- Year not given · 4+4 marks
DM vs RDBMS SQL; project failure
Short answer
RDBMS SQL is verification-driven, retrieving known facts; data mining is discovery-driven, finding hidden patterns. Project failures stem from unclear goals, poor data quality, underestimating ETL, lack of support, unrealistic expectations, wrong tools, and not acting on results.
Compare Data Mining with RDBMS SQL in Knowledge Extraction. Explain the cause of DM & DW Project Failure.
Data mining vs RDBMS SQL in knowledge extraction
| Point | RDBMS / SQL | Data mining |
|---|---|---|
| Approach | Verification-driven: user states exactly what to retrieve | Discovery-driven: finds unknown patterns |
| Question type | "What was the total sales of Pokhara in 2025?" | "Which customers are likely to churn?" |
| Result | Stored facts (data) | Patterns, rules, models (knowledge) |
| Techniques | Selection, join, aggregation | Classification, clustering, association, prediction |
| Data | Current, operational | Large historical, preprocessed |
| Prediction | Not possible | Possible |
| User needed | Must know the data and the query | Needs less prior hypothesis |
Causes of data mining and data warehouse project failure
- Unclear goals: no business objective or poor requirement analysis.
- Poor data quality: dirty, incomplete or inconsistent source data.
- Underestimating ETL effort (it takes most of the time and cost).
- Lack of management support or user involvement; users resist change.
- Unrealistic expectations: expecting instant magic results; over-ambitious scope (big-bang approach).
- Wrong tools or architecture; poor performance and scalability.
- Lack of skilled staff and domain experts; poor project management and budget overrun.
- Ignoring metadata, security and privacy.
- Not acting on results or poor interpretation and presentation.
✓ Answer checked by Claude Opus 5.5 (high effort)
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