state architecture. The functional decomposition and the data flow diagrams apply to
applications. The taxonomy/ontology applies to text. The corporate data model applies to
the data warehouse. The dimensional model applies to data marts. And the selective
subdivision of data applies to the data lake.

Each of these forms of data modeling has their own idiosyncrasies. Each form of data
modeling has a certain similarity to the other forms of data modeling. And each form of
data modeling is required in order to build an effective end-state architecture.

Proactive/Reactive Data Models

One of the interesting features of the end-state architecture is the ability of the analyst to
traverse and communicate from one form of data modeling to another. In other words,
when an analyst is working on the corporate data model, the analyst can go look at what
the data flow diagrams look like. Or when an analyst is working on a taxonomy, the
analyst can look at the corporate data model. Or when an analyst is assigning the
selective subdivision of data, the analyst can go look at the functional decomposition of
data.

The ability to traverse the network of information of data formed by the different forms
of data modeling in the end-state architecture is a very important feature. By being able
to traverse the network formed by the different forms of data modeling, the analyst can
find and examine the lineage of the data. By examining the lineage of data, the analyst
can understand such things as the following:

Where did the data come from?

What data were chosen?

What data were not chosen?

What calculations were made on the data?

When were the calculations applied?

In a word, the ability to be able to traverse the network of the different forms of data
models in the end-state architecture is one of the more important features of the end-state
architecture.

Fig. 14.1.19 shows the network.

Chapter 14.1: Data Models Across the End-State Architecture