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Retail Technology / Reverse LogisticsSourced

Case Study: Optoro builds data trust and saves 44 hours per week with Monte Carlo

Optoro Case StudySourced & dated by Case Study Desk
Key facts · TL;DR
Company
Optoro
Industry
Retail Technology / Reverse Logistics
Challenge
Engineers spent hours each week investigating bad-data support tickets.
Headline result
Optoro saves 44 engineering hours per week investigating bad data with Monte Carlo

Key results

44 hrs/week
Engineering time saved
across 11+ engineers
4 hrs/week
Saved per engineer
on bad-data support tickets

The challenge

Optoro, a reverse-logistics and returns technology company, saw its data engineers spend significant time each week investigating bad data reported through support tickets.

The solution

Optoro deployed Monte Carlo, using automatically generated data lineage to trace issues and reduce manual investigation.

The fact that the Monte Carlo system is able to build this lineage itself is remarkable. This required little to no input from our data teams in terms of structuring upstream and downstream dependencies.

PC
Patrick Campbell
Lead Data Engineer, Optoro

The results, in context

Optoro estimated Monte Carlo saves at least four hours per engineer each week on bad-data investigations, totaling 44 hours per week across its team of 11-plus data engineers.

Products used

Monte Carlo Monte Carlo Data + AI Observability Platform