Getting Started with IBM Data Studio for DB2

314 Getting Started with IBM Data Studio for DB2
11.1 Data lifecycle management: The big picture
Figure 11.1 illustrates the data management lifecycle phases and key value that a data
lifecycle management approach can bring to those phases.
Figure 11.1 -- Managing data across the lifecycle can enhance value and productivity
from requirements through retirement
As you can see in Figure 11.1, there are many considerations for effective management of
data, databases, and data applications. As described by Holly Hayes in her
developerWorks article entitled “Integrated Data Management: Managing data across its
lifecycle”, the main steps involved in the complete data lifecycle include:
Design -- Discover, harvest, model, and relate information to drive a common semantic
understanding of the business. You may need to interact with business users to track
and gather requirements and translate those requirements into a logical design to
share with application architects. A physical database model is generally used as the
way to convert a logical design to a physical implementation that can be deployed into
a database management system. If you are working with existing data assets
(databases), you need to understand what tables already exist, and how they may
relate to other tables or new tables you may want to create. In addition, you may wish
to conform to a naming standard or enforce certain rules about what kind of data can
be stored in a field or whether data stored in a field must be masked for privacy. All of
these considerations happen during the design phase of the lifecycle.