Course: B.Tech (Semester V)
Subject Code: KDS051
Subject Name: Business Intelligence and Analytics
Maximum Marks: 100
Time: 3 Hours
Pattern:
Section A: 10 short questions × 2 marks = 20 marks
Section B: Attempt any 3 × 10 marks = 30 marks
Section C (Q3–Q7): Attempt any 1 part per question × 10 marks = 50 marks
SECTION A – Short Answer Questions (2 × 10 = 20 Marks)
a. What is the historical context of Business Intelligence, and how has it evolved over time?
b. Explain the significance of effective and timely decision-making in BI.
c. Define Data Mining and highlight its significance in BI.
d. Explain the architecture of Data Mining.
e. What is the purpose of Data Validation in BI?
f. Explain the concept of Data Transformation.
g. What is the analytics process?
h. Differentiate between Descriptive and Prescriptive Analytics.
i. Define Business Activity Monitoring (BAM).
j. What is Complex Event Processing (CEP)?
SECTION B – Descriptive / Analytical Questions (Any 3 × 10 = 30 Marks)
a. Explain the significance of mathematical models in BI. Discuss types, applications, and impact on decision accuracy.
b. Detailed analysis of OLAP (Online Analytical Processing): tools, applications, and examples.
c. Overview of Principal Component Analysis (PCA) – principles, applications, and benefits in dimensionality reduction.
d. Discuss the Iris dataset, its significance, and its use in machine learning and statistics.
e. Discuss the role of Business Process Management (BPM) in BI implementation and organizational optimization.
SECTION C – Long Answer / Case-Based (Each 10 Marks)
Q3. Real-Time BI
a. Discuss the challenges and advantages of Real-Time BI systems and their impact on operational efficiency.
b. Explain the historical evolution of Business Intelligence and key technological milestones.
Q4. Data Warehousing
a. Explain principles of data modeling in Data Warehousing and its role in information retrieval.
b. Discuss Star Schema and Snowflake Schema with diagrams and differences.
Q5. Data Quality and Reduction
a. Explain the principles of Data Validation and its importance in ensuring accuracy and reliability.
b. Discuss Data Reduction techniques — Sampling, Selection, and PCA — and how they help in efficient analysis.
Q6. Analytics
a. Describe analytical techniques used in Descriptive Analytics — statistical methods and visualization tools.
b. Explain Predictive Analytics: its methodologies, algorithms, and forecasting applications.
