An  Assurance System is a Management System aimed at ensuring enterprise compliance, resilience, and risk mitigation against both internal and external Policys and threats. It encompasses processes, Directives and technologies that work in concert to validate enterprise adherence to policy requirements, industry standards, and internal policies while simultaneously bolstering the enterprise's ability to withstand and adapt to various challenges and disruptions.
ensuring enterprise compliance and resilience against internal and external constraints:

a. Regulation constraints: they defined what is allowed and not allowed by the law (See  Regulation Article).

b. Internal policies and rules constraints: they defined what is allowed and not allowed by the enterprise (see Business Policy).

c. Operational constraints: they maintain operational capacities of the company (maintain ability to produce, maintain quality, ensure product development , ability to hire, to train, etc, see Business Rule).

d. Architectural constraints: they guide design decisions and shape the overall structure of a system (see  Architecture principle).

A  Data Assurance Instrument is a resource or course of actions used by an Data Catalog to achieve its objectives.

For instance:

Actions plans are course of actions aimed at solving Data breaches.

Data Controls are mechanisms used to ensure data quality and data integrity

Data Processors are used to processings involved in Data Lineages.

A  Data Lineage is about tracking the flow of information from source Information Assets to final Information Assets.

It is necessary to guarantee the quality, usability and security of business data.

For large organizations, it is also a key conformity legal requirement, for instance in BCBS 239 and Solvency II.

Business Data Lineage is defined as a business data life cycle that describes the source of business data and where it moves over time.

A  Data Processor is the collection and manipulation of items of data to produce meaningful information.

In this sense it can be considered a subset of information processing, the change (processing) of information in any manner detectable by an observer.

An  Information Asset  represents anything that can be communicated or memorized by an Agent to produce and react to Outcome Events.

An  Information Asset is either an Information Entity or an  Information Property.
The difference lies in their relationship to change and to time. Information Entity(ies) can change over time and have a lifeycle while  Information Propertys are immutable characteristics.

A  Management System is a mezzo Enduring Initiative within an Enterprise, aimed at creating, maintaining, evaluating, evolving, and operating a collection of essential  Functional Architecture Assets of the Enterprise.

A  Management System may transcend organisational boundaries and consequently requires an integrated team working under the direction of a Management Initiative Committee.

An  Organization is a group of people who share a common purpose and establish a functional division of labor in pursuit of their common purpose.

It is the relationships between its members in the pursuit of their common purpose that give unity and identity to an organization.

A  Policy is a Directive that is not directly enforceable whose purpose is to govern, guide or constrain the structure and Behavior of Agents in the enterprise.

Policies provide the basis for rules and govern Behaviors carried out by Agents.

Example of Policys are Regulation Articles, Business Policys or Architecture principles.

A  Business Object Assurance Case is a Data Assurance Case related to a critial Conceptual Entity Asset.

Its purpose is to defined the quality and integrity rules that apply to a Conceptual Entity Asset in each business contexts such as activity domains on the enterprise.

A  Data Catalog is an Assurance System of  Data Assets, ensuring understanding,  trust, compliance and confidence of enterprise data. This includes:

1. Relationship with Enterprise Glossary to provide business context to metadata.

2. Data policy definition and enforcement to ensure data quality.

3. Data Lineage to master data provenance: where data comes from, how data is transformed, and where it is used.