技术
- 分析与建模 - 机器学习
- 平台即服务 (PaaS) - 应用开发平台
适用行业
- 建筑物
- 水泥
适用功能
- 销售与市场营销
用例
- 楼宇自动化与控制
- 时间敏感网络
服务
- 培训
关于客户
HOMER是一家早教公司,成立于2013年,是一家阅读平台。多年来,该平台不断发展,提供的功能远远超出了阅读应用程序的范围,还提供数学、社交和情感学习、创造力等课程。该公司的分析结构基于三个核心方面:获取、参与和保留。从收购的角度来看,分析团队有助于确定广告支出的效率,使营销团队能够根据业务成果更准确地分配预算。在用户参与度方面,HOMER 面临着独特的挑战,因为该应用程序的目标受众是父母和监护人,而不是该应用程序的最终用户。从保留方面来看,团队专注于了解客户行为。
挑战
HOMER 是一家早期学习公司,其数据管理面临着重大挑战。尽管该公司的根基是数据驱动的,但当 Joe Nowicki 于 2021 年 2 月加入担任数据和洞察副总裁时,数据和分析架构被认为处于基础阶段。该公司的数据团队花费了大量时间构建 ETL 管道,这是一个费力且耗时的过程。扁平化和维护 Stripe 数据每月要花费团队数十个小时,阻碍他们为更广泛的组织增加价值。遗留数据实践导致了一种不信任感,领导者无法依赖仪表板视图和不明确的商业智能。获取及时的数据至关重要,但却缺乏。对整个数据基础设施进行了多次设想和修订,最终选择了 Databricks 的 Delta Lake,为未来的机器学习应用程序设置 HOMER。
解决方案
Joe Nowicki 凭借之前在 Fivetran 的经验,看到了通过自动化 ELT 简化数据管理流程的机会。他知道开箱即用的 ELT 平台可以为数据团队提供巨大的效率,也知道维护连接器需要付出巨大的努力。他发现 Fivetran 的连接器和业务需求存在大量交叉。从支付平台到营销供应商,HOMER 的数据团队正在处理来自常见来源的大量结构化数据,这些数据可以通过 Fivetran 轻松导入。这使得工程人员可以将时间集中在维护自定义源和事件流上。 Joe 使用包括 Stripe 在内的 6 个 Fivetran 连接器构建了一个概念验证,以向 HOMER 团队演示该工具。
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