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Faster Insights Drive Better Business Outcomes: A Case Study on Fannie Mae
技术
- 分析与建模 - 机器学习
- 功能应用 - 企业资源规划系统 (ERP)
适用行业
- 化学品
- 设备与机械
适用功能
- 质量保证
用例
- 租赁金融自动化
挑战
房利美 (Fannie Mae) 是一家领先的金融服务公司，在管理大量业务数据方面面临着挑战。该公司在 2022 年实现了超过 200 万套房屋的购买和再融资，并为全美约 598,000 个租赁单位提供了融资，并且正变得越来越数字化和以数据为中心。为了利用新旧应用程序中的所有业务数据，并打破现有的数据孤岛，该公司希望创建一个敏捷且动态的企业数据湖。然而，管理这个数据湖的过程既复杂又耗时。其 15,000 个数据集中的每一个数据集都经过初始注册过程来分配唯一标识符，并且每个字段都必须手动记录。这种方法提高了合规性和透明度，但由于需要向每个数据集添加一组精心设计的元数据，导致流程变慢。
关于客户
房利美 (Fannie Mae) 是一家领先的金融服务公司，为全美各地的贷方提供可靠的抵押贷款融资来源。通过购买抵押贷款，该公司帮助贷方向更多人提供新的抵押贷款。在此过程中，房利美扩大了获得经济适用住房的机会，为租房者、购房者和房主提供支持。房利美拥有约 8,000 名员工，到 2022 年实现了约 260 万套购房、再融资和租赁单位的收购和融资。
解决方案
为了建立更快、更动态的数据基础设施，Fannie Mae 选择 Pentaho Data Catalog 作为集中式、数据不可知的工具来加速数据可用性。该软件完全在 Amazon Web Services (AWS) 的云中跨多个可用区运行，并具有自动扩展功能，以确保快速性能和业务连续性。它处理数千万个文件和相关属性，并将它们聚合成数千个高级数据集，业务团队可以轻松使用和参考这些数据集以获得可行的见解。 Fannie Mae 现在依赖基于 Pentaho Data Catalog API 的流程自动化，这使得该公司能够将其广泛的业务应用程序连接到企业数据湖并每天更新数据集。 Pentaho Data Catalog 执行自动预注册步骤，使用机器学习和 AI 来验证和标记元数据并检测敏感数据。然后，它可以立即将所有内容提供给公司的元数据分析师、数据管理员、数据管理者和业务数据官员，以进行进一步处理和分析。
运营影响
数量效益
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