CDMP DQ-1220 Passing Score

The primary failure is Timeliness. The transaction may be completely accurate and complete, but it is not available within the period required by the consuming business process.

Timeliness evaluates whether data is available when needed for its intended use. The relevant threshold must therefore come from the business requirement rather than from an arbitrary technical target. In this scenario, the fraud-monitoring process requires the transaction within five minutes, while delivery occurs approximately nine hours later.

The root cause could exist in extraction frequency, integration queues, batch processing, network delays, source-system availability, or downstream ingestion. Lineage and operational metadata should be used to identify where the latency occurs.

Timeliness must also be distinguished from Currency. Currency asks whether information reflects a sufficiently recent real-world state; Timeliness asks whether data is delivered or available within the required period. A current transaction that arrives too late can therefore fail Timeliness even though the underlying value accurately represented reality when captured.

DAMA's revised DMBOK2 Chapter 13 recognizes both Timeliness and Currency as separate standard dimensions.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Timeliness; Currency; Data Quality Requirements; Data Integration; Operational Monitoring.

===============

The failed control is principally Reasonableness. DAMA treats reasonableness as the extent to which data values or patterns conform to credible expectations. The current DMBOK2 revision recognizes Reasonableness among the standard Data Quality dimensions, alongside Accuracy, Validity, Completeness, Integrity, Uniqueness, Timeliness, Currency and Consistency.

A $100,000,000 residential electricity bill may be syntactically valid: the field may accept the number, the record may satisfy referential constraints, and the value may have arrived on time. Nevertheless, it is grossly inconsistent with the customer's historical pattern of approximately $300 per quarter. A reasonableness control would therefore compare the amount with expected ranges, historical consumption, statistical limits, peer-group distributions, or business thresholds and flag the value before billing.

This illustrates an important distinction from Accuracy. Accuracy asks whether the stored value correctly represents reality. Reasonableness asks whether the value is credible in context. The absurd magnitude itself is detectable without first obtaining an independently verified “true” bill value.

Practical controls include range checks, deviation-from-history rules, z-score/outlier detection, control limits, and exception thresholds. Such rules should be governed as metadata and owned by an appropriate Data Steward.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Data Quality Dimensions; Reasonableness; Measurement and Monitoring; Statistical Process Control; Data Quality Rules.

===============

DAMA-DMBOK2 defines obfuscation or redaction as making information anonymous or removing sensitive information. The purpose is to reduce the risk that protected, confidential, or personally identifiable information can be exposed to users or processes that do not require access to the original values.

Obfuscation can involve masking, substitution, shuffling, temporal variation, partial display, or other methods that change what the recipient sees while preserving sufficient utility for the intended activity. For example, a customer-service agent may see only the final digits of an account identifier, or a development team may receive realistic but anonymized production-derived test data.

Redaction may remove information entirely from a representation when there is no legitimate requirement to expose it.

The technique is therefore a Data Security and privacy control, not database compression, publication, or classification.

There is also an important Data Quality consideration. Masked or obfuscated data used for testing must remain structurally valid and retain required relationships so that applications behave realistically. Metadata should indicate that a dataset has been transformed for privacy purposes so consumers do not mistake masked values for authoritative production data.

Reference Topics: DAMA-DMBOK2 Chapter 7 — Obfuscation; Redaction; Data Masking; Privacy; Sensitive Information; Chapter 13 — Fitness for Purpose and Controlled Transformation.

===============

A Data Lineage tool provides the capture and maintenance of source structures for each attribute on the data model. Lineage connects modeled data elements to their originating structures and records how those elements move and transform through downstream systems. DAMA-related practice material identifies this specific capability as the correct function of lineage tooling.

At attribute level, lineage may identify a source table and column, transformation logic, staging structures, intermediate mappings, target columns, and ultimately the report or analytical object in which the value appears. This information allows organizations to answer fundamental questions such as: Where did this value originate? What transformations were applied? Which systems depend on this field? What downstream assets will be affected if its definition changes?

For Data Quality, lineage is a critical diagnostic capability. If a value becomes inaccurate after a transformation, lineage helps isolate the point of failure and distinguish source-system defects from integration or reporting defects. It also supports impact analysis before quality-rule changes are deployed.

Metadata Management normally provides the repository and technical structures that preserve lineage. Governance establishes ownership and documentation standards, while Data Stewards use lineage to understand business impact and coordinate remediation.

The other choices do not describe recognized lineage functions.

Reference Topics: DAMA-DMBOK2 — Metadata Management; Data Lineage; Data Architecture; Source-to-Target Mapping; Chapter 13 — Issue Investigation and Root-Cause Analysis.

===============