A restructured tech stack geared to better ingest and organize multiple data sources helps identify more users.

A full-service real estate company, Reali blends the best in real estate and technology to offer premium solutions and services for both buyers and sellers. Their suite of technology-driven tools, mobile app, and locally licensed real estate agents have reinvented the real estate model to put consumer interests first.

The team at Reali understands the power of data insights. With a strong tech stack in place, their team eagerly utilized data and analytics to understand daily performance and drive key business decisions. However as Reali’s growth exploded in recent years and they added new data sources to their tech stack, their BI tools became disorganized. The more data Reali added, the more data cleanup was required. And with more and more data sources, they realized they weren’t able to track a sizable percentage of their users. Reali reached out to Mammoth Growth to organize their BI tools and make data insights easier to extract without technical support.

Mammoth Growth’s initial project was to rebuild Reali’s Looker implementation and to organize their many different data sources. Reali relied on Looker for self-serve analytics.  However, without a clear schema or back-end data orchestration, only a technical expert could build accurate reports with confidence. Too many data sources resulted in duplicate data, conflicting results, and no clear source of truth. Mammoth Growth found the answer in Reali’s Snowflake implementation.

A closer look revealed all of Reali’s data cleanup, aggregation, and organization happened in Looker every time they ran a report. Mammoth Growth pulled all of these data transformations out of Looker and organized them in Snowflake. By generating truth tables in Snowflake prior to porting their data into Looker, Reali team members can focus on building reports with accurate, reliable data, instead of trying to do their own data cleanup every time they need insights. This new implementation removed common errors and improved the overall quality of their BI reports.

With this new architecture in place, Mammoth Growth then implemented dbt to further productize these data transformations. Now dbt tests all of Reali’s data to audit and verify truth tables, alerting the team when something is off. Bringing these engineering practices into Reali’s BI environment will help automate their data accuracy for the long term.

Once data was clean and organized within their BI environment, Mammoth Growth turned to Reali’s primary challenge: accurate user tracking. After examining their entire martech stack, Mammoth Growth discovered a mismatch between user data flowing from Segment through Mixpanel and Appsflyer. To clear this up, Mammoth Growth built a custom attribution solution that would merge user data from all three sources to better identify unique users, instead of relying solely on Mixpanel. With this modern data pipeline in place, everyone at Reali immediately had a much clearer picture of the full customer journey, 

This custom solution combined with the data cleanup in Snowflake has offered the strongest insight into user attribution, and has reduced the amount of untracked users by 25%. Mammoth Growth improved data accuracy and accessibility for non-technical team members throughout Reali, and now everyone there can get valuable insights faster.