Gartner Research


Published: 18 May 2023



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  • 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。



  • 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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