About Client:
The client is a global travel company operating a major international airline alongside multiple digital platforms for booking flights, hotels, vacation packages, and ground transportation. With millions of daily interactions across mobile apps, websites, call centers, and partner ecosystems, delivering consistent and personalized customer experiences is core to the business.
Background:
The client’s data ecosystem spans airline operational systems, online travel agency (OTA) platforms, CRM tools, and third-party partner APIs. To centralize insights, a cloud-based data lake was implemented. However, rapid growth in data volume, legacy systems, and inconsistent partner feeds introduced significant data quality issues. Over time, this reduced confidence in analytics and slowed downstream innovation.
To address this, the client required a scalable DQI framework supported by a centralized DQI dashboard to continuously measure and monitor data health across the pipeline.
Challenge:
The organization faced multiple data quality challenges that made consistent governance difficult:
- Inconsistent data formats across the data lake due to conflicting schemas and naming conventions
- Missing and incomplete data, especially for critical booking, passenger, and contact attributes
- Duplicate records leading to fragmented customer views and distorted reporting
- Delayed detection of issues, with quality problems surfacing late and impacting real-time use cases
- Manual data fixes, consuming disproportionate data engineering effort
- Lack of visibility, with no unified DQI dashboard to track trends, health scores, or root causes
- Rigid rule management, where hard-coded checks limited adaptability to new data sources
