---
title: "DM vs RDBMS SQL; project failure"
subject: "Data Mining"
chapter: 1
key: "semester_7/data_mining:dm-vs-rdbms-failure"
source: https://www.bctengineer.com/q/semester_7/data_mining/dm-vs-rdbms-failure
site: BCT Engineering
---

# DM vs RDBMS SQL; project failure

Data Mining · Chapter 1: Introduction

- Asked in: year not given, 4+4 marks
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_7/data_mining/dm-vs-rdbms-failure
- Whole chapter: https://www.bctengineer.com/md/semester_7/data_mining/chapter-1

## Question

Compare Data Mining with RDBMS SQL in Knowledge Extraction. Explain the cause of DM & DW 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.

## Answer

#### 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
1. **Unclear goals**: no business objective or poor requirement analysis.
2. **Poor data quality**: dirty, incomplete or inconsistent source data.
3. **Underestimating ETL effort** (it takes most of the time and cost).
4. **Lack of management support** or user involvement; users resist change.
5. **Unrealistic expectations**: expecting instant magic results; over-ambitious scope (big-bang approach).
6. **Wrong tools or architecture**; poor performance and scalability.
7. **Lack of skilled staff** and domain experts; poor project management and budget overrun.
8. **Ignoring metadata, security and privacy.**
9. **Not acting on results** or poor interpretation and presentation.

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. How do SQL and data mining differ in approach?
   A. SQL is discovery-driven, mining is verification-driven
   B. SQL is verification-driven (user states what to retrieve), mining is discovery-driven
   C. Both are verification-driven
   D. Both are discovery-driven
   Answer: B. SQL answers exact questions; mining finds unknown patterns. (Key point: Verification vs discovery)

2. What does data mining output compared with SQL?
   A. Stored facts only
   B. Patterns, rules and models (knowledge)
   C. Only aggregated totals
   D. Only raw rows
   Answer: B. SQL returns stored data; mining produces knowledge such as rules and models. (Key point: Output type)

3. Which query suits data mining rather than SQL?
   A. Total sales of Pokhara in 2025
   B. Which customers are likely to churn
   C. List all customers in Kathmandu
   D. Count of orders yesterday
   Answer: B. Predicting churn needs mining; the others are direct retrieval or aggregation. (Key point: Prediction capability)

4. Which is a cause of DM and DW project failure?
   A. Clear business objective
   B. Underestimating ETL effort and poor data quality
   C. Strong management support
   D. Modest scope
   Answer: B. ETL takes most time and cost, and dirty data and unclear goals lead to failure. (Key point: Project failure causes)

## Related in Data Mining

- [RDBMS vs DW database; DW terms](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Ardbms-vs-dw-terms) — Data Mining, chapter 1
- [Data mining vs DW and applications](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-vs-dw-applications) — Data Mining, chapter 1
- [Feature selection vs feature extraction](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Afeature-selection-vs-extraction) — Data Mining, chapter 2
- [Need of DW and DM in government, medical, military](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adw-necessity-gov-medical-military) — Data Mining, chapter 1
- [Descriptive vs predictive data mining](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-descriptive-predictive) — Data Mining, chapter 1
- [Data mining vs query tools](https://www.bctengineer.com/question.md?key=semester_7%2Fdata_mining%3Adm-vs-query-tools) — Data Mining, chapter 1

## Related in other subjects

- [Data mining and business knowledge](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adata-mining-business-knowledge) — Information System, chapter 4
- [Project management body of knowledge](https://www.bctengineer.com/question.md?key=semester_7%2Fict_project_management%3Adefine-pmbok) — ICT Project Management, chapter 2
- [Project management knowledge areas](https://www.bctengineer.com/question.md?key=semester_7%2Fict_project_management%3Apm-knowledge-areas) — ICT Project Management, chapter 2
- [Delta modulation encoder/decoder; PCM vs DM](https://www.bctengineer.com/question.md?key=semester_6%2Fcommunication_systems%3Adm-encoder-decoder-pcm-vs-dm) — Communication Systems, chapter 4