Which aspect of information governance BEST enables an enterprise to avoid duplication of records and promote consistency of data?

Data modeling is the process of creating a conceptual, logical, and physical representation of data elements and their relationships. A core objective of data modeling is to establish a single, authoritative structure for data. By defining entities, attributes, and relationships, and by applying normalization rules, data modeling directly identifies and eliminates data redundancy. It enforces data integrity constraints (e.g., data types, formats, unique keys), which ensures that data is consistent and reliable across different systems and applications within the enterprise. This makes it the most direct and effective practice for avoiding duplication and promoting consistency.

A. Data loss prevention (DLP) is a security measure focused on preventing the unauthorized exfiltration of sensitive data, not on managing its internal structure or quality.

C. Blockchain management is a specific technology for distributed ledgers. While it ensures integrity within its chain, it is not a foundational governance practice for all enterprise data.

D. Enterprise architecture (EA) is a broad discipline that includes information architecture. Data modeling is the specific, granular activity within EA that directly addresses data structure, duplication, and consistency.

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1. ISACA

CGEIT Review Manual

8th Edition. Domain 2: Information Governance

Section 2.4

Information Architecture. The manual explains that a key component of information architecture is the creation of data models (conceptual

logical

physical) to provide a standard definition and format for enterprise data. This process is fundamental to reducing data redundancy and ensuring data consistency.

2. ISACA

COBIT 2019 Framework: Governance and Management Objectives. Management Objective APO14

Managed Data. Practice APO14.03

"Define and maintain the enterprise data model

" is described as essential for creating a common data vocabulary and structure

which directly supports data quality objectives like consistency and the avoidance of duplication.

3. DAMA International

The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK2)

2nd Edition. Chapter 5

"Data Modeling and Design

" Section 2

Goals. This authoritative guide explicitly lists key goals of data modeling as improving data quality

reducing data redundancy

and improving data consistency through the formal process of defining and structuring data.