Case Study: Appcues cuts data quality issues 77% with Metaplane
Key results
The challenge
Appcues depends on data across product, operations, sales, marketing, and customer success, but a single Business Intelligence Architect worked exclusively on data. Before Metaplane, data quality issues were flagged by stakeholders, sometimes several times in one week, and he would not know about a problem until someone pinged him. Getting changes right the first time and manually fixing breakages wasted time and caused burnout.
The solution
Appcues adopted Snowflake, dbt, and Metaplane together. Metaplane was configured to automatically add tests and monitor data quality across hundreds of tables in Snowflake and dbt, with Slack alerts and machine-learning models tunable in a click, integrating with Looker for root-cause analysis and downstream impact.
“The important thing is that when things break, I know immediately—and I can usually fix them before any of my stakeholders find out.”
AMAndrew MackenzieBusiness Intelligence Architect, Appcues
The results, in context
After adopting Metaplane, Appcues experienced 77% fewer data quality issues and roughly a 10x reduction in stakeholder 'WTF' messages about broken data. The team became more productive, spending on average 20 to 40 fewer hours per month investigating data issues.