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紫色横幅图像,文字说明数据网格的业务案例

数据网格的业务案例

A new solution using Data Mesh is helping enterprises improve 和 accelerate insights. 

 

几十年来,企业在其数据架构上投入了大量资金. Many have poured substantial resources into creating architectures designed to help them rapidly transform ever-increasing amounts of data into actionable insights.

 

这些投资通常不会带来预期的价值. 只有13%的企业擅长实施数据战略《威利斯人官方网站下载app》(MIT 技术 Review)和Databricks今年早些时候的研究显示. 只有26个.8% of firms report successfully embedding a strong data culture across their organizationNewVantage Partners去年的一项研究报告称.

 

对于许多企业, 他们选择的集中式数据架构, 比如数据仓库和数据湖, 是当前问题的根源吗. 数据加载时间长, 分析瓶颈, 以及过度分散和集中的团队, 以及数据质量问题和发现挑战, 这些都是这些架构的副作用吗.

 

最重要的是, domain teams can find themselves having difficulties using the data products that are produced — with this key goal going missing in the rush to onboard 和 process data. 与此同时, 开发领域团队的能力, 创造价值的关键是什么, 在使用集中式架构时是否会被忽略.

 

越来越多的企业正在寻找更灵活的解决方案. 这就是数据网格的作用. 

 

 

什么是数据网格?

 

数据网格 数据架构是一种分散的方法吗, 最初由思想工作者Zhamak Dehghani定义. 在数据网格中,数据不会集中在一个集中的池中. Instead it is broken down into distinct ‘data products’ that are owned 和 managed by the domain teams closest to them.

 

数据网格的四个基本原则,由Zhamak定义为:

 

  • 面向领域的分散数据体系结构. 在数据网格中, 数据由最接近数据的团队拥有和控制, 减少了数据生产者和消费者之间的步骤和切换

 

  • 数据作为产品进行管理. 定制产品使需要数据的团队能够高度访问数据. This empowers teams across domains to self-serve 和访问 whatever they need quickly 和 easily

 

  • 自助服务数据基础设施. 构建数据网格是为了支持自助服务, 和 give teams the automated means to operationalize 和 extract value from data without the manual 和 h和-crafted assistance of centralized experts

 

  • 联合控制. 治理在平台层是自动化的, ensuring st和ards are upheld without impacting flexibility or limiting how individual domains can use data

 

 

这对你的组织意味着什么?

 

作为一种架构方法, Data Mesh is neatly aligned with the data goals that enterprises want to achieve today. It brings data producers 和 consumers closer together 和 empowers teams to self-serve 和访问 highly relevant data products. 因此,它可以很好地帮助公司创建和嵌入敏捷, data-driven cultures of innovation 和 实验 that extend across their organization.

 

 

以下是数据网格为企业提供的一些转型好处:

 

 

更快地做出更明智的决定

 

在集中式数据架构中, 有很多专业人士, h和-crafted steps between the creation of data 和 the actions that result from it. Data is ingested or onboarded in bulk — steps that are often not visible to teams that need the data; even once data is available, 团队可能会面临很长的分析前置时间来将其转化为洞察. 

 

With Data Mesh, a lot of those steps are removed — as in automated or rendered unnecessary. 领域团队使用自己的数据,并管理自己的数据产品. 他们知道自己拥有哪些数据, 他们可以自由地在他们选择的任何时间和任何方式进行操作. 这与集中式数据架构的世界形成了强烈的对比, 在哪里可以产生标准化的数据视图, 在这种假设下,一种方法可以适用于所有的情况. With data mesh, domain teams can be empowered to pull customized views of data as they wish.

 

For enterprises, Data Mesh t在这里fore drives a huge acceleration in decision-making. 通过使域团队能够更快地操作和处理数据, organizations can gain competitive advantages 和 extract greater value from the large volumes of data they gather 和 hold.

 

At 一个主要的金融服务机构, Data Mesh architecture had a substantial impact on average times to value almost immediately. With access to domain-oriented data products 和 the freedom to operationalize data at speed, 高管们可以问更多的问题, 得到更可靠的答案, 并以前所未有的速度根据有价值的见解采取行动. Domain teams were also able to build analytical data directly into their customers’ digital experiences, 提供真正的市场差异化.

 

 

创造真正数据驱动的创新文化

 

One of the biggest advantages of a decentralized architecture like Data Mesh is that it puts the end users of data in control of how it’s managed 和 used.

 

在数据网格中,领域团队处于主导地位. 作为自己数据产品的保管人和控制者, 他们可以随心所欲地试验这些数据. 他们可以问更多的问题, 模拟的场景, 和 explore more data-driven moonshot ideas — the kinds of things that lead to lasting, 有意义的创新.

 

Every domain team is incentivized to ensure that their data products are as co在这里nt 和 well-maintained as possible, 因为它们直接影响团队的分析能力和结果. So, 在一个组织, that adds up to a culture w在这里 everyone across every domain is invested in data 质量, 实验, 并推动数据创新的边界.

 

At 盛宝银行, Data Mesh played a significant role in the organization’s journey to becoming a data-driven open banking institution, 与Thoughtworks合作. The implementation of Data Mesh principles alleviated challenges around data visibility, 质量, 和访问, 和 empowered teams not only to move their open banking objectives ahead but also to continuously improve upon them.

 

 

支持人工智能和机器学习计划

 

AI 和 machine learning have quickly evolved from highly-sophisticated specialist technologies into essential capabilities applied across all levels of the modern enterprise. 交付价值, both need two things; high-质量, 相关的数据集, 以及能够为他们识别强大用例的创新思维.

 

当域团队跨数据网格控制他们自己的数据产品时, those teams will naturally start to build 和 maintain the kinds of data sets needed to fuel game-changing AI 和 ML use cases. 

 

+, 因为域团队是数据的保管人, t在这里 are far fewer barriers preventing them from experimenting with AI 和 bringing powerful new use cases to life. 数据网格成为AI和ML创新的推动者, with teams even having the freedom to create data products specifically for AI 和 ML use — making the capabilities accessible to more teams 和 across more domains than ever before.

 

 

转型始于一个成功的商业案例

 

Together, those benefits form the foundation of a robust business case for Data Mesh. 它们具有广泛的适用性和相关性, 但这还远远不是Data Mesh能提供的唯一优势. 这种方法也可以很好地帮助组织:

 

  • 改善数据质量和治理, 和 even automate many elements of governance 和 compliance using purpose-built data products

 

  • 由于可视性的提高,对新兴法规的响应速度更快, 质量, 以及跨Data Mesh启用的治理模型

 

  • Create or participate in data product marketplaces 和 securely share data products — or even collaborate to co-create products — across organizations

 

  • Identify more opportunities across your enterprise data with more eyes on the data that matters — 和 more teams incentivized to explore every potential use case for it

     

然而, it’s worth keeping in mind that any business case you create for Data Mesh needs to be highly tailored to the challenges your organization is facing. 机会是, some of the benefits we’ve highlighted will resonate more clearly 和 feel more exciting than others. 这就是你需要关注的领域.

 

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