Case Study: Afterpay promotes data trust and self-serve data quality with Anomalo
Key results
The challenge
Afterpay, an Australia-based buy-now-pay-later platform acquired by Block in early 2022, wanted to build broad data trust and reduce the bottleneck of routing every data quality question through its engineering team. Concentrating all metrics and checks within engineering would not scale as demand for reliable data grew across the organization.
The solution
Afterpay implemented Anomalo using a six-dimension data quality framework covering timeliness, completeness, uniqueness, accuracy, consistency, and validity, and made data quality metrics transparent to all users. Anomalo was configured for scheduled checks and alerting, and data quality metrics were tracked via the Anomalo API and combined with the Amundsen data discovery catalog.
“In our framework, all the metrics and data quality checks are transparent to our users. If we keep all the metrics within the engineering team, then in the future all the requests will come to the engineering team. This is not scalable over time.”
XJXinwei JiangData Engineer, Afterpay
The results, in context
Within four months, Afterpay was able to address 30% of its data-related questions in a self-serve capacity, a result the company attributes jointly to Anomalo and Amundsen. Making data quality metrics transparent across team members supported broader data trust and reduced reliance on the engineering team for routine questions.