Data Quality - Cycle Overview

Dataquality Metrics

Data Quality - Cycle Overview


A typical use of data quality follow the cycle of :

  • Quality Assessment. Part of the process of refining data quality rules for proactive monitoring deals with establishing the relationship between recognized data flaws and business impacts.
  • Quality Design to detect the anomalies
  • Quality Transformation to correct (or not) the anomalies
  • Quality Monitoring to measure the data quality

Data Quality Cycle

Oracle Warehouse Builder Implementation

Specifically, the steps performed in Warehouse Builder to insure data quality are:

  • Starting at the 12 o’clock position, the metadata about data sources is captured.
  • Next, the data sources are profiled.
  • The data rules are then derived from data profiling or existing data rules are imported or entered manually.
  • Data flows (mappings) are designed utilizing name and address and match-merge operators
  • Data flows are combined in process flows, adding data auditors that measure data quality at any given point in the process
  • The processes are deployed and executed, transforming raw data into quality information
  • Information quality is, as a final step, continuously monitored in the operational environment by Warehouse Builder’s data auditor programs


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Dataquality Metrics
Data Quality

measures the quality of data through metrics and try to improve them. You will find it in two main domains : The management of attribute data with the Master Data Management (MDM) The management...

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