Case Study: Lebara drives operational efficiency and customer engagement with Anomalo
Key results
The challenge
Lebara, a London-based telecommunications provider founded in 2001, relied on manual data validation in which teams spent significant time checking data quality and often found issues only after they had reached downstream business users. This reactive approach limited trust in data and constrained the scalability of the company's broader data and AI initiatives.
The solution
As part of a cloud migration to Databricks, Lebara adopted Anomalo to automate data quality monitoring across its data estate. Automated, machine-learning-based checks replaced manual validation, allowing issues to be detected proactively and root causes to be diagnosed faster.
“We realized that without a solid foundation of data quality, our other initiatives would falter. That's why automating data quality became a cornerstone of our transformation.”
MCMatt CrawleyChief Data Officer, Lebara
The results, in context
Lebara reports saving an estimated 5,000-plus person-hours annually and seeing 15% growth in new customer acquisition. The share of the data team's time spent on data quality issues dropped from roughly 70% to less than 30%, an estimated 80% of data issues are now caught before they impact the business, and AI-driven communications powered by high-quality data account for over 80% of customer interactions.