How does the ANSI/SPARC architecture model relate to data architecture?

1 External level

The external level of the ANSI/SPARC model defines how different users and applications view and interact with data. It consists of multiple schemas, each representing a subset of data relevant to a specific purpose or context. For example, a salesperson might see a customer's name, address, and purchase history, while an accountant might see their invoice number, payment status, and tax details. The external level allows data to be customized and tailored to different needs and preferences, without affecting the underlying data structure or integrity. Data architects can use the external level to design user-friendly and efficient data interfaces and access methods.

2 Conceptual level

The conceptual level of the ANSI/SPARC model defines the logical structure and meaning of data, independent of any physical implementation or user view. It consists of a single schema that represents the entire data domain of an organization, including entities, attributes, relationships, constraints, and rules. For example, a conceptual schema might define a customer as an entity with attributes such as name, address, phone number, and email, and a relationship with another entity called order. The conceptual level provides a consistent and comprehensive data model that captures the business logic and semantics of data. Data architects can use the conceptual level to design data standards, quality, and governance policies and procedures.

3 Internal level

The internal level of the ANSI/SPARC model defines the physical storage and organization of data, taking into account the characteristics and limitations of the hardware and software systems. It consists of one or more schemas that specify how data is stored in files, tables, indexes, partitions, clusters, and other structures. For example, an internal schema might define how a customer entity is mapped to a row in a relational database table, or how an order entity is stored in a document-oriented database collection. The internal level provides a low-level data model that optimizes data performance, availability, and security. Data architects can use the internal level to design data architectures that meet the technical requirements and constraints of the data environment.

4 Data independence

One of the main benefits of the ANSI/SPARC model is that it supports data independence, which means that changes in one level do not affect the other levels. For instance, adding a new attribute to a customer entity in the conceptual level does not require modifying the existing external schemas or internal schemas, as long as the mapping between them is maintained. Similarly, changing the physical storage format of a customer entity in the internal level does not affect the logical definition or user view of the entity in the conceptual or external levels. Data independence enables data architects to manage data changes more easily and flexibly, without compromising data quality or consistency.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?