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Transforming Viewer Experience with IoT: A Case Study of ITV
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
- 设备与机械
适用功能
- 质量保证
- 销售与市场营销
用例
- 租赁金融自动化
- 网状网络
服务
- 数据科学服务
挑战
在过去的十年中，广播电视行业发生了重大变化，这很大程度上是由于流媒体服务的兴起。这些变化导致观众期望发生变化，人们现在希望能够随时随地在任何设备上观看各种高质量的节目。英国公共广播电视网络 ITV 面临着满足这些不断变化的期望的挑战，同时还要管理近 4000 万观众生成的大量内容数据。该公司之前依赖多个遗留数据平台，导致数据碎片化。其数据团队同样分散在营销、商业广告和产品体验领域，并拥有自己的技术堆栈。当该公司推出新的数字化战略时，很明显它需要对其平台进行现代化改造，并进行以数据为核心的大规模数字化转型。 ITV 寻求一个平台，使其能够整合其数据源并使用分析、机器学习、基于规则的算法和其他工具来了解观众的期望和行为并改善用户体验。
关于客户
ITV 是一家英国公共广播电视网络，自 1955 年开始运营。它通过其平台提供一系列直播和录制节目，并制作、委托和销售电视内容。该公司的目标是成为一家数字主导的媒体和娱乐广播公司，按照观众的意愿向其提供高质量的内容。 ITV 拥有近 4000 万观众，每天都会产生大量内容数据。除了作为广播电视网络的角色外，ITV 也是一个广告驱动型业务。因此，该公司希望利用数据来确保广告商瞄准最合适的受众。
解决方案
为了实现这些目标，ITV 在 AWS 上进行了整合，并选择 Databricks Lakehouse Platform 作为其数据平台。 ITV 使用 Databricks Lakehouse 平台为数据网格框架提供支持，以连接其三个不同的领域——营销、商业广告和产品体验。每个领域都包含一个由数据科学家、工程师和分析师组成的跨职能团队，他们创建各种数据产品。每个团队以不同的方式处理数据摄取，具体取决于他们摄取的数据类型及其来源。 Lakehouse 有效管理 ITV 从流数据源和批量数据源生成的数据规模，用于分析和机器学习目的。为了创建数据网格，ITV 依靠开源 Delta Lake 作为其存储层，并依靠 Unity Catalog 作为集中治理层。 Unity Catalog 帮助 ITV 为其数据用户创建了一个自助服务平台，并使 ITV 更容易满足其合规性要求。
运营影响
数量效益
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