About Client:
A global travel company that operates a major international airline alongside a suite of online platforms for booking flights, hotels, vacation packages, and ground transportation. With millions of customers interacting daily across apps, websites, call centers, and partner channels, delivering seamless, personalized experiences is at the heart of their business.
Background:
The client’s data landscape spans operational airline systems, online travel agency (OTA) platforms, CRM tools, and partner APIs. To centralize insights, they had built a cloud-based data lake. However, the rapid growth of data sources combined with legacy systems and inconsistent partner feed, led to major data quality issues that eroded trust and slowed innovation.
Challenge:
- Inconsistent Formats In Data Lake: Different systems and partners used conflicting data structures and naming conventions
- Missing & Incomplete Data: Critical booking and contact details were often absent or partially captured.
- Duplicate Records: Customer and booking duplication skewed reports and led to fragmented profiles.
- Delayed Issue Detection: Data quality problems emerged late in the pipeline, affecting real-time operations and personalization.
- Manual Data Fixes: Data teams spent excessive time manually cleaning and patching issues.
- Lack of Visibility: No centralized dashboard or framework to monitor data quality trends, health, or to identify root cause of issues across the pipeline
- Rigid Rule Management: Hard-coded validations across pipelines made it hard to adapt to new sources and rules.
