Rapidly apply built-in predictive models to discover problems. Offload data science tasks such as feature selection, bucketing, and binning. Select and run machine learning models with just a few clicks. From data discovery to complex predictions, OwlDQ offers algorithms for duplicate detection, cross-column categorical patterns, and outlier analysis.
Trying to guess where our outliers were and then write code reactively to catch them didn't work. We needed a way to find our blindspots.Read More
One of the most time consuming aspects of data quality is writing the actual rules. OwlDQ can replace the task of implementing handwritten rules for standard technical data quality constraints. Rule conditions are adaptive so if your data has natural variance, the models will automatically adjust over time. For most, this means a reduction in 50%-70% reduction in total number of rules. Reducing 'data quality' technical debt is the primary reason for modernizing existing rule-based solutions.
With Owl we were able to throw away over 50% of our rules. We removed all low value null checks and simple conditions. Owl found and protected Pii data in places we didn't think to look.Read More
Often the first and most common data routine is to move data from the source into the target system. This is growing even more popular with cloud migrations. OwlDQ checks that every record in every cell matches between copies (as well as standard row count, column, and conformity checks). This is most commonly used when loading third-party data files, during cloud migrations, and after moving data to persistent storage.
Owl's daily snapshot view revealed that our database tables were not in sync with the upstream source. Owl identified replication errors in our homegrown scripts and our commercial CDC tool.Read More
Add data quality to your data pipeline by using Owl’s DQ framework and libraries. Access dozens of DQ algorithms that run consistently on files, database tables and kaka topics. All formats from s3 buckets to json, xml and csv. The framework is purpose built for DQ making each line of code simple, terse and scalable. Use IF blocks to make decisions when erroneous data enters your pipeline.
Data quality can be simple or complex depending on your needs. Many organizations require the ability to catalog all data experiments, apply DQ checks, run DQ jobs on a regular schedule, alert when errors arise and move errors into a work queue to remediate. Every wonder how successful your current DQ program is? Try Owl reports to get an overview of your progress overtime.