One of the greatest challenges and most time-consuming efforts in a data warehouse project is integrating data between disparate data sources into a meaningful collection of information for users to make decisions. Significant resources need to be applied to understanding not only the data and structures, but also gathering and analyzing the meta data of source systems to fully appreciate the context of the information. Further efforts may also need to be utilized or developed if the data exists in nonstructured or proprietary formats, exasperating an already very complex process. Once this basic step of understanding and accessing your data sources is somewhat manageable, you need to determine the best approach to integrating this data. Depending on your business requirements and source system data state, integrating data from these systems can be exigent if common data columns exist between the applications. The task becomes even more difficult if common elements do not exist between source systems and new, inexact methods need to be designed and developed in order to meet business needs to integrate this data.
This column is the first part of a two-part series examining a data integration strategy for matching and integrating data between heterogeneous source systems when no common elements exist to facilitate the process. In this installment of the series, we will look at some typical data integration approaches and provide an example case. In the second part of this series, we will look at the logic needed to implement this data matching method and some of the details that need to be considered before attempting this process. We are making the assumption that the data quality and data integration product or method being used by your firm does not have this type of capability.
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