How does Qualytics work?
How does Qualytics work?
Manage data quality at scale
through advanced automation.
Inferred and Authored Checks
Both inferred and authored Data Quality Checks are easily managed within Qualytics. Adjust parameters with intuitive controls and apply tags to trigger downstream actions in response to the identification of an anomaly.
Complex Authored Checks
Qualytics can support complex rule creation including detecting if specific rows in a selected table or field exist in a compared table/field in your Datastore.
Anomaly Detection
Review identified Anomalies with all relevant contextual information including the anomalous record’s values and the details of the triggering data quality checks.
Suggested Values
When your historic data suggest the correct value for an anomalous value, Qualytics will present it as a suggestion. These suggested values are highlighted in context of anomaly review, as well as alongside the anomaly in your enrichment store for downstream corrective actions.
Row and Column Level Anomaly Detection
Qualytics detects row-level (record) and column-level (shape) anomalies. Shape anomalies may originate from a shape check or a rollup of multiple record anomalies generated by the same check.
Anomaly Status
Dealing with false positives at scale can be tedious. Qualytics gives users the ability to give feedback to supervised learning methods behind anomalies, constantly learning while maintaining a worklist of anomalies for end users.
Data Confidence
stage in the data lifecycle.
Data Quality Checks
-
Profile historic data to infer
85% of rules via machine
learning -
Author unique rules for your
business needs -
Rules evolve over time
based on your feedback
Anomaly Detection
- Run scans on actuals to detect anomalous records
-
Identify both record and
column level anomalies -
Mark anomaly status to adjust/tighten/loosen
tolerances of the checks
Remediation
- Tags are utilized to drive notifications
-
Trigger downstream workflows
via multiple integrations -
Ensure future accuracy and reliability of data by solving
issues at the source
Table Views
Robust metadata based on historic actuals enables data quality checks to be contextual and specific to fields at great depth.
SLAs
Freshness tracking is a core aspect of data quality - define SLAs for your data or allow Qualytics to auto-detect SLAs that are then monitored for freshness adherence.
Remediation Triggers
An anomaly is often a very important signal. Drive your downstream workflows with tags, notifications and contextual payloads.
Insights
Integrations
Qualytics fits seamlessly into your data stack.
