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国联股份入选数博会发布“企业数据要素竞争力报告”:以产业级数据底座推进产业数智化

亿邦智库黄斌 2026-09-09 13:25
亿邦智库黄斌 2026/09/09 13:25

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本文核心介绍了国联股份作为产业互联网标杆,入选2026数博会发布的首份《2026企业数据要素竞争力报告》的相关信息,同时整理了国联股份建设数据要素竞争力的实操干货,具体如下:

1. 核心基础信息:本次发布的报告历时近一年调研近百家企业,提出了企业数据要素竞争力的概念,构建了“获、治、用、安”四力评价模型,国联凭借二十余年的产业数据实践成为大型产业互联网平台的标杆案例。国联很早就将数据要素作为核心战略支撑,提出“平台、科技、数据”三位一体战略,认为数据是业务运转的底层燃料,而非仅用来挂牌售卖的表层资源。

2. 实操落地路径:国联从数据获取、治理、应用三个维度搭建核心能力,经过二十余年积累形成超200TB覆盖百余个细分行业的工业大数据,通过三级组织架构推进规范数据治理,结合AI打造多多智工产业智能平台,在多个生产运营场景落地,实现了显著的降本提效。

本文为产业领域品牌商展示了数智化转型的方向,提供了数据要素建设的标杆实践干货,可指导品牌布局核心竞争力,具体如下:

1. 产业消费与发展趋势:当前产业已经进入数据要素驱动数智化转型的阶段,数据是品牌未来发展的核心底层支撑,品牌商需要尽早将数据要素建设纳入核心战略布局,挖掘数据对业务的支撑价值,而非仅将数据作为可售卖的表层资源。

2. 可借鉴的实践经验:数据获取层面,可沿着业务发展逐步积累核心商情和交易数据,形成自有数据体系后对接官方大数据交易所完成数据确权和资产化,合规开发数据产品;数据治理层面,可搭建前中后台三级分工架构,配套专门的治理机制保障数据安全和客户隐私;数据应用层面,结合AI技术落地到价格监测、生产调度、供需匹配等场景,可有效实现降本提效,强化品牌竞争力。

本文为To B产业领域的卖家提供了数智化转型的机会参考和可落地的实践经验,核心干货如下:

1. 行业增长机会:当前数据要素和AI结合是产业领域的重要增长赛道,卖家可通过积累自身运营过程中的产业数据,对接正规大数据交易所实现数据资产化,挖掘除传统交易之外的新增长曲线。

2. 可借鉴的落地路径:数据端可沿着业务发展逐步积累,从早期的商情数据到后续的交易数据,逐步形成稳定的自有数据体系;组织层面调整架构适配数据开发,提前建立数据安全合规的治理机制;应用层面结合AI落地供需匹配、生产调度、产业链风险预警等场景,直接提升自身运营效率。

3. 效果参考:国联的实践显示,应用数据+AI模式后,接入企业运营采购成本平均降低5%,订单履约周期缩短20%,物流效率提升30%,生产效率提高15%至30%,降本提效效果明确,值得卖家尝试转型。

本文给传统制造工厂推进数字化转型、对接产业互联网提供了明确的启示和商业机会,核心干货如下:

1. 可对接的商业合作机会:国联股份作为头部产业互联网平台,目前已经开放云工厂、数字工厂合作,截至2025年底已经和290家云工厂达成合作,其中约92家为深度数字化改造的数字工厂,有转型需求的工厂可对接这类平台获得成熟的数字化改造支持。

2. 数字化转型的启示:工厂不用盲目追求自主搭建全链条数据能力,可依托产业平台的“数据+AI”能力,优化自身生产、供应链管理环节,实现生产管理从传统的“事后报警”到“事前干预”的升级,还可以通过多个智能体协同优化全局生产目标,提升整体运营水平。

3. 转型价值参考:接入平台成熟的数字化方案后,工厂可实现采购成本降低、履约周期缩短、物流和生产效率提升,平均生产效率可提高15%-30%,降本提效效果明确。

本文给产业数字服务商指明了行业发展趋势,提供了可借鉴的数据要素服务解决方案,核心干货如下:

