Many similarities exist between producing quality data and manufacturing quality products. Similarities such as quality measures, conformity to specifications, low defect rate, and improved user satisfaction.
If we go back to the development of the machine and the industrial mass production, we will find similarities to the advancement of the data processing power and the mass production of large volume of data (stored in data warehouses) that is widely distributed and easily accessed.
Faced by the reality of advancing data processing power, several organizations are opted or forced to manage large volume of data. Some organizations are motivated by the relatively cheap technology enabled their vision. Others are scared of the competition and the demand from data consumers for different views of data.
In reality, the standards, specifications, the engineering methods, and the personnel with the know-how are not advancing in parallel. This situation led to chaos in the quality of data presented to the data consumers in specially in the business or government operation paradigm.
This post will discuss the data quality within the context of application and data integration, which as argued has played a big role to uncover data quality chaos. I will examine the following aspects in data quality:
1) The current issues in data quality.
2) What is meant by quality data.
3) XML specifications and modeling techniques as a tagging solution to manage and control data quality.
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