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November 9, 2009

Whom to Blame for no Success in Data Integration?

Filed under: Data Integration, Data Quality — Olga Belokurskaya @ 9:03 am

I’ve recently met an interesting thought about the source of issues in data integration at an article by Noreen Kendle, a data management analyst.

Very often we hear and read about data integration failures and challenges companies face when starting data integration initiatives. According to the article, the thing is that the typical tools/technology approach of merging data from various database systems doesn’t work due to several things, such as:

Methodology – Tools simply do their job on merging based on data field names or content. Assuming field names may be misleading, you can imagine the mess that may come out of it.

Dimensions of time – Time bring changes. This is true about data as well: data meaning changes over time, so two items that seem identical, but represented by different time mark may mean rather different things.

Capturing data relationships – The full meaning of data may be lost if the data is viewed individually, with no reference to its relations with other data. There should be relative meaning of data items preserved somehow in order to preserve data integrity.

So, the better is the data name quality, content, and structure/relationships the better tools/technology approach to data integration works. But all this is insufficient without understanding of the business meaning of data, i.e. the data should be mapped to a business data model, and only then it is ready for data integration.
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