Data Architecture - quick overview

Over the past couple weeks, i have talked about Enterprise Architecture as an overview, the various layers within it, and about application architecture. In this weeks blog, i will discuss about Data Architecture. As the name suggests, data architecture is the way data is organized. Just as the application architectural layer is important, for this layer to work we need data. That is why I feel it is very essential to have a well organized and consistent data flow throughout the architectural layers in order to maintain consistency overall. It is essentially a lifeline to the entire enterprise.

Data - lifeline to all the architectural layers

This brings me to the other topic where data is either in house production or is being fed by the dependent applications. In order to ensure that we are getting a constant supply of data from the outside systems, it is very important to have a consistent and systematic infrastructure in place. The way we can achieve this by proper planning using data models and the other tools that are available in the marketplace. Here is a quick wiki link to the various data modeling tools and their comparisons.

Since the data architectural layers value is in its planning and predictability model, the data architect divides the information into 3 architectural processes as noted in my classroom readings;

• Conceptual
• Logical
• Physical

where all three processes represent three different components, such as conceptual is for business entities; logical is the logic behind these entities interaction; and physical is the meaning behind where and how are we sharing and storing data (servers, history, integration, analytics, etc.)