1. 行业发展趋势:当前产业互联网正从传统的交易“连接器”向产业数字科技服务商转型,数据要素是产业数智化的核心底层支撑,“数据+AI”是未来产业服务商的核心竞争力方向,市场对产业级数据应用的需求正在快速增长,行业空间广阔。

2. 客户核心痛点:传统产业客户的普遍痛点是数据积累零散、治理不规范,数据无法有效支撑生产运营决策,亟需完整的数据要素全链条服务帮助其完成数字化升级。

3. 可借鉴的完整解决方案:数据端逐步积累“商情数据+交易数据”双轮数据体系,对接官方大数据交易所完成确权和资产化;治理端搭建前中后台三级组织分工体系,配套专门治理架构保障数据安全和隐私;应用端围绕“算力-算法-数据”搭建产业级智能平台,落地多场景应用,形成全链条闭环服务能力。

本文给产业互联网平台型企业推进数据要素建设、提升核心竞争力提供了成熟的实践参考,核心干货如下:

1. 当前产业平台的核心需求:当前产业平台的核心发展方向是将数据从资源转化为资产再转化为核心竞争力,构建可持续的数智化增长能力,适应产业升级的需求。

2. 可借鉴的平台运营与建设做法:战略层面,需要在发展早期就将数据要素放到核心战略底层,明确“平台、科技、数据”三位一体的核心战略体系;数据能力建设层面,分获取、治理、应用三个环节逐步推进:获取端长期积累核心交易数据,对接正规大数据交易所完成数据确权资产化,合规挂牌数据产品;治理端搭建前中后台三级组织架构,配套专门治理体系分工推进。

3. 风险规避要点:要提前建立数据安全和用户隐私保护的治理机制,将数据安全纳入核心治理议题,合规开展数据要素开发应用,避免合规风险。

本文给产业互联网和数据要素领域的研究者提供了标杆案例和产业新动向的一手信息,核心干货如下:

1. 产业最新动向:当前数据要素已经成为产业数智化的核心竞争力,头部产业互联网平台正处于从传统产业电商向产业数字科技平台的关键转型期,头部企业已经完成数据从采集、治理到产品化、交易化的全链条闭环布局,数据要素对产业协同的乘数效应正在逐步释放。

2. 值得研究的商业模式创新:国联股份探索出了一套区别于传统数据售卖的新商业模式,不将数据作为表层挂牌售卖的资源,而是将数据作为一切业务运转的底层燃料,通过“数据+AI”为产业提供智能技术服务,将数据依次转化为资源、资产、竞争力,形成了完整的闭环商业模式,为整个行业提供了可参照的范本。

3. 研究基础参考:当前业界已经发布了首份企业数据要素竞争力报告,提出了“获、治、用、安”四力评价模型,为后续相关领域研究提供了成熟的分析框架。

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Quick Summary

This article introduces that Guolian Shares, a benchmark in industrial internet, was featured in the first *2026 Enterprise Data Factor Competitiveness Report* released at the 2026 China International Big Data Industry Expo. It also shares actionable insights from Guolian Shares' practices in building data factor competitiveness as below:

1. Core background: The report was compiled after nearly one year of research on nearly 100 enterprises. It proposes the concept of enterprise data factor competitiveness and develops a four-dimensional evaluation framework covering "acquisition, governance, application and security". With over 20 years of industrial data practice, Guolian Shares stands out as a benchmark case for large-scale industrial internet platforms. The company positioned data factors as core strategic support early on, with a three-in-one "platform, technology, data" strategy, and views data as the underlying fuel for business operations rather than just a superficial asset for direct sale.

2. Implementation framework: Guolian Shares has built core capabilities across three dimensions: data acquisition, governance and application. After two decades of accumulation, it holds over 200TB of industrial big data covering more than 100 sub-sectors. It implements standardized data governance through a three-tier organizational structure, and has developed the Duoduo Smart Industry intelligent industrial platform powered by AI, which has been deployed in multiple production and operation scenarios to deliver significant cost reduction and efficiency improvement.

