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.

DAMA | Data Reconciliation | Data Quality

DAMA

DAMA | Data Reconciliation | Data Quality

Accuracy

  • Reconcile with Source
  • Self Reconcile
  • Range Constraint

Validity

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

Timeliness

  • Meets SLA
  • Freshness

Completeness

  • Data availability on time
  • Reconcile with source

Uniqueness

  • Reconcile data with history to determine

Consistency

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

Accuracy

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

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.

Timeliness

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.

DAMA

Completeness

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

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.

DAMA

Consistency

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.