Do you trust the data that you use for Data Science?
Self-service Data Quality
Self-service Data Quality
Analyze the Data Quality of data sources for your ML models directly from your desk
DQO includes data quality checks that will verify the most common data quality issues that would make the data unusable for machine learning. All you need to do is connect to the data source, enable the required quality checks and verify the source data.
- Built-in standard data quality checks
- Run data quality checks directly from your desktop
- Validate data sources before building a machine learning model
Training data revalidation
Training data revalidation
Automatically monitor the quality of the data that is used to retrain your machine learning model. Never retrain your machine learning model using poor quality of data.
DQO is a data monitoring platform that continuously runs your data quality checks every day or when the source data is updated. Ensure that the quality of your data meets the requirements for your machine learning model.
- Validate your training data daily
- Detect outliers in your data sources that can seriously affect the performance of your machine learning model
- Find inconsistencies in your data
Data Quality and ML in one place
Data Quality and ML in one place
Store the source code of your Data Quality rules and your Machine Learning scripts together in one place
All data quality rules are stored in text files, which you can store in Git along with your machine learning scripts. Data quality rules are editable with all popular code editors (such as VSCode) using autocomplete.
- Store data quality rules in Git
- Edit data quality rules with a text editor
- Get auto suggestions (autocomplete) of data quality rules
Measure level of your data correctness in your database
Measure level of your data correctness in your database
Familiarize yourself with the data you store in your database by visualizing the results of data quality monitoring
DQO is a data monitoring platform that continuously performs data quality checks every day or whenever the source data is updated. Ensure the reliability of the data in your database.
- Validate your data every day
- Find inconsistencies in the data
- Detect outliers in your data sources
