Data and Information Inventory and Attributes - Classification attributes for the Data and Information Inventory
Data and Information Inventory and Attributes
Classification attributes for the Data and Information Inventory
Classification attributes position each Data and Information type within the enterprise data taxonomy — its structural form, governance category, business domain, and sensitivity profile.
| Attribute Name | Maturity | Description and Notes |
| Structure | Crawl | Description — The intrinsic structural form of this Data and Information type. Three valid values: Structured (machine-readable with a defined schema — relational records, CSV files, EDI transactions, JSON with enforced schema), Semi-Structured (self-describing but without a rigid schema — XML, Avro, Parquet, variable-field JSON documents), or Unstructured (no predefined schema or machine-readable structure — PDF documents, images, audio recordings, video files, email bodies, binary files). Describes the type itself, not the technical asset housing it. Benefit(s) — Enables architectural decisions appropriate to the structural form. Structured types warrant different storage, processing, and governance patterns than Unstructured types. A single Data and Information type may appear in multiple structural forms depending on context — record the dominant or governing form. Source — Manual. Examples — Structured (Customer Invoice as EDI X12 transaction), Semi-Structured (Product Catalog as JSON), Unstructured (Contract Document as PDF) Notes — Valid values: Structured | Semi-Structured | Unstructured. Structure is independent of the technical asset housing the data — a Customer Invoice is Structured whether it lives in a relational database, an S3 bucket, or a file system. The physical housing is captured in the Housing Data Stores relationship attribute. |
| Data Category | Crawl | Description — The governance category of this Data and Information type, determining its ownership model, quality standards, lifecycle characteristics, and change management requirements. Benefit(s) — Different categories require fundamentally different governance approaches. Master Data requires a single authoritative source and strict change control. Reference Data requires broad accessibility and version management. Transactional Data requires immutability and audit trails. Metadata requires automated population and cross-system consistency. Source — Manual. Examples — Master Data, Reference Data, Transactional Data, Analytical Data, Operational Data, Unstructured Information, Metadata Notes — Valid values: Master Data (core business entities — Customer, Product, Vendor, Employee), Reference Data (lookup values and classifications — Country Codes, Currency Codes, Status Values), Transactional Data (business event records — Orders, Invoices, Payments), Analytical Data (derived and aggregated — KPIs, Reports, Dashboards), Operational Data (system-generated operational records — Logs, Alerts, Metrics), Unstructured Information (documents, images, audio, video), Metadata (data about data — schemas, lineage, data quality scores). |
| Data Domain | Crawl | Description — The business domain or functional area this Data and Information type belongs to. Enables domain-level data governance, ownership assignment, and portfolio analysis. Benefit(s) — Groups related Data and Information types under a common governance umbrella. Enables the enterprise to assess data coverage, quality, and risk by domain. Domain-level analysis reveals which business areas are well-governed and which are data governance blind spots. Source — Manual. Examples — Finance, Customer, Product, Operations, Human Resources, Risk, Regulatory, Technology, Supply Chain, Legal Notes — Use the enterprise’s standard domain taxonomy where one exists. If none exists, use the examples above as a starting point and extend as needed. |
Sensitivity Classification [Multi-Value] | Crawl | Description — The sensitivity classifications applicable to this Data and Information type, referencing the Data Sensitivity Types Inventory. A type may carry multiple classifications simultaneously. Benefit(s) — The most governance-critical classification attribute in this inventory. Sensitivity Classification drives encryption requirements, access control, retention policy, disposal method, regulatory compliance scope, and cross-border transfer restrictions. Without it, no data governance decision can be made responsibly. Source — Manual. Examples — PII; PHI; PCI; Confidential; None Notes — Reference the Data Sensitivity Types Inventory for valid values and definitions. Separate multiple values with semicolons. None is an explicit governance statement — a type classified as None has been affirmatively assessed as carrying no sensitive data. Do not leave this attribute blank. |
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