Consequences of Poor Data Administration — What Can Happen? Which outcome is a realistic result of poor data administration practices across an organization's data landscape?

Introduction / Context:
Weak data administration leads to inconsistent definitions, poor documentation, and gaps in datasets. Understanding likely symptoms helps teams diagnose governance failures and prioritize remediation.

Given Data / Assumptions:

• No strong central data governance exists.
• Metadata is incomplete or unmanaged.
• Stewardship and ownership are unclear.

Concept / Approach:
With poor administration, projects may create schemas ad hoc, leaving crucial attributes undocumented or entirely absent. Instead of unified definitions, multiple conflicting definitions tend to appear. Therefore, “missing data elements” is a realistic, negative outcome. The alternatives that claim a single definition or familiarity are positive outcomes and unlikely under poor administration.

Step-by-Step Solution:
Consider typical failures: inconsistent definitions, duplication, and gaps.Assess each option for positivity vs. negativity.Recognize that missing data elements directly reflect governance gaps.Select “Missing data elements.”

Verification / Alternative check:
Audits of poorly governed systems typically find absent attributes (for example, missing master data keys), confirming that missing data elements are common.

Why Other Options Are Wrong:

• Single definition: Describes good governance, not poor administration.
• Familiarity with data: Also a sign of good stewardship and documentation.
• All of the above: Contradictory since (a) and (b) are positive outcomes.
• Automated lineage: A mature capability, unlikely where governance is weak.

Common Pitfalls:
Underestimating the downstream cost of missing attributes: broken integrations, unreliable analytics, and rework.

Final Answer:
Missing data elements