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SOA: Integrating Data
SOA is commonly used to knit together heterogeneous data sources. But as is often the case, the theory and potential for a technology is often far different than what is observed in practice.
Very often project run into a kind of data impedance running between the different data sources. Problems can result when:
- Data Structures and service versions change frequently
- Data Quality isn’t consistent and is difficult to measure
- Data Sources are distributed both internal and external to the company
Non-uniformity of data and formats can make it necessary to write very data-specific transformation algorithms and the specificity of the coding can cause software to be brittle and subject to failure. This is in fact a weakness of traditional data integration that SOA seeks to solve.
How to overcome this? Laurie Newman in an ITNews column suggests that SOA architects consider the following data integration elements during in their design:
- Data Semantics: The business context behind data definitions for concepts such as customer address, product category, or employee type
- Data Quality: Improving the accuracy and consistency of data moving between heterogeneous systems
- Data Governance: Data and metadata management, documentation, reporting, and auditing tools
- Data Access: Tools to untap structured, semi-structured, and unstructured data in hierarchical and relational databases, mainframe systems, files and documents, and applications
Newman argues that without sophisticated Data management tools SOA projects will suffer.
