4DAlert & DAMA

4DAlert follows DAMA-DMBOK Functional Framework and delivers a standard industry view of data quality management functions, terminology and best practices. 4DAlert compliments organization’s data architecture, data assets, and provides a blueprint for managing highest standard of data quality. 4DAlert’s predefined dashboards ensure that business users have visibility into data quality and data meets business needs for information.

Awesome Image


Enumeration Check against master data
Number Check
Invalid character
Number meets a certain range


Data reconciliation across layers of objects
Data reconciles with historical trend
Alerts when outlier are spotted.


Reconcile with Source
Self Reconcile
Range Constraint


Meets SLA


Data availability on time
Reconcile with source


Reconcile data with history to determine


The level of accuracy with which data represents the real-world scenario and is confirmed by a verifiable source. Data accuracy ensures that the associated real-world entities can participate as intended.


Validity denotes the availability of value attributes for alignment with a specific domain or requirement. It helps for enumeration checks against master data and identifies anomalies in the data set.


Using Timelines, 4DAlert verifies whether the object meets SLA or not. According to mentioned refreshed SLA for each object, 4DAlert determines the freshness of an object. Timelines help in detecting data anomalies in time.


Completeness helps business to make sure that important attributes are available on time. It determines whether the data is sufficient to make meaningful inferences and decisions.


Uniqueness helps ensure no duplication or overlaps in the data sets. Data uniqueness is measured against all records within a data set or across data sets. A high uniqueness score ensures that duplicates or overlaps are minimized, increasing trust in data and analysis.


Consistency checks if the same information is stored and used in multiple instances. It is expressed as a percentage of values that matches across multiple records. Data consistency ensures that analytics capture and leverage the value of data correctly.