Case Study: Nextbite achieves scalable data quality monitoring with Sifflet

Nextbite saves 3 hours a week with Sifflet

Nextbite, an all-in-one virtual restaurant solution, faced challenges ensuring data quality at scale due to its heavy reliance on numerous external data sources. The company partnered with the data observability vendor Sifflet to address the ongoing difficulty of consolidating and guaranteeing the quality of this data within their modern data stack.

Sifflet implemented its data quality monitoring platform, which used an auto-coverage feature to rapidly deploy checks and leveraged machine learning to account for cyclical data trends. This solution saved Nextbite several weeks of development time and approximately 3 hours per week previously spent manually tracking errors. With Sifflet, the team now proactively identifies 1-2 data provider issues weekly, resolving them before they impact business reporting.

Nextbite