Data Architecture
Edit on GitHubData architecture is composed of models, policies and rules or standards that govern which data is collected, data relationships, how it is stored, arranged, integrated, and processed.
Why
Data architecture provides meaning to all the data that is collected. This gives the ability to the system to work with accurate information efficiently to solve the business problem. In addition to that, it gives a new opportunity to analyze and predict trends in information and to generate business insights.
How
A successful enterprise needs to have a top-down coherent data architecture, designed based on the business processes and operations. Usually, there are three different layers for a data architecture.
Conceptual Level Data Architecture Design based on Business Process and Operations
- The core data entities and data elements such as those about customers, products, sales.
- The output data needed by the clients and customers.
- Security policies to be applied to each data entity.
- The relationships between the data entities, such as reference integrity, business rules, execution sequence.
- Standards of data quality, operations, and Service Level Agreements (SLAs).
Logical Level Data Architecture Design
- The type of database and the data format to be used. E.g. NoSQL, Relational
- The naming conventions and data integrity.
- Data archival/retention policies.
- Privacy and security information.
- Data Replications.
- Data Flows and Pipelines.
Physical/technology model
- Provides the data mechanism for a specific process and functionality, or how the actual data architecture is implemented on underlying technology infrastructure. Also considers the different dimensions of collected data, such as time, etc. to use for analytical purposes later.