Published: 18 May 2023
客户对可用数据的需求，推动了数据工程技术的发展。数据和分析领导者可以通过促进协作、提早论证业务价值、实现发布流程自动化、将数据作为产品来管理以及消除运营负担等方式，增强数据工程实践。
成功的数据工程团队是跨职能团队，采用DataOps实践。
相比于注重技术增强，企业机构如果注重数据工程所产生的业务价值，能更高效地确定数据交付需求的优先级。
对于新的/新兴数据工程团队而言，测试和发布流程需要消耗大量人力。成熟的团队已开始对这些流程进行自动化改造。
许多企业机构在运营单体数据系统和流程，大幅延长了数据交付时间。
虽然许多企业机构已经采用了自助式数据准备，以便业务用户探索数据，但很少能看到将工作负载交付到生产阶段所需的网络控制管理流程。
希望提升数据工程实践的数据和分析（D&A）领导者，应：
建立包含数据、业务和技术角色的跨职能团队，并且根据需要设置产品负责人/经理，以鼓励协作。
在重大数据工程项目启动前展示业务价值，实现价值驱动，避免出现多余的数据工程工作。
提高流程效率，首先实现单个测试和发布流程的自动化，然后通过发布管道协调各个步骤。
将单一数据交付流程分解为模块化数据产品交付，倡导数据复用。
使用必要的网守协议将重复性的数据准备工作推送至生产环境，减轻公民角色的运营负担。
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