How is data designed at architectural and component level?

Data Design at Architectural Level

Data design translates data objects defined during analysis model into data structures at the software component level and, when necessary,a database architecture at the application level.
There are small and large businesses that contains lot of data. There are dozens of databases that serve many applications comprising of lots of data. The aim is to extract useful information from data environment especially when the information desired is cross functional.
Techniques like data mining is used to extract useful information from raw data. However, data mining becomes difficult because f some factors:
- Existence of multiple databases.
- Different structures.
- Degree of detail contained with databases.Alternative solution is concept of data warehousing which adds an additional layer to data architecture. Data warehouse encompasses all data used by a business. A data warehouse is a large, independent database that serve the set of applications required by a business. Data warehouse is a separate data environment.

Data Design at Component Level

It focuses on representation of data structures that are directly accessed by one or more software components. Set of principles applicable to data design are:
- Systematic analysis principles applied to function and behavior should also be applied to data.
- All data structures and operations to be performed on each should be identified.
- The content of each data object should be defined through a mechanism that should be established.
- Low level data design decisions should be deferred until late in design process.
- A library of data structures and operations that are applied to them should be developed.
- The representation of data structure should only be known to those modules that can directly use the data contained within the structure.
- Software design and programming language should support the specification and realization of abstract data types.