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Scientifically, data architecture refers to the method of designing, planning and implementing an integrated data resources which is driven by business rules and based on the real world entities, activities and processes which are being represented as objects perceived by the organization.

The data architecture is what provides the guide for the overall structure of the data resources so that the organization can have a consistent foundation for identifiable, readily available, high-quality data to support the business information demand.

A generic data architecture could be likened to a template for which very specific processes are implemented from the real world into its digital equivalent. It is designed such that it is very specific to a purpose but it does not detail anything about a particular company framework.

To illustrate this, it was mentioned earlier that a generic data architecture may be designed for the specific purpose of student registration. It could be that different schools and universities have different ways of implementing the same process of student registration.

For instance one religious university in New York may require a student who is registering with data about the marriage background of parents and family history. But the same student registration process in a university in Paris does not require such data as that asked for in the New York University.

Therefore, generic data architecture should be designed as a general framework for the student registration process and if any of the two universities want to use the generic data architecture, it is up to them to add their own specification that suit their needs.

They may even have different processes wherein some processes are found in one university while not in the other. And yet they are both implementing the same process which is the student registration.

The design for a generic data architecture, like the common data architecture, falls under the belt of data architects who define the target state and align business development and make constant monitoring to ensure that the blue print is optimized and strictly followed by the people implementing the business processes.

The design of generic data architecture is still the same as any other data architecture minus the specific implementation needs of companies. As such, a generic data architecture undergoes the process of conceptualization, which takes care of representing business entities; logical representation, which handles the formation of logic on how entities are related; and physical implementation, the realization of data mechanism to specific function business functionalities. There are still the three general requirements in designing common data architecture which are the organizational requirements and technology requirements.

The very main purpose of the generic data architecture is for business organizations to have standards way of running the business and the way the processes are carried out digitally.

Having standards can also make it a lot easier for business solutions vendors to develop software applications with a generic framework bundled with specific configurations tools and interfaces.

Since business organizations can operate in many different industries such as mining, manufacturing, logging and many others, creating generic data architectures for this creates streamlining of processes for optimization.

For in-house IT developers, having a generic data architecture expedites the systems analysis and design process and creates a smooth flow of the data resources implementation.

For data warehouse implementation, a generic data architecture makes it easy to scale the system as the business organizations and the process and data needs become more complex.