Increase the trust in your data by knowing the quality of the data. Implement data quality rules and monitor the outcomes over time.
Know the quality of your data. Implement data quality rules according to rules defined by your business. Group the rules according to your perspectives and call the groups from your ETL pipelines automatically. Quickly drill down from data quality dashboards to the root cause of data quality failures. And easily track the data issues and ensure their efficient analysis.
Reduce the time spent manually analyzing your data and focus on utilizing high-quality data to make business-critical decisions with confidence.
Consolidated data quality
Integrate and schedule data quality monitoring into your processes (in batches or in real time) and automatically get informed about the data quality issues via email notifications or in the application.
Start with your business needs and metric definitions defined by your business department. Then get a framework to set your data quality rules enabling them to become a part of data quality observability.
Enable your teams to have better control over the migrated data. Migrate easily with data integrity checks providing a full table of results – comparisons between the original and migrated data.
Automate your data quality monitoring, detect anomalies, and predict the future status of data quality for risk mitigation, fraud prevention, and better business results.
Increase the trust in your data by knowing the quality of the data.
Apply specific measurements on any kind of data by using basic measurements, data integrity analyses, or data quality rules. Create your own hierarchy of measurement rules based on their importance using various data sources. Enable rapid discovery and triggering of new issues.
Adjust dashboards according to your individual needs and find out, at a glance, the current state of your data. Watch how data patterns have evolved in real time, track data quality trends, and see future projections.
Get information about which individual records failed the condition. You can gather the records from each run and keep the history or get them on demand.
Connect to all existing database platforms and measure the data quality on them. Use the command line to launch and receive results for data quality checks. Track down and investigate the data quality issue and act to find, fix, and close the problem. Receive notifications if something goes wrong and eliminate critical situations.
Thanks to the awareness Accurity helps spread about data quality, we no longer encounter the data quality issues that we fixed in the past. Data quality rules have become enshrined in the data entry process. I firmly believe that if we had rules over all our data, there would be no bad data quality in our reporting whatsoever.
Get your demo to see how Accurity can help to increase trust in data and improve business decisions thanks to high-quality data.