Begin with business processes and then progress into leading-edge technologies
Topic: Data Integration
SOA is merely one of many application architectures. Data models are required for any and all applications, not just SOA, and that is the point. The assertion that SOA requires MDM first is obvious. The problem is that nobody wants to fund MDM until it's too late. They plod along without it until such a mess is made, that they finally must bite the bullet and begin some sort of MDM initiative. But it's much more costly to clean up a mess, than it would have been to have had MDM all along. That's why MDM usually fails. It's a lot of work for no immediate payoff, and the first project that includes MDM takes on the brunt of the work. Now, who wants to be first?
Topic: SOA
SOA uses interoperable services grouped around business processes to ease data integration
Blog: A Fresh Look at SOA Governance
Article: Joe McKendrick on The Evolution of SOA
White Paper: Driving Business Agility Through SOA Connectivity and Integration
Related Topics
Data Integration, Data Modeling and Visualization, Master Data Management
SOA for Dummies: IBM® Limited Edition Mini eBookThis eBook introduces you to the basics of SOA in context with the real-life experiences of seven companies, demonstrating that SOA allows you to work smarter and optimize costs for more significant business success.
Driving Business Agility Through SOA Connectivity and IntegrationThis white paper shows how a service oriented architecture (SOA) helps align the infrastructure with business needs in order to achieve maximum flexibility.

The technology tips and tools to enhance your ability to respond to business change with ease and success.

Learn more about this middleware layer that pools and dynamically provisions infrastruction application delivery resources to lower costs and improve efficiency.

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I totally agree that serious data modeling has to be done and that this must drive the data architecture.
SOA cannot be properly delivered without this. It is essential in defining what the transactional and non-transactional data structures should be.
Crazily, instead of doing proper data modeling an ensuring that this drives the data architecture, tools are now being developed to perform data normalization on raw data in an effort to manage this. A return to the 1960s and 70s and Relational Data Analysis.
Power and elegance come from simplicity.