Data and Information Inventory and Attributes

Enterprise Architecture depends on this inventory as the data vocabulary of the Enterprise Model. Without governed Data and Information types, every architectural artifact — data flow diagrams, integration maps, capability models — uses informal terminology that means different things in different contexts. The same class of content is called “customer data” in one diagram and “account information” in another, making cross-artifact analysis unreliable and AI-assisted enterprise reasoning impossible. This inventory provides the stable, governed vocabulary that makes Enterprise Model traversal precise.

Application Portfolio Management (APM) depends on this inventory to understand what data each application owns and produces. Without governed Data and Information types, APM cannot answer: which application is the system of record for this class of data? Which applications would be affected by a change to this data type’s definition? Which rationalization candidates are systems of record for high-sensitivity types that cannot be retired without a migration plan? These questions drive defensible portfolio investment decisions and cannot be answered without this inventory.

Technology Portfolio Management (TPM) depends on this inventory to govern the technology choices that store and process each data type. Storage architecture decisions, database technology selections, and data pipeline technology choices are all influenced by the Structure, Volume, and Velocity characteristics of the data types being processed. Sensitivity Classification drives encryption technology requirements. Regulatory Obligations drive data residency and sovereignty technology constraints. Without this inventory, TPM makes technology decisions without a complete picture of what the technology must handle.

The Enterprise Model depends on this inventory as the data layer of the Enterprise Model graph. Every Data and Information type is a node in the graph, connected to the Capabilities that produce and consume it, the Integrations that carry it, the Applications that own it, and the Data Stores that house it. These typed relationships are the edges that make the Enterprise Model graph traversable by AI agents: from a regulatory obligation, through the data types it governs, to the integrations that carry those types, to the applications and capabilities involved, in a single connected path. Without governed data type nodes, this traversal is impossible.