Case Study: PepsiCo achieves data quality at scale with Monte Carlo

How PepsiCo Achieved Data Quality at Scale with Monte Carlo

PepsiCo, the global food and beverage company behind brands like Lay’s, Doritos, and Gatorade, needed a better way to improve data incident detection and reporting across a large number of data pipelines and tables. To address these data quality challenges at scale, PepsiCo turned to Monte Carlo’s data observability platform, using it to monitor the health of its Snowflake data assets and share data quality information with engineering teams and data users.

With Monte Carlo, PepsiCo implemented automated quality monitors, Slack alerts, incident tracking, field-level lineage, and dbt integration to improve visibility into production pipelines and speed root-cause analysis. According to PepsiCo, Monte Carlo helped the team detect and resolve data issues faster, strengthen reliability, and build toward clearer SLIs and SLOs; while the case study does not provide hard numeric ROI, it highlights improved data servability and more efficient handling of data incidents through Monte Carlo.

PepsiCo

Sagar Saraiya

Data Platforms Leader