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.