When dealing with missing data, why might a DBA choose to construct a report listing the missing values?

Question

BlackTom AI · Accepted Answer

Missing values often signal problems in upstream data collection, ingestion, or ETL processes, so a DBA will build a missing-values report to locate and characterize those gaps.

1) Relevant knowledge points and concepts
- Data quality: completeness is a core dimension; missingness undermines analysis and reporting.
- Types of missingness: MCAR (missing completely at random), MAR (missing at random), MNAR (missing not at random) — patterns matter for remediation.
- Root-cause analysis: identifying where and when data are missing helps find the broken process (sensor, form, connector, validation rule).
- Instrumentation and monitoring: periodic reports provide baselines and trends to detect regressions.

2) Step-by-step reasoning process
- Define scope: select tables/columns critical to business logic.
- Query for null/empty values: use SQL to count rows where column IS NULL or value = ''.
- Aggregate results: compute counts and percentages per column, table, and source system.
- Trend analysis: compare daily/weekly snapshots to detect sudden spikes in missingness.
- Prioritize and communicate: present highest-impact missing fields to data owners for corrective action.

3) Example intermediate calculation
- If table has 10,000 rows and column X shows 1,200 NULLs: missing rate = (1,200 / 10,000) * 100 = 12%. Repeat for each column to rank severity.

4) Why the correct answer is correct
A report listing missing values directly reveals where data collection or pipelines fail, enabling targeted fixes. It ties observed missingness to specific fields, sources, and times—information required to correct upstream processes and improve overall data quality.

5) Why the other options are incorrect
- "To eliminate the need for SQL programming during data retrieval": Generating such a report typically requires SQL or similar queries; it does not eliminate programming.
- "To satisfy a requirement for high-availability redundant systems": Missing-data reports relate to data quality, not system availability or redundancy.
- "To prove that the database normalization process was successful": Normalization concerns schema design and redundancy, not the presence or absence of values in rows.

Conclusion: The correct reason is to identify where data collection processes may be insufficient, which matches the provided answer.

When dealing with missing data, why might a DBA choose to construct a report listing the missing values?单项选择题

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