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Anodot > 实例探究 > 利用 AI 扩展业务指标可观察性:Freshly 的案例研究
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Scaling Business Metrics Observability with AI: A Freshly Case Study

技术
  • 分析与建模 - 实时分析
  • 平台即服务 (PaaS) - 应用开发平台
适用行业
  • 服装
  • 水泥
适用功能
  • 仓库和库存管理
用例
  • 实时定位系统 (RTLS)
  • 资产跟踪
挑战
刚刚缺乏衡量和评估数据的数据基础设施,依靠直觉来评估业务绩效。
关于客户
Freshly 是雀巢于 2020 年收购的预餐配送服务。该公司拥有一支由数据副总裁 David Ashirov 领导的数据团队,他在数据工程、商业智能和营销方面拥有丰富的经验。
解决方案
新构建的数据结构用于连接整个公司的数据,使用各种 SaaS 产品进行数据捕获、仓储、分析和警报。实施Anodot自主业务监控以实现持续的实时数据监控。
运营影响
  • The implementation of a robust data infrastructure and automated data monitoring has significantly improved Freshly's business operations. The company now has a single source of information for any business question, fostering trust in the data among employees. The data team can now easily build any kind of report that anyone in the company could want. The company can also monitor millions of metrics in real time for abrupt and significant changes, allowing for quick response to incidents and minimization of impact to the business. This has helped prevent incidents that could potentially drain millions in revenue. The company can now also better understand its business processes and data needs, enabling it to make more informed decisions and drive growth and efficiency.
数量效益
  • The company's estimated revenue per employee increased to $140,408, significantly higher than the average small business revenue per employee of $100,000.
  • The data team was able to build every kind of report that anyone in the company could want within a month of establishing the data fabric.
  • The company can now monitor millions of metrics in real time for abrupt and significant changes.

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