Case Study: Anvyl finds its most valuable data and saves up to a year of manual work with Select Star

Select Star’s automated data catalog helps Anvyl to find its most valuable data

Anvyl, a 50+ employee production management software startup, struggled to serve a rapidly growing B2B customer base because inconsistent cataloging and naming across BigQuery and Looker made data hard to find. Internal teams were forced to manually search 200+ columns, avoid deleting views for fear of breaking dashboards, and faced up to a year of work to classify and document metadata — creating slow, error-prone reporting. Select Star’s automated data catalog and intelligent data discovery platform was engaged to address these challenges.

Select Star implemented automated metadata ingestion, end-to-end data lineage, and a popularity score to show which datasets, dashboards, and columns users actually rely on, while helping standardize KPIs and remove duplicate data. The result: Anvyl regained visibility into its most valuable data, stabilized upstream changes to prevent broken dashboards, reduced duplicated assets, and sped onboarding — saving the data and analytics team up to a year of manual tasks and significantly cutting time spent fielding data requests.

Anvyl

Christine Liu

Data Engineer