您现在访问的是微软AZURE全球版技术文档网站,若需要访问由世纪互联运营的MICROSOFT AZURE中国区技术文档网站,请访问 https://docs.azure.cn.

如何将工作与有意义的学习指标进行协调?How can we align efforts to meaningful learning metrics?

业务成果概述讨论了度量和传达转换对业务产生的影响的方法。The business outcomes overview discussed ways to measure and communicate the impact a transformation will have on the business. 遗憾的是,在某些情况下,可能需要几年时间才能产生实实在在的结果。Unfortunately, it can take years for some of those outcomes to produce measurable results. 看板和 C 套件与显示长时间的 0% delta 的报表不满意。The board and C-suite are unhappy with reports that show a 0% delta for long periods of time.

学习指标是可与长期业务成果关联的过渡、较短的指标。Learning metrics are interim, shorter-term metrics that can be tied back to longer-term business outcomes. 这些指标在增长思维方式上非常合理,有助于定位文化,使其变得更具弹性。These metrics align well with a growth mindset and help position the culture to become more resilient. 学习指标突出显示了成功的初期指标,而不是突出显示长期业务目标的预计进度。Rather than highlighting the anticipated lack of progress toward a long-term business goal, learning metrics highlight early indicators of success. 度量值还突出显示了故障的早期指示器,这可能会产生最大的机会来了解和调整计划。The metrics also highlight early indicators of failure, which are likely to produce the greatest opportunity for you to learn and adjust the plan.

与此框架中的大部分资料一样,我们假定您熟悉最佳的 转换旅程 ,这与您所需的业务成果最相符。As with much of the material in this framework, we assume you're familiar with the transformation journey that best aligns with your desired business outcomes. 本文将针对每个转换旅程概述几个学习指标,以说明这一概念。This article will outline a few learning metrics for each transformation journey to illustrate the concept.

云迁移Cloud migration

此转换侧重于成本、复杂性和效率,重点关注 IT 运营。This transformation focuses on cost, complexity, and efficiency, with an emphasis on IT operations. 此转换背后最容易测量的数据是将资产移动到云。The most easily measured data behind this transformation is the movement of assets to the cloud. 在这种类型的转换中,数字区域由虚拟机衡量,这些虚拟机托管这些 Vm 的虚拟机) 、机架或群集、数据中心运营成本、需要资本支出以维护系统,以及一段时间内对这些资产进行折旧的虚拟机 (。In this kind of transformation, the digital estate is measured by virtual machines (VMs), racks or clusters that host those VMs, datacenter operational costs, required capital expenses to maintain systems, and depreciation of those assets over time.

将 Vm 迁移到云时,会降低对本地旧资产的依赖。As VMs are moved to the cloud, dependence on on-premises legacy assets is reduced. 还降低了资产维护的成本。The cost of asset maintenance is also reduced. 遗憾的是,在群集取消预配并且数据中心租约过期之前,企业无法实现成本降低。Unfortunately, businesses can't realize the cost reduction until clusters are deprovisioned and datacenter leases expire. 在许多情况下,在完成折旧周期之前,不会实现工作的全部价值。In many cases, the full value of the effort isn't realized until the depreciation cycles are complete.

在制作财务报表之前,始终与 CFO 或金融办公室保持一致。Always align with the CFO or finance office before making financial statements. 但是,IT 团队通常可以根据 CPU、内存和使用的存储空间来估算每个 VM 的当前货币成本和未来的货币成本值。However, IT teams can generally estimate current monetary cost and future monetary cost values for each VM based on CPU, memory, and storage consumed. 然后,你可以将该值应用于每个已迁移的 VM,以估计立即节约成本,并估计未来的工作量。You can then apply that value to each migrated VM to estimate the immediate cost savings and future monetary value of the effort.

应用程序创新Application innovation

启用云的应用程序创新主要关注客户体验,以及客户使用公司提供的产品和服务的意愿。Cloud-enabled application innovation focuses largely on the customer experience and the customer's willingness to consume products and services provided by the company. 更改增量的时间会影响使用者或客户购买行为。It takes time for increments of change to affect consumer or customer buying behaviors. 但应用程序创新周期往往比其他形式的转换要短得多。But application innovation cycles tend to be much shorter than they are in the other forms of transformation. 传统的建议是,你应该首先了解你想要影响的特定行为,并使用这些行为作为学习指标。The traditional advice is that you should start with an understanding of the specific behaviors that you want to influence and use those behaviors as the learning metrics. 例如,在电子商务应用程序中,采购总量或外接程序购买可以是目标行为。For example, in an e-commerce application, total purchases or add-on purchases could be the target behavior. 对于视频公司,观看视频流的时间可能是目标。For a video company, time watching video streams could be the target.

客户行为指标可以很容易受到外部变量的影响,因此,在学习指标中包含相关统计信息通常很重要。Customer behavior metrics can easily be influenced by outside variables, so it's often important to include related statistics with the learning metrics. 这些相关的统计信息可能包括发布节奏、每个版本解决的 bug、单元测试的代码覆盖率、页面吞吐量、页面吞吐量、页面加载时间以及其他应用程序性能指标。These related statistics can include release cadence, bugs resolved per release, code coverage of unit tests, number of page views, page throughput, page load time, and other application performance metrics. 每个可以显示不同的活动和基本代码的更改,以及与更高级的客户行为模式关联的客户体验。Each can show different activities and changes to the code base and the customer experience to correlate with higher-level customer behavior patterns.

数据创新Data innovation

更改行业、中断市场或转换产品和服务可能需要数年的时间。Changing an industry, disrupting markets, or transforming products and services can take years. 在支持云的数据创新工作中,试验是衡量成功的关键。In a cloud-enabled data innovation effort, experimentation is key to measuring success. 可以通过共享预测指标来进行透明,如百分比概率、失败试验的次数以及定型的模型数。Be transparent by sharing prediction metrics like percent probability, number of failed experiments, and number of models trained. 故障的累计速度将快于成功。Failures will accumulate faster than successes. 这些指标可以 discouraging,执行团队必须了解正确使用这些指标所需的时间和投资。These metrics can be discouraging, and the executive team must understand the time and investment needed to use these metrics properly.

另一方面,一些正面指标通常与数据驱动的学习相关:集中处理异类数据集、数据入口和数据 democratization。On the other hand, some positive indicators are often associated with data-driven learning: centralization of heterogeneous data sets, data ingress, and democratization of data. 团队在了解未来的客户时,可以立即产生真实结果。While the team is learning about the customer of tomorrow, real results can be produced today. 支持的学习指标包括:Supporting learning metrics could include:

  • 可用的模型数。Number of models available.
  • 使用的合作伙伴数据源数。Number of partner data sources consumed.
  • 生成入口数据的设备。Devices producing ingress data.
  • 入口数据量。Volume of ingress data.
  • 数据的类型。Types of data.

一个更有价值的指标是从组合数据源创建的仪表板的数目。An even more valuable metric is the number of dashboards created from combined data sources. 此数字反映受新数据源影响的当前状态业务流程。This number reflects the current-state business processes that are affected by new data sources. 通过公开共享新数据源,你的业务可以使用报表工具(如 Power BI)来构建增量见解和推动业务变化,从而利用数据。By sharing new data sources openly, your business can take advantage of the data by using reporting tools like Power BI to produce incremental insights and drive business change.

后续步骤Next steps

了解学习指标之后,就可以开始 构建业务案例 来应对这些指标。After learning metrics are aligned, you're ready to begin building the business case to deliver against those metrics.