Data and Information Inventory and Attributes - Technical attributes for the Data and Information Inventory
Data and Information Inventory and Attributes
Chapter 16. Technical attributes for the Data and Information Inventory
Executive Summary: Chapter Overview
IF4ITThe Bottom Line
Core Concepts
| Concept | Definition & Strategic Role |
|---|---|
| Format and Schema | Format and schema attributes describe how the data type is represented in files, messages, APIs, tables, or documents. They support integration planning, catalog alignment, and technical validation. |
| Technical Representation | Technical representation connects a governed type to implementation patterns without making the physical implementation authoritative for the type definition. This preserves the logical governance boundary. |
| Interface Context | Interface-related attributes identify how systems exchange or expose the data type. They help integration architects understand where format, payload, and contract decisions affect governed data. |
Quick Q&A
Question: How should technical attributes relate to the logical Data and Information type?
Read More Below
Technical attributes capture the formats, volumes, velocities, and catalog references that characterize how this Data and Information type is technically implemented and discovered.
| Attribute Name | Maturity | Description and Notes |
Common Formats [Multi-Value] | Walk | Description — The file, exchange, or encoding formats in which this Data and Information type commonly appears across the enterprise. Benefit(s) — Enables format-aware integration design and data pipeline architecture. Surfaces format diversity that may require transformation governance. Source — Manual. Examples — JSON; XML; CSV; Parquet; PDF; HL7 FHIR; EDI X12; Avro; Excel Notes — Informational rather than prescriptive — a type may appear in multiple formats depending on context. Separate multiple values with semicolons. |
| Typical Volume | Walk | Description — Order-of-magnitude estimate of how many instances of this type the enterprise generates, receives, or processes per day or month. Benefit(s) — Informs storage architecture, processing infrastructure sizing, and retention cost estimation. A type with billions of daily instances requires fundamentally different infrastructure than one with thousands. Source — Manual. Examples — Thousands per day (Regulatory Filings), Millions per day (Transaction Records), Billions per month (Clickstream Events) |
| Typical Velocity | Walk | Description — How frequently instances of this type are produced, updated, or received. Benefit(s) — Informs streaming vs. batch architecture decisions. Real-time types require fundamentally different pipeline design than periodic types. Source — Manual. Examples — Real-time (sub-second), Near-real-time (seconds to minutes), Hourly, Daily, Weekly, Periodic (ad hoc or scheduled) Notes — For types with variable velocity, record the governing or most common velocity. |
| Data Catalog Reference | Walk | Description — The identifier or link to the corresponding entry for this Data and Information type in the enterprise Data Catalog, where one exists. Benefit(s) — Connects the logical governance layer (this inventory) to the physical asset discovery layer (the Data Catalog). Enables practitioners to navigate from a governed Data and Information type to its technical metadata, schema details, and physical asset locations in the catalog without manual search. Source — Manual. Examples — Collibra Asset ID: 7f3a-2b91, Alation Article: /data/customer-profile, Microsoft Purview: Customer_Profile_v2, AWS Glue: customer_profile_table Notes — The Data and Information Inventory is the governance vocabulary layer; the Data Catalog is the physical asset discovery layer. These are complementary, not competing instruments. See the Data and Information Governance Context section of this document for a full discussion of the relationship between this inventory and the enterprise Data Catalog. |
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