Operational data characteristics: Decide whether the following is accurate. “Data in operational systems are typically fragmented and inconsistent.”

Introduction / Context:
Operational (OLTP) systems are built to run the business—order entry, billing, fulfillment—rather than to provide integrated analytics. Because different departments adopt systems at different times and with different rules, organizations often end up with multiple sources containing overlapping data and divergent definitions. This question asks if such data are “typically fragmented and inconsistent.”

Given Data / Assumptions:

• Multiple source applications exist (CRM, ERP, billing, support).
• Local optimizations and departmental semantics vary.
• There is no assumption of a fully mature MDM/governance program.

Concept / Approach:
Fragmentation occurs when the same business entity (for example, Customer) appears across several systems with different keys or attributes. Inconsistency arises from different coding schemes, timings, and quality controls. Data warehousing and MDM aim to reconcile these differences through conformed dimensions, survivorship rules, and golden records.

Step-by-Step Solution:

Verification / Alternative check:
Data quality assessments repeatedly reveal cross-system variance even in modern stacks; remediation reduces but rarely eliminates divergence at the source.

Why Other Options Are Wrong:

• Tying fragmentation only to legacy or absence of MDM is too narrow; even modern SaaS stacks fragment data.
• “Indeterminable without profiling” ignores widespread industry experience; profiling quantifies, but the tendency is well-known.

Common Pitfalls:
Assuming one system of record for all attributes; underestimating semantic drift; skipping conformance for speed, causing metric disparity.

Final Answer:
Correct