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Using Location in Real Estate Market Analysis Applications
技术
- 分析与建模 - 大数据分析
- 分析与建模 - 实时分析
- 应用基础设施与中间件 - 数据交换与集成
适用功能
- 商业运营
- 销售与市场营销
用例
- 质量预测分析
- 实时定位系统 (RTLS)
- 供应链可见性(SCV)
服务
- 数据科学服务
- 系统集成
挑战
JLL, a global leader in real estate services, was looking to scale their Gea solution in key countries such as the US, Australia, the UK, Italy, and France. The Gea platform brings together thousands of data types on Real Estate assets to ensure accurate valuations and analyses are provided to their clients. Connecting to their Big Data Cloud infrastructure (JLL DataHub) would be fundamental in order to ensure high-quality, automated data pipelines could support frequent updates to the billions of data points that would be available in the solution. However, they faced challenges in localization for different markets, handling big data, and ensuring the final application would be user-friendly for consultants with more commercial profiles.
关于客户
JLL is a world leader in real estate services, powered by entrepreneurial spirit. They have 93,000 employees globally and are present in more than 80 countries. They buy, build, occupy and invest in a variety of assets including industrial, commercial, retail, residential and hotel real estate. Their clients span industries including banking, energy, healthcare, law, life sciences, manufacturing and technology. They manage 5 billion square feet of property and facilities, with 39,000 leasing transactions globally. JLL is also well-known for their adoption of innovative technology and data analytics, through initiatives like JLL Spark, which provides the nimbleness of a startup backed by JLL's expertise and resources.
解决方案
JLL turned to CARTO to support the development of their Gea solution. CARTO provided first-class map-based visualizations and data pipeline solutions in one single platform, decreasing complexity, saving a significant amount of time, and avoiding mistakes in the dataops, devops, and GISops process. CARTO’s connectivity to solutions such as Databricks, as well as the platform’s Python package (CARTOframes), significantly reduced the strain on JLL’s Data Science division, allowing them to focus on high-quality spatial analysis that brings value to their consultants and their clients.
运营影响
数量效益
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