Data quality has mostly been solved by rules written after issues occur or manual investigations, taking up valuable time from data engineering and data science teams. Interconnectivity of data systems means that a data anomaly in one place can populate elsewhere almost immediately, resulting in a domino effect. Anomalo is focused on cloud-enabled data stacks, looking to build a dedicated data quality assurance ‘layer’ where unsupervised machine learning is used to automate monitoring, detect issues, and show root causes.
Learn below how you can automate data quality monitoring for your data