This article outlines directions for digital transformation for industrial brands and shares benchmark practices for building data factor competitiveness, to guide brands in developing core competitive strengths as below:

1. Industrial trends: The sector has entered a phase of data factor-driven digital transformation, where data serves as the core underlying support for future brand growth. Brands should integrate data factor development into core strategic planning as early as possible, and unlock data's business support value, rather than treating data as just a superficial tradable asset.

2. Actionable best practices: For data acquisition, brands can gradually accumulate core business intelligence and transaction data along with business growth, then connect to official big data exchanges for data confirmation and assetization after building an in-house data system, to develop data products in compliance with regulations. For data governance, brands can build a three-tier division of labor across front, middle and back offices, with dedicated governance mechanisms to protect data security and customer privacy. For data application, integrating AI technology into use cases such as price monitoring, production scheduling and supply-demand matching delivers tangible cost reduction, efficiency improvement and stronger brand competitiveness.

This article offers digital transformation opportunities and actionable insights for B2B industrial sellers, with key takeaways as below:

1. Industry growth opportunities: The combination of data factors and AI is currently a major high-growth track in the industrial sector. Sellers can accumulate industrial data generated in daily operations, connect to regulated big data exchanges to realize data assetization, and unlock new growth curves beyond traditional transaction revenue.

2. Actionable implementation roadmap: For data development, sellers can accumulate data gradually along business expansion, building a stable in-house data system starting from business intelligence data, then expanding to transaction data. For organization, adjust internal structures to support data development and establish data security and compliance governance mechanisms in advance. For application, integrate AI into scenarios including supply-demand matching, production scheduling and industrial chain risk early warning to directly improve operational efficiency.

3. Outcome benchmarking: Guolian Shares' practice shows that after adopting the "data + AI" model, partnered enterprises see an average 5% reduction in procurement costs, a 20% shortening of order fulfillment cycles, a 30% improvement in logistics efficiency, and a 15% to 30% increase in production efficiency, with clear cost and efficiency benefits that make this transformation worthwhile.

This article offers clear insights and business opportunities for traditional manufacturing factories pursuing digital transformation and industrial internet connectivity, with key takeaways as below:

1. Accessible partnership opportunities: As a leading industrial internet platform, Guolian Shares has opened cloud factory and digital factory cooperation programs. As of the end of 2025, it has partnered with 290 cloud factories, of which around 92 are digital factories with in-depth digital transformation. Factories with transformation needs can partner with such platforms to access mature digital transformation support.

2. Transformation insights: Factories do not need to pursue independent end-to-end data capability building blindly. They can leverage industrial platforms' "data + AI" capabilities to optimize their own production and supply chain management, upgrade production management from traditional "post-incident alerting" to proactive "pre-incident intervention", and optimize overall production goals through multi-agent collaboration to improve overall operational performance.

3. Transformation value: After accessing the platform's mature digital solutions, factories can reduce procurement costs, shorten fulfillment cycles, and improve logistics and production efficiency, with an average 15%-30% increase in production efficiency and clear cost reduction and efficiency improvement outcomes.

This article outlines industry development trends for industrial digital service providers and shares a referenceable data factor service solution, with key takeaways as below:

1. Industry trends: The industrial internet is currently shifting from a traditional transaction "connector" to industrial digital technology service provider. Data factors are the core underlying support for industrial digitalization, and "data + AI" is the core competitive direction for future industrial service providers. Market demand for industrial-grade data applications is growing rapidly, creating broad industry space.

2. Core customer pain points: Most traditional industrial clients face common pain points: scattered data accumulation and non-standard governance leave data unable to effectively support production and operation decision-making, creating strong demand for full-stack data factor services to support their digital upgrade.

