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SOA Watch: Architects gain ground in data wars

By David S. Linthicum

October 15, 2008 — (Page 1 of 2)

I've always thought a lot about how data relates to architecture, especially architectural patterns such as SOA. While some would like to break them apart, I think SOA is architecture, and indeed the foundational architecture is always going to be information/data. To that end, I've always been an advocate for a Common Data Model (CDM) or forcing a semantic/data-level understanding of the domain, and then attempting a logical restructuring before that information is bound to services and/or processes.

However, as I speak to people about data governance and SOA, I'm often taken aback by the lack of understanding about how both notions relate to one another. While most thought leaders in this space agree, I still think that the rank-and-file SOA architect ignores their data. Why? In most cases, because it's a huge mess, while in other cases, it's due to ownership issues within the enterprise. Sometimes it's a lot of both.

So why is there an issue here? Say you’re building a SOA for the first time. You know you’ll have to collect services, either existing or new, and you know you’ll have to do something with those services to form solutions. But services are not much without the data they control, so clearly a large part of the architecture is dealing with the underlying information, either understanding, restructuring or abstracting.

The core issue is that a well-defined and well-designed SOA needs to consider the data, and possibly change the way the data is represented. This is done either through physical changes in the existing structures or schemas, or by leveraging a database abstraction layer that’s able to expose a well-designed schema no matter how messed up and un-normalized the existing data is. In any event, you’ll have to dig deep into the existing data, and that will require dealing with the owners.

Most architects don’t deal with the information due to ownership or political issues within the enterprise. Since data governance spans many organizations in many instances, they quickly find that turf issues around controlling the existing enterprise data can quickly become a problem. And these are problems that are more difficult to solve than any technology issues you’ll have.

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