We’ve built a process and library on Deequ which provides a robust Data Quality Monitoring solution for Cloud Native data and machine learning pipelines.
Regardless of velocity or volume of data, you can ensure that missing or incorrect data will be detected. Improving confidence of users and decisions.
Get reliable analytic and machine learning pipelines that are resistant to failure from unexpected changes or modifications in data.
ML Model Performance
One of the best kept secrets to better model performance is quality data. One 2016 paper showed 17% increase in model accuracy form clean data.
Why is Data Quality Important?
Data Quality is the achilles heel of analytics & data science. Poor data quality leads to slower and lower quality decisions along with spending significant dollars attempting to find the source of the problem. In fact, according to the Harvard Business Review, IBM estimated poor data quality cost the US economy $3.1 Trillion in 2016 alone.
phData will implement Data Quality Monitoring for your Cloud Native Data Warehouse or Data Lake. This solution defines a process by which organizations can move from a reactive approach to data quality to a proactive approach, saving time and money and speeding decision making. In short you will start finding data quality issues before your users do.
Standard Data Quality process for the Cloud
Check for completeness, consistency, and accuracy based on business rules
Automatically generate data quality rules
Perform data quality on streaming or batch
Ready to learn more about Data Quality Monitoring from phData? Let's chat.