Automating Data Quality for Enterprise: Lessons from Lebara’s Journey

How Lebara Drives Operational Efficiency and Best-in-Class Customer Engagement with Anomalo and Databricks

In the fast-paced world of telecommunications, staying ahead means leveraging data to its fullest potential. Kicking off the Databricks Data + AI World Tour in London last week, Anomalo’s Director of Machine Learning, Vicky Andonova, joined Matt Crawley, Chief Data Officer of Lebara, to share the multi-year story behind Lebara’s enterprise data quality journey.

Here’s how Lebara, a leading European mobile network operator, transformed its approach to data quality and emerged as a data-driven powerhouse.

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. “As a Chief Data Officer, my role is primarily to drive value through data,” says Matt.

He paints a picture of the challenges they faced:

• 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.

“We were data-rich but insight-poor,” Matt recalls. “We knew we needed a radical change to stay competitive.”

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:

1. Modernizing Infrastructure: Moving from on-premises systems to a cloud-based Databricks architecture.
2. Leveraging AI and ML: Implementing machine learning models to drive personalized customer interactions.
3. Automating Data Quality: Partnering with Anomalo to automate data quality monitoring at scale.

“We realized that without a solid foundation of data quality, our other initiatives would falter,” Matt explains. “That’s why automating data quality became a cornerstone of our transformation.”

The Results: A Data Quality Success Story

The impact of Lebara’s data quality journey has been remarkable:

• Proactive Issue Detection: “Now, [an estimated] 80% of data issues are caught before they impact the business,” Matt shares.
• 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 roughly 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.

While the qualitative benefits were clear, Lebara also saw significant business ROI:

• Cost Savings: Reduced need for manual data validation saved an estimated 5,000 person-hours annually.
• Revenue Growth: Improved data quality led to more effective marketing campaigns, resulting directionally in a 15% increase in customer acquisition rates.
• Operational Efficiency: AI-assisted responses in call centers saved an estimated 40 seconds per customer interaction, translating to millions in annual savings.

Breaking it Down: Lebara’s Journey to Enterprise Data Quality at Scale

Lebara’s journey began with a pivotal decision to revolutionize their business through a digital and data transformation. As Matt pointed out, this shift was crucial in positioning Lebara as a data-first organization. 

Building a data-first culture involved 3 foundational steps:

1. Establishing a Data Governance Committee: This cross-functional team set the strategy and priorities for data initiatives.
2. Starting Small: Lebara’s data team focused initially only on critical data assets, gradually expanding their scope.
3. Training and Change Management: Employees across the organization were trained on new tools and processes, to ensure enterprise-wide trust and buy-in.

One of the most striking aspects of Lebara’s transformation was its rapid adoption of data to drive a competitive edge. This allowed the team to power exceptional customer experiences for the millions of customers that trust Lebara as their mobile virtual network of choice.

These business initiatives included:

• Personalized Communications: Directionally, the team is seeing roughly 80% of Lebara’s communications now driven by machine learning capabilities.
• AI in Customer Service: They’ve implemented AI-assisted responses in call centers, saving approximately 40 seconds per customer contact.
• Smart FAQ Platform: A newly launched system allows customers to ask questions in their preferred language and receive instant answers.

As Lebara expanded its data-driven initiatives, data quality became increasingly critical. Matt emphasized that poor data quality can lead to incorrect decisions, especially as AI and ML models become more prevalent in decision-making processes.

Key capabilities for data quality, made easier by Anomalo:

1. Automation is Key: Manual data validation is time-consuming and often ineffective at scale. Anomalo’s AI-powered approach allows Lebara to monitor data quality across their entire data estate with a lean team.
2. Proactive vs. Reactive: Identifying issues proactively builds trust in data across the organization. Anomalo’s out-of-the-box dashboards and visualizations means teams will see value in Anomalo from day one. 
3. Root Cause Analysis (RCA): Quick identification of the source of data issues reduced resolution time from hours to minutes, thanks to Anomalo’s automatic RCA functionality.

Here, data and business teams can partner with external vendors like Anomalo. Rather than trying to build everything in-house, the ROI on solutions like Anomalo can be realized in days, not years.

Looking Ahead: The Next Frontier

As companies increasingly leverage generative AI and large language models, ensuring the quality of both structured and unstructured data is becoming a new frontier. For Lebara, these initiatives would encompass capabilities like sentiment analysis, predictive network optimization, and proactive customer churn prevention. While solutions are still evolving, data leaders should start considering strategies for managing and validating unstructured data quality.

“Our journey to automated data quality wasn’t just about implementing a new tool,” Matt concludes. “It was about transforming our entire approach to data. Today, data isn’t just a byproduct of our operations; it’s the lifeblood of our business.”

Lebara’s story serves as a powerful testament to the transformative potential of automated data quality. As businesses continue to navigate the data-driven future, those who prioritize data quality will find themselves not just surviving, but thriving in a data- and AI- first world.

To learn more about our integration with Databricks, connect to Anomalo on Partner Connect. For more information about Anomalo and to explore how data quality will drive your business forward, request a demo.