Cloud Computing and SOA Convergence in Your Enterprise: A Step ...

Case Study: Defining the Data for Blue Mountain Hammocks 101
Product
Product_Number
Product_Description
Product_Size
Product_Weight
Product_Price
Sales
Customer_Number
Product_Number
Amount_Sold
Date_Sold
Again, it’s very simple to make our point here. Clearly, your problem domain
will deal with many more data elements, entities, and complexities, but the core
idea is the same.
The resulting information model, or the final outcome of this process, is less
complex and is easy to understand, but it also represents all of the information as
discovered in our current systems, including groupings of data around Customer,
Product, and Sales.
The idea is to move from the complex and ill defined to the logical and well
defined, thus having a complete understanding of the as-is and to-be states of
your data level before moving on to services and processes, covered next.
Data Cataloging
Data cataloging is about formalizing the information we gather in the previous
two steps (see Figure 5.6), including the data dictionary. The difference is
that the data dictionary is typically local to a single system or application,
whereas the data catalog spans all systems in the problem domain.
Catalog the Data
Data
Catalog
Figure 5.6 Before building the information model, we need to catalog all of the
data previously defined in the data dictionary. We do this to make it easier to
break out the data as we look to relocate some systems to the clouds.