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Data is any company's lifeblood. If the data can't be accessed, or is slow to be accessed, or is of poor quality when it arrives, the company pays the price. SOA provides access points for common functions so that they can be reused in multiple business processes throughout an enterprise, but the essence of what those processes are sharing is data. One of the key benefits of embarking on SOA is that you can treat data sources and applications that store and act on data as services and combine them into composite applications. This provides the company with unparalleled data access, efficiency and resiliency to change.

The problem: then you are dependent on the quality of the source data and may have limited insight into all the relevant definitions of and limitations of how it's been described. While services that participate in SOA are supposed to be self-describing, there are no standards for how deeply the real meaning of the data is described. As an example, if a customer's name is entered into the system, and an address requested, that data could easily reside in a dozen different data silos. Each one could have a slightly different view of what a customer means. While one would return a company's Texas location as the address for that customer, another might return the company's California address and still another might return the CEO's home address. Data governance, including management of metadata descriptions, is the key to knowing which address is the right one to return for this particular business process requestor and for hundreds of other similar situations.

Even that example assumes all three addresses are complete and correct. Another aspect of data governance is data quality monitoring. Data quality is of extreme importance to every business process and to a company's success as a whole. In a SOA, data quality is even more essential. Any errors in the data will be visible globally across the enterprise by any consumer that uses the service that pulls information from the faulty data source. Using the above example, if invoices, bills or products are consistently sent to the wrong address, the company will lose a lot of business. Data quality evaluations to find anomalous data, and manual or automated remediation of that data, must be an integral part of any really useful SOA plan. The consumer needs to be able to trust that the data they request from the service will be both correct and relevant to their current need.


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