The Challenge
Drowning in Data, Starving for Insights
Three years ago, Lebara found itself at a crossroads. Despite being in the telecommunications business for over two decades, the company was struggling to harness the true power of its
data. The challenges they faced included:
- Manual Data Validation: Teams spent countless hours manually checking data quality, often finding issues only after they reached downstream business users.
- Lack of Trust: Business leaders were hesitant to rely on data, often reverting to gut feeling for critical decisions.
- Reactive Problem-Solving: The data team was constantly putting out fires, with little time for strategic initiatives.
- Limited Scalability: As data volumes grew, manual processes simply couldn’t keep up.
The Solution
Embracing Automation and AI to Drive Trust in Data
Recognizing the need for a complete overhaul, Lebara embarked on an ambitious data transformation journey.
Their strategy focused on three key pillars:
- Modernizing Infrastructure: Moving from on-premises systems to a cloud-based Databricks architecture.
- Leveraging AI and ML: Implementing machine learning models to drive personalized customer interactions.
- Automating Data Quality: Partnering with Anomalo to automate data quality monitoring at scale.
The Results
A Data Quality Success Story
The impact of Lebara’s data quality journey has been remarkable:
- Proactive Issue Detection: An estimated 80% of data issues are now caught before they impact the business.
- Efficiency Gains: Time spent on data quality issues dropped from roughly 70% to less than 30% of the data
team’s workload.
- Improved Customer Experience: AI-driven communications, powered by high-quality data, now account for over 80% of customer interactions.
- Faster Resolution: Root cause analysis time reduced from days to minutes.
- Increased Data Trust: Business leaders now confidently make data-driven decisions, knowing the underlying data is reliable.