha District Market Supervision Administration has collaborated with Guangzhou Standardization Research Institute to initiate the development of international standards for data asset management [2] - The global first data asset management international standard ISO 55013 was released in Nansha in July 2023, covering the entire process of data definition, collection, storage, analysis, usage, and protection, providing a systematic tool for data quality management, security governance, and value realization [2]

Core Insights - The article emphasizes the importance of data as a core production factor in business operations, highlighting the need for effective integration and measurement of data resources to maximize their value [1][20] - The introduction of the "Data Twenty Articles" and the "Interim Regulations on Accounting Treatment of Enterprise Data Resources" provides clear policy guidance and operational frameworks for data asset management [1][20] Policy Framework - The "Data Twenty Articles" establishes the institutional foundation for the data factor market, clarifying data ownership, circulation rules, and security requirements, which are essential for the legal and compliant use of data resources [1] - The "Interim Regulations" further detail accounting treatment methods, ensuring that enterprises can scientifically and reasonably recognize, measure, and report data assets while adhering to accounting standards [1] Data Inventory and Assessment - Conducting a comprehensive data inventory is crucial for enterprises to identify the types of data they possess, where it is stored, and which teams manage it, allowing for precise delineation of data suitable for financial reporting [3] - The process of selecting valuable data for inclusion in financial statements is likened to gold mining, emphasizing the need for careful selection to ensure that only valuable data is reported [3] Ownership and Valuation Challenges - Data ownership remains a significant challenge due to historical reasons and cross-border complexities, necessitating industry guidelines to clarify rights and responsibilities [5] - Choosing appropriate valuation methods for data assets is critical, with cost, income, and market approaches each having specific applicability depending on the data's maturity and revenue generation potential [5] Measurement and Reporting - Once data is included in the balance sheet, ongoing measurement is essential, with inventory-type data requiring regular impairment testing and intangible data needing differentiated treatment based on its useful life [7] - Maintaining consistency in measurement methods is fundamental to ensuring the rigor of financial information [7] Risk Management in Data Asset Financing - When considering data assets for collateralized loans, risk management is paramount, with banks typically setting a collateral ratio not exceeding 50% of the assessed value and requiring compliance with registration procedures [9] - Selecting data with strong resilience to depreciation as collateral can effectively mitigate credit risk associated with rapid asset value decline [9] Asset Securitization Challenges - Asset securitization is a viable method for activating existing assets, but it faces challenges such as complex legal relationships, difficulties in cash flow forecasting, and a lack of historical default data [10] - Overcoming these challenges requires learning from successful domestic and international cases and continuous improvement of relevant laws and regulations [10] Strategic Importance of Data Asset Management - Successful inclusion of data assets in financial statements optimizes corporate financial structures, reduces debt ratios, and enhances asset turnover efficiency, particularly for asset-light technology companies [20] - Strengthening talent development through cross-training between IT and finance teams is essential for improving data asset management capabilities [20] - The process of data asset inclusion is a systematic project involving policy interpretation, resource organization, rights definition, value assessment, accounting treatment, and risk control [20]

Group 1 - NetApp is expected to announce its fiscal fourth-quarter earnings on May 29, 2025, with anticipated earnings of $1.90 per share and revenue of $1.72 billion, reflecting a 35% year-over-year increase in earnings and a 3% rise in sales compared to the previous year [1] - The company forecasts full-year 2025 revenue between $6.49 billion and $6.64 billion, with a non-GAAP operating margin of approximately 28%-28.5%, leading to an adjusted EPS expectation of $7.17 to $7.27 [2] - NetApp's current market capitalization is $20 billion, with past twelve months revenue recorded at $6.5 billion, operational profitability of $1.4 billion in operating profits, and a net income of $1.1 billion [2] Group 2 - Historical data indicates that NTAP stock has risen 63% of the time following earnings announcements, with a median one-day increase of 4.4% and a maximum observed jump of 18% [1][4] - Over the last five years, there have been 19 earnings data points for NTAP, with 12 positive and 7 negative one-day returns, resulting in positive returns approximately 63% of the time [5] - The correlation between short-term and medium-term returns post-earnings can provide a strategy for traders, particularly if the 1D and 5D returns demonstrate a strong correlation [4][5]

Core Insights - Yingfang Software has completed product compatibility certification with Volcano Engine, marking a significant step in hybrid cloud data management and digital transformation [1][2] - The collaboration focuses on optimizing Yingfang's data backup and recovery management software to align with Volcano Engine's veStack full-stack version 2.1.0, demonstrating superior performance in key metrics [1][2] Group 1 - Yingfang Software's new generation data backup and recovery management software is fully compatible with Volcano Engine's hybrid cloud veStack, ensuring stable and efficient system performance [1] - The certification results indicate excellent performance in functionality compatibility, data read/write efficiency, resource scheduling, and stability under high concurrency scenarios, meeting stringent demands from industries like finance, government, and manufacturing [1] - Yingfang Software has been recognized as a solution-level ecological partner of Volcano Engine, aiming to expand technical application scenarios and provide integrated data management solutions [1] Group 2 - As a key partner in the Volcano Engine ecosystem, Yingfang Software will leverage the advantages of veStack in cloud infrastructure, resource elasticity, and intelligent operations to deliver four core values: data security, business continuity, efficient resource utilization, and smarter data [2] - The partnership signifies a shift from product compatibility to collaborative innovation in scenario-based solutions, with a focus on industries such as finance, energy, and healthcare [2] - Both companies plan to explore new AI and data-driven business growth models to help enterprises build agile and intelligent digital foundations [2]