For decades, enterprises have invested heavily in their data architectures. 许多人已经投入大量资源创建架构，旨在帮助他们快速地将不断增长的数据转换为可操作的见解.
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 Technology Review and Databricks earlier this year. Only 26.8%的公司表示，他们成功地在公司内部植入了强大的数据文化, reported a study by NewVantage Partners last year.
For many enterprises, the centralized data architectures they have chosen, such as data warehouses and data lakes, are at the root of ongoing problems. Long data onboarding times, analytical bottlenecks, and overstretched and centralized teams, together with data quality issues and discovery challenges, can all be unwanted side-effects of these architectures.
Crucially, 领域团队可能会发现自己在使用所生产的数据产品时遇到了困难——在仓促地装载和处理数据时，这个关键目标消失了. Meanwhile, 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. This is where Data Mesh comes in.
What is Data Mesh?
Data mesh is a decentralized approach to data architecture, originally defined by Thoughtworker Zhamak Dehghani. In a Data Mesh, data doesn’t sit together in a centralized pool. 相反，它被分解成不同的“数据产品”，由最接近它们的领域团队拥有和管理.
The four foundational principles of Data Mesh, as defined by Zhamak, are:
Domain-oriented decentralized data architecture. In a Data Mesh, data is owned and controlled by the teams closest to it, removing the number of steps and handoffs between data producers and consumers
Data is managed as products. Bespoke products make data highly accessible to the teams that need it. 这使跨域的团队能够自助服务，并快速轻松地访问他们需要的任何东西
Self-service data infrastructure. Data Meshes are built to enable self-service, 为团队提供自动化的操作方法，并从数据中提取价值，而无需集中专家的手工和手工协助
Federated governance. Governance is automated at the platform layer, 确保支持标准，而不影响灵活性或限制各个域使用数据的方式
What does that all mean for your organization?
As an architectural approach, 数据网格与当今企业想要实现的数据目标整齐地结合在一起. 它拉近了数据生产者和消费者之间的距离，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 and embed agile, 数据驱动的创新和实验文化，扩展到整个组织.
Here are some of the transformational benefits that data mesh offers enterprises:
Making better-informed decisions, faster
In centralized data architectures, there are a lot of expertly, 数据的创建和由此产生的操作之间的手工步骤. 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.
有了Data Mesh，很多这样的步骤都被删除了——因为在自动化或渲染中是不必要的. Domain teams onboard their own data, and manage their own data products. They know what data they have, and they’re free to operationalize it however and whenever they choose. This makes a strong contrast with the world of centralized data architectures, where there can be a tendency to produce standardized views of data, under that assumption that one size will fit all. 有了数据网格，域团队可以根据自己的意愿提取定制的数据视图.
因此，对于企业来说，数据网格极大地加速了决策的制定. By enabling domain teams to operationalize and 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，数据网格体系结构对平均时间几乎立即产生重大影响. 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, get more reliable answers, and 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
In a Data Mesh, domain teams are in the driver’s seat. As the custodians and controllers of their own data products, they’re free to experiment with that data however they like. They can ask more questions, simulate more scenarios, 探索更多数据驱动的登月想法——这类事情会导致持久, meaningful innovations.
每个领域团队都受到激励，以确保他们的数据产品尽可能保持一致和良好的维护, as they directly impact that team’s analytical capabilities and outcomes. So, across an organization, 这就形成了一种文化，即每个领域的每个人都对数据质量进行投资, experimentation, and pushing the boundaries of data innovation.
At Saxo Bank, 在该组织成为数据驱动的开放银行机构的过程中，Data Mesh发挥了重要作用, working in partnership with Thoughtworks. 数据网格原则的实现缓解了数据可见性方面的挑战, quality, and access, 授权团队不仅可以推进他们的开放式银行目标，还可以不断改进它们.
Supporting AI and machine learning initiatives
人工智能和机器学习已经从高度复杂的专业技术迅速发展为应用于现代企业各个层次的基本能力. To deliver value, both need two things; high-quality, relevant data sets, and 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.
Plus, because the domain teams are the custodians of that data, 阻碍他们进行人工智能实验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 and ML innovation, 团队甚至可以自由地为AI和ML创建专门的数据产品——这使得更多的团队可以使用这些功能，并且可以跨越更多的领域.
Transformation starts with a winning business case
这些优点共同构成了Data Mesh健壮业务案例的基础. They’re widely applicable and 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 quality and 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, quality, and governance models enabled across the Data Mesh
However, 值得记住的是，您为Data Mesh创建的任何业务案例都需要针对组织面临的挑战进行高度定制. Chances are, 威利斯人app所强调的一些好处会引起更清晰的共鸣，让人感到更兴奋. And it’s those areas where you need to focus.