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Purple banner image with text saying 数据网格的商业案例


一种使用Data Mesh的新解决方案正在帮助企业改进和加快洞察力. 


For decades, enterprises have invested heavily in their data architectures. 许多人已经投入大量资源来创建架构,旨在帮助他们快速地将不断增长的数据转化为可操作的见解.


Often these investments often don’t deliver the promised value. Just 13% of organizations excel at delivering on their data strategy, according to research by MIT 技术 Review 和 Databricks earlier this year. 只有26个.8%的公司报告成功地将强大的数据文化嵌入整个组织, reported a study by NewVantage Partners last year.


对于许多企业, the centralized data architectures they have chosen, such as data warehouses 和 data lakes, are at the root of ongoing problems. 数据加载时间长, 分析瓶颈, 和 overstretched 和 centralized teams, together with data 质量 issues 和 discovery challenges, can all be unwanted side-effects of these architectures.


最重要的是, 领域团队可能会发现自己在使用生成的数据产品时遇到了困难——在匆忙地开发和处理数据时,这一关键目标消失了. 与此同时, developing capability in domain teams, which is essential for creating value, can get overlooked when centralized architectures are used.


Increasingly, enterprises are looking for more flexible solutions. 这就是数据网格的作用所在. 





数据网格 is a decentralized approach to data architecture, originally defined by Thoughtworker Zhamak Dehghani. 在数据网格中, data doesn’t sit together in a centralized pool. 相反,它被分解成不同的“数据产品”,由与它们关系最近的领域团队拥有和管理.


The four foundational principles of Data Mesh,由扎马克定义为:


  • Domain-oriented decentralized data architecture. 在数据网格中, data is owned 和 controlled by the teams closest to it, removing the number of steps 和 h和offs between data producers 和 consumers


  • 数据作为产品进行管理. Bespoke products make data highly accessible to the teams that need it. 这使得跨域的团队能够快速、轻松地自助访问他们需要的任何东西


  • 自助服务数据基础设施. Data Meshes are built to enable self-service, 并且为团队提供自动化的方法来操作和从数据中提取价值,而不需要集中的专家的手工和手工制作的帮助


  • 联合控制. Governance is automated at the platform layer, 确保在不影响灵活性或限制单个域使用数据的情况下维护标准



What does that all mean for your organization?


作为一种架构方法, Data Mesh与当今企业想要实现的数据目标相一致. 它使数据生产者和消费者更紧密地联系在一起,并使团队能够自助服务和访问高度相关的数据产品. So it’s well-placed to help companies create 和 embed agile, 数据驱动的创新和实验文化延伸到整个组织.



Here are some of the transformational benefits that data mesh offers enterprises:



Making better-informed decisions, faster


在集中式数据体系结构中, 有很多专业的, 在创建数据和由此产生的操作之间手工制作的步骤. Data is ingested or onboarded in bulk — steps that are often not visible to teams that need the data; even once data is available, teams may face long analytical lead times to translate it into insight. 


使用Data Mesh,很多这些步骤都被删除了——比如自动删除或者变得不必要. Domain teams onboard their own data, 和 manage their own data products. 他们知道自己有什么数据, 和 they’re free to operationalize it however 和 whenever they choose. This makes a strong contrast with the world of centralized data architectures, w在这里 t在这里 can be a tendency to produce st和ardized views of data, under that assumption that one size will fit all. 有了数据网格,领域团队可以按照自己的意愿拉出定制的数据视图.


因此,对于企业来说,Data Mesh驱动着决策的巨大加速. By enabling domain teams to operationalize 和 act on data faster, 组织可以获得竞争优势,并从他们收集和持有的大量数据中提取更大的价值.


At one major financial services institution, Data Mesh体系结构几乎立即对平均时间值产生了重大影响. 可以访问面向领域的数据产品,可以自由地快速操作数据, executives were able to ask more questions, 获得更可靠的答案, 和 act on valuable insights faster than ever before. 领域团队还能够将分析数据直接构建到客户的数字体验中, providing real differentiation in the market.



Creating truly data-driven cultures of innovation


像Data Mesh这样的去中心化架构的最大优势之一是,它让数据的最终用户能够控制如何管理和使用数据.


在数据网格中, domain teams are in the driver’s seat. As the custodians 和 controllers of their own data products, they’re free to experiment with that data however they like. 他们可以问更多的问题, 模拟的场景, 并探索更多数据驱动的“登月”想法——那些能带来持久发展的想法, 有意义的创新.


每个领域的团队都被激励去确保他们的数据产品尽可能的一致和良好的维护, as they directly impact that team’s analytical capabilities 和 outcomes. So, 在一个组织, 这就形成了一种文化,每个领域的每个人都致力于数据质量, 实验, 和 pushing the boundaries of data innovation.


At 盛宝银行, Data Mesh在该组织成为一个数据驱动的开放银行机构的过程中发挥了重要作用, working in partnership with Thoughtworks. 数据网格原则的实现减轻了关于数据可见性的挑战, 质量, 和访问, 并授权团队不仅向前推进他们的开放银行目标,而且持续改进这些目标.



Supporting AI 和 machine learning initiatives


人工智能和机器学习已经迅速从高度复杂的专业技术发展成为适用于现代企业所有层次的基本能力. 交付价值, both need two things; high-质量, 相关的数据集, 和 innovative minds that can identify powerful use cases for them.


When domain teams are in control of their own data products across a Data Mesh, 这些团队自然会开始构建和维护所需的数据集,以推动改变游戏规则的AI和ML用例. 


+, because the domain teams are the custodians of that data, 阻止他们尝试人工智能并将强大的新用例带入生活的障碍要少得多. The Data Mesh becomes an enabler of AI 和 ML innovation, 团队甚至可以自由地创建专门用于人工智能和ML使用的数据产品,这使得更多团队和更多领域可以访问该功能.



Transformation starts with a winning business case


总之,这些好处构成了数据网格的强大业务案例的基础. They’re widely applicable 和 relevant, but they’re far from the only advantages that Data Mesh can deliver. The approach also lends itself well to helping organizations:


  • Improve data 质量 和 governance, 甚至可以使用专门构建的数据产品自动化治理和遵从性的许多元素


  • Respond faster to emerging regulations thanks to the improved visibility, 质量, 和 governance models enabled across the Data Mesh


  • 创建或参与数据产品市场,并跨组织安全地共享数据产品——甚至协作共同创建产品


  • 通过更多地关注重要的数据,在企业数据中发现更多的机会,从而激励更多的团队去探索它的每个潜在用例


然而, 值得记住的是,您为Data Mesh创建的任何业务案例都需要高度定制,以适应您的组织所面临的挑战. 机会是, 我们所强调的一些好处会比其他的更能引起人们的共鸣,让人更兴奋. And it’s those areas w在这里 you need to focus.