Data Architecture is a set of rules, policies, standards and models that govern and define the type of data collected and how it is used, stored, managed and integrated within an organization and its database systems. It provides a formal approach to creating and managing the flow of data and how it is processed across an organization’s IT systems and applications.
Overview of Data Architecture
A data architecture should set data standards for all its data systems as a vision or a model of the eventual interactions between those data systems. Data integration, for example, should be dependent upon data architecture standards since data integration requires data interactions between two or more data systems. A data architecture, in part, describes the data structures used by a business and its computer applications software. Data architectures address data in storage, data in use and data in motion; descriptions of data stores, data groups and data items; and mappings of those data artifacts to data qualities, applications, locations etc.
Essential to realizing the target state, Data Architecture describes how data is processed, stored, and utilized in an information system. It provides criteria for data processing operations so as to make it possible to design data flows and also control the flow of data in the system.
The data architect is typically responsible for defining the target state, aligning during development and then following up to ensure enhancements are done in the spirit of the original blueprint.
During the definition of the target state, the Data Architecture breaks a subject down to the atomic level and then builds it back up to the desired form. The data architect breaks the subject down by going through 3 traditional architectural processes:
- Conceptual - represents all business entities.
- Logical - represents the logic of how entities are related.
- Physical - the realization of the data mechanisms for a specific type of functionality.
Data architecture should be defined in the planning phase of the design of a new data processing and storage system. The major types and sources of data necessary to support an enterprise should be identified in a manner that is complete, consistent, and understandable. The primary requirement at this stage is to define all of the relevant data entities, not to specify computer hardware items. A data entity is any real or abstracted thing about which an organization or individual wishes to store data.
Data Asset Framework (DAF)
Data Center Infrastructure
Data Center Infrastructure Management (DCIM)
Data Delivery Platform (DDP)
Data Description (Definition) Language (DDL)
Data Driven Organization
Data Flow Diagram
Data Health Check
Data Integration Framework (DIF)
Data Life Cycle
Data Loss Prevention (DLP)
Data Presentation Architecture
Data Protection Act
Data Quality Assessment (DQA)
Data Quality Dimension
Data Quality Standard
Data Reference Model (DRM)
Data Structure Diagram
Data Value Chain
Data Vault Modeling
Data and Information Reference Model (DRM)
Data as a Service (DaaS)
Database Design Methodology
Database Management System (DBMS)