The client is an asset manager of single family real estate. As a nimble start-up company in a relatively new market, they have had to navigate the lack of data type standards in their industry. The real estate space is also dominated by old-money investors and long-tenured decision makers unaware of benefits of a modern data stack.
Their small team also needed to be able to make decisions quickly to keep up with their growing customer base and large network of property managers. This meant working closely with a trusted data partner who could match their ability to pivot quickly as they built out their technology stack.
They needed a solution that would not only help them ingest, clean, and leverage the data they already had at their disposal, but that would scale with their team and support innovation as they continue to elevate their analytics and data governance capabilities.
The main goal of the project was to transition the client’s existing data from a MySQL database to Snowflake’s cloud-based environment. This migration involved data ingestion from their existing database, third party sites, and Snowflake privately shared listings. In doing so, the client is enabled to consolidate business intelligence reporting and decision-making, as well as identify trends from within the platform—eliminating many spreadsheets and data manipulation errors in the process. The client is also sharing data back to property managers through the Snowflake Marketplace.
IBM Consulting’s experience across the modern data stack, including data pipeline and raw data methodology, allowed us to accelerate the client’s Snowflake migration, help them select the right software packages, and bolster their efforts to establish best practices.
By including a Senior Engineer with a real estate background on the team for this project, we were able to establish our credentials and build trust with MCS very quickly. That allowed us to shorten the learning curve around the client’s business and account for industry-specific challenges when building their data pipelines.
