A business analyst is improving data quality in a CRM system. Many customer records have missing or inconsistent information, such as incomplete addresses and duplicate entries. Which process should the analyst prioritize?

Question

BlackTom AI · Accepted Answer

Data cleansing to standardize and correct inaccurate or incomplete data.

1. Relevant knowledge points and concepts
- Data quality dimensions: completeness (missing fields), accuracy (correct values), consistency (uniform formats), and uniqueness (no duplicates).
- Data cleansing (a.k.a. data cleaning or data scrubbing): processes to detect and correct or remove corrupt, inaccurate, or duplicate records; includes standardization, validation, enrichment, and deduplication.
- Other data activities: predictive modeling (uses clean data to forecast), data visualization (presents data), and data mining (discovers patterns). These rely on good data quality but do not fix poor data.

2. Complete reasoning process step by step
- Start from the symptoms: incomplete addresses and duplicate entries directly map to low completeness and poor uniqueness.
- Identify the immediate goal: make records usable—fill or flag missing fields, standardize address formats, merge duplicate customer records.
- Match the appropriate process: data cleansing explicitly performs these tasks (standardization, validation, deduplication, enrichment).
- Recognize sequencing: cleansing should occur before downstream tasks like predictive modeling, visualization, or mining, since those assume reliable inputs.

3. Intermediate steps (detailed actions)
- Standardize addresses: apply address parsing and normalization (e.g., "St." → "Street", consistent country/state codes).
- Validate/complete: use reference data or third-party enrichment to fill missing fields (postal codes, city).
- Deduplicate: use matching rules or fuzzy matching to identify and merge duplicate customer records while preserving audit trails.
- Run quality checks and set automated rules to prevent recurrence.

4. Why the correct answer is correct
- The question describes missing and inconsistent information and duplicates—precisely the issues data cleansing is designed to resolve. Cleansing directly improves completeness, consistency and uniqueness, enabling accurate downstream analysis.

5. Why other options are incorrect
- Predictive modeling: useful for forecasting but ineffective on poor-quality raw records; it should follow cleansing.
- Data visualization: displays trends but does not fix incomplete or duplicate data; visualizations may be misleading if data are dirty.
- Data mining: discovers patterns but also requires high-quality input; it won’t resolve missing/inconsistent entries.

6. Conclusion
- Therefore, the analyst should prioritize: Data cleansing to standardize and correct inaccurate or incomplete data. This matches the provided correct answer.

A business analyst is improving data quality in a CRM system. Many customer records have missing or inconsistent information, such as incomplete addresses and duplicate entries. Which process should the analyst prioritize?单项选择题

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