3. Referenceable full-stack solution: For data development, gradually build a dual-wheel data system combining "business intelligence data + transaction data", then connect to official big data exchanges for data confirmation and assetization. For governance, build a three-tier division of labor across front, middle and back offices, with a dedicated governance architecture to protect data security and privacy. For application, build an industrial-grade intelligent platform around "computing power - algorithms - data", deploy the solution across multiple scenarios, and build closed-loop full-stack service capabilities.

This article offers mature practical references for industrial internet platform enterprises to build data factor competitiveness and improve core strengths, with key takeaways as below:

1. Core demand for industrial platforms today: The core development direction for current industrial platforms is to convert data from a resource to an asset, then to a core competitiveness, build sustainable digital-led growth capabilities, and adapt to industrial upgrading demands.

2. Referenceable platform operation and development practices: At the strategic level, data factors should be positioned as core underlying strategy from the early stage of development, with a clear three-in-one core strategic system of "platform, technology, data". For data capability building, progress step-by-step across three stages: acquisition, governance and application. For acquisition, accumulate core transaction data over the long term, connect to regulated big data exchanges for data confirmation and assetization, and list data products in compliance. For governance, build a three-tier organizational structure across front, middle and back offices, with a dedicated governance system for division of labor.

3. Risk mitigation: Enterprises should establish governance mechanisms for data security and user privacy protection in advance, integrate data security into core governance agendas, develop and apply data factors in compliance, and avoid regulatory risks.

This article offers benchmark case studies and first-hand insights on new industry trends for researchers focused on industrial internet and data factors, with key takeaways as below:

1. Latest industry trends: Data factors have now become the core competitiveness for industrial digitalization. Leading industrial internet platforms are in a critical transition period from traditional industrial e-commerce to industrial digital technology platforms, and leading players have completed closed-loop full-stack layout from data collection and governance to productization and trading. The multiplier effect of data factors on industrial collaboration is gradually being released.

2. Worth-studying business model innovation: Guolian Shares has developed a new business model different from traditional data sales. Instead of positioning data as a superficial asset for direct listing and sale, it treats data as the underlying fuel for all business operations, delivers intelligent technology services to the industrial sector through "data + AI", and converts data sequentially into a resource, an asset, and a competitive advantage, forming a complete closed-loop business model that serves as a referenceable template for the entire industry.

3. Reference for research foundations: The industry has released its first report on enterprise data factor competitiveness, which proposes the four-dimensional evaluation framework of "acquisition, governance, application and security", providing a mature analytical framework for future research in related fields.

Disclaimer: The "Quick Summary" content is entirely generated by AI. Please exercise discretion when interpreting the information. For issues or corrections, please email run@ebrun.com .

I am a Brand Seller Factory Service Provider Marketplace Seller Researcher Read it again.

【亿邦原创】2026年8月28日,在贵阳举办的2026中国国际大数据产业博览会上,亿邦智库正式发布了首份《2026企业数据要素竞争力报告》。该报告历时近一年、结合了近百家企业的实地走访调研,总结分析了企业在数智时代的核心竞争力——“企业数据要素竞争力”的概念,并构建了数据要素竞争力之“获、治、用、安”四力评价模型。国联股份凭借在产业互联网领域二十余年深耕积累的数据要素实践,作为大型产业互联网平台的标杆案例入选报告。

事实上,国联股份很早就将数据要素置于公司核心战略的底层支撑地位,在上市之初,就明确提出“平台、科技、数据”三位一体的核心战略体系。公司管理层明确表示:“数据作为公司的底层战略支撑,我们始终认为是未来实现产业数字化和智能化的底层基石。数据的更大价值不在‘挂牌售卖’的表层,而在于它是公司一切业务运转的底层燃料。”

强化企业数据要素竞争力,首重数据获取能力建设,也就是要有核心数据资源的特殊获得途径。对于产业互联网平台而言,交易数据就是最宝贵的数据资源。在此方面,国联股份走过了一条持续二十余年的渐进之路。从2002年国联资源网起步积累商情数据,到2015年“多多系”垂直电商平台上线实现交易数据大规模积累,形成“商情数据+交易数据”的双轮数据体系。2019年7月公司在主板上市后加速数字化投入,明确“平台、科技、数据”战略。2022至2023年是数据要素化的关键决策期,公司入选工信部“大数据产业发展试点示范项目”,与北京国际大数据交易所合作实现数据确权和数据资产定价。经过二十余年产业深耕,公司已沉淀超200TB工业大数据,覆盖百余个细分行业,数据年增速达100%。目前,公司已与北京国际大数据交易所、贵阳大数据交易所达成战略合作,累计挂牌200余款数据产品。

强化企业数据要素竞争力,关键在治理体系的建设上。而治理体系的核心是组织。在组织架构上,国联股份采用“前中后台”三级架构统筹数据要素开发利用:前台各子公司独立数据部负责产业链数据归集;中台以产品或项目研究为导向倒推数据要素需求;后台由公司管理层统筹战略决策和技术体系建设。在治理层面,公司建立以董事会及其下设的战略规划与ESG委员会为决策层的三级ESG治理架构,将“数据安全与客户隐私保护”列为重要议题。

把数据与AI结合,形成新时代的创新能力,即数据的应用能力,是企业数据要素竞争力的核心体现。在这一方面,国联股份围绕“算力-算法-数据”完整架构展开布局。2025年,公司发布“多多智工”产业级智能体平台,以通用大模型、行业AI模型为核心,以行业数据为支撑,通过MCP链接各应用系统,可快速搭建工业智能体。该平台在大宗商品全球价格监测、国际物流智能调度、产业链风险预警等关键场景中大幅提升企业运营决策的精确性与响应效率。凭借这一创新实践,多多智工平台荣获“2025工业智能体创新应用价值典范奖”,公司入选首批工业智能体创新中心。公司开发的智能供需匹配系统,通过AI图像识别和产业数据匹配,实现“以图找商”,直接盘活了海量供应商和产品数据。在云工厂场景中,AI智能体实现从“事后报警”到“事前干预”的转变,能耗智能体、质量智能体、生产智能体之间可基于全局目标进行实时协同优化。

截至2025年底,国联股份已与290家云工厂达成合作,其中约92家为深度数字化改造的“数字工厂”。应用国联股份跨境业务模式的企业,运营采购成本平均降低5%,订单履约周期缩短20%,物流效率提升30%,生产效率提高15%至30%。2025年,公司研发费用增长82%,正处于从产业电商向产业数字科技平台的关键转型期。国联股份正加速推动数据从采集治理到产品化交易化的全链条闭环,释放数据要素对产业协同的乘数效应。

国联股份的理想图景是构建“专业性+应用性”的数据要素竞争力——聚焦二十余年产业深耕积累的全链条工业数据,通过数据治理、产品化和智能化应用,构建“数据+AI”双重能力,实现从产业互联网“连接器”向技术服务商的转变,为产业互联网企业如何将数据从“资源”转化为“资产”再转化为“竞争力”提供了可参照的范本。

本文首发于 亿邦动力 官方网站

文章来源:亿邦智库

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FAQ回顾

企业数据要素竞争力的评价模型是什么?

《2026企业数据要素竞争力报告》构建了数据要素竞争力“获、治、用、安”四力评价模型,分别对应数据获取能力、数据治理体系、数据应用能力、数据安全保障四大核心维度。

国联股份在数据要素应用方面有哪些成果?

国联股份打造“多多智工”产业级智能体平台,可快速搭建工业智能体,已与290家云工厂达成合作,其中92家为深度数字化改造的数字工厂,合作企业采购成本平均降5%,履约周期缩短20%,物流效率提升30%。

产业互联网企业如何打造数据要素竞争力?

首先要建设核心数据获取能力,积累商情、交易等核心数据资源;其次要搭建完善的组织治理体系保障数据安全;最终要结合AI技术落地多场景数据应用,实现数据从资源到资产再到竞争力的转化。

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