We had a great chance to help a huge online retail clothing brand that works with more than a million customers to achieve the next level of data quality and consistency through Data Warehouse optimization.
The client’s former Data Warehouse infrastructure was undermining data consistency. Due to the lack of source monitoring, standardization, documentation, and testing, missing data remained unnoticed for up to 1 month. The messiness and difficulty to grasp and manage the Data Warehouse was spawning extra maintenance costs and effort, reporting delays, and regular data inconsistencies.
Solution we provided
Based on thorough client discussions and research, we suggested implementing dbt as a tool to enrich the existing Data Warehouse with structure, documentation, and lineage information it lacked. The ultimate goal of adopting dbt was to standardize data transformations and set up Slack-integrated tests and alerts, achieving process optimization and workflow facilitation across the board.
We ultimately managed to reduce the inflow rate of data support requests by 90% and cut overall maintenance hours by 75%, exceeding the client’s expectations. The dbt-equipped smoothed-out Data Warehouse has become much easier to understand and manage, while its updated rates of performance help cut maintenance costs, boost data efficiencies, and speed up the transformation of insights into actions.