Ensuring data quality
Dealing with data quality problems is diverse and depends on the importance of the problem, the context of usage and the problem cause. Possibilities for ensuring data quality are mentioned as:
• Quality checks before the final loading into the Data Warehouse Data Base.
• Data Cleansing in the ETL process.
• Loading and tagging problematic data (e.g. referential integrity or value domains)
• Automatic correction and data cleansing (e.g. format errors).
• Manual correction and data cleansing (e.g. data interpretation; frequent the problems are already known by the domain expert).
• Feedback to data suppliers about test results (for possible data correction and further data delivery).
• Error location and co-ordination with data suppliers.
• Organisational approaches.
Interestingly not one enterprise listed the integration of the quality specification and quality measurement in the meta data management. If possible the data quality lacks should be reported to the data suppliers and improvement should start at their causes (proactive). A continuous contact between the central data warehouses and source systems is therefore useful.