CDMP Changed DQ-1220 Questions

The activity is Standardization. Standardization converts semantically equivalent values into a consistent representation so they can be compared, matched, integrated, and interpreted reliably.

In the scenario, Street, St., and Str may represent the same address concept but differ syntactically. Converting them to an approved standard reduces superficial variation before entity matching.

Standardization is commonly used for names, addresses, telephone numbers, units of measure, dates, product descriptions, geographic references, abbreviations, and coded values. It frequently precedes matching and de-duplication because entity-resolution algorithms perform more effectively when predictable representational differences have already been normalized.

Reference Data and Metadata Management are important supporting disciplines. Reference Data can define approved abbreviations or formats, while metadata documents transformation rules and intended meaning.

Standardization does not prove that the resulting address is factually accurate. It improves consistency and comparability. An address can be perfectly standardized yet belong to the wrong customer.

Therefore, standardization should be combined with validation, verification, matching, and source-improvement controls where appropriate.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Standardization; Cleansing; Matching; Consistency; Reference Data Management.

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The strongest indicator is the uncontrolled proliferation of multiple integration technologies. Enterprise integration environments often become progressively more complex when different projects independently adopt different ETL platforms, messaging products, APIs, middleware technologies, replication mechanisms, file-transfer approaches, and proprietary interfaces.

The problem is architectural fragmentation. Each integration technology introduces its own configuration methods, transformation logic, monitoring mechanisms, metadata, failure handling, security controls, support skills, and operational procedures. As the number of technologies increases, point-to-point dependencies multiply and the organization accumulates technical debt. This makes end-to-end lineage, troubleshooting, change-impact analysis, and consistent application of Data Quality rules significantly harder. The source question itself identifies this specific integration-management issue. DAMA-oriented exam material likewise identifies multiple integration technologies as the characteristic cause of an overly complex integration landscape.

DAMA-DMBOK2 therefore emphasizes managed Data Integration and Interoperability architecture, reusable patterns, common models, governed interfaces, and metadata describing transformations.

From a Data Quality perspective, uncontrolled integration technology can result in inconsistent transformations, duplicated cleansing rules, mismatched reference values, and poorly understood lineage. Standardization reduces these risks by making movement and transformation processes more transparent and governable.

Reference Topics: DAMA-DMBOK2 Chapter 8 — Data Integration and Interoperability; Integration Architecture; Metadata and Lineage; Chapter 13 — Quality Controls Across Data Movement.

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A document-management search function depends heavily on metadata to identify, index, classify, and retrieve stored content. If known artefacts exist in the repository but cannot be found through search, inadequate or incorrectly maintained metadata is the most direct explanation.

DAMA-DMBOK2 distinguishes document content from the metadata that describes it. Typical descriptive metadata includes title, author, subject, keywords, document type, creation date, classification, and other characteristics used by retrieval mechanisms. Administrative and structural metadata may additionally support lifecycle management, versioning, access control, and relationships among document components. Without sufficiently accurate and complete metadata, the repository may physically contain a document while users remain unable to discover it.

This is also a Data Quality problem. Metadata itself is data and must satisfy quality expectations such as completeness, validity, consistency, and accuracy. A missing subject classification, incorrect document type, or inconsistent keyword convention can directly reduce findability.

Public access is neither required nor desirable for all documents, especially where confidential information exists. Business Intelligence is unrelated to basic document discovery, and Data Quality metrics alone do not make a document searchable unless the underlying metadata is correctly populated.

Reference Topics: DAMA-DMBOK2 — Document and Content Management; Metadata Management; Descriptive Metadata; Search and Retrieval; Chapter 13 — Metadata Quality and Fitness for Purpose.

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Periodic archiving is critical for maintaining database performance. As a production CRM accumulates historical transactions, active tables and indexes can become increasingly large. This increases storage consumption, backup duration, index-maintenance overhead, and the quantity of data that database engines must process during operational queries.

DAMA-DMBOK2 treats archiving as an important Data Storage and Operations activity. Historical information that remains subject to retention requirements but is no longer frequently needed for operational processing can be moved to suitable archival storage. DAMA-aligned guidance for this scenario specifically links periodic transaction archiving with maintaining production database performance.

Archiving is not the same as arbitrary deletion. Retention policies, legal obligations, recovery requirements, auditability, and business value determine how long data must remain accessible and where it should be stored. The archive must also be recoverable and appropriately secured.

Data Quality implications include maintaining integrity and traceability during migration to the archive. Records should remain complete, relationships should be preserved, and metadata should indicate retention status and archival location.

Providing reporting sources or managing deleted customers may be secondary considerations, but neither is the principal purpose described in the question.

Reference Topics: DAMA-DMBOK2 Chapter 6 — Data Storage and Operations; Archiving; Database Performance; Retention; Chapter 13 — Integrity and Historical Data.

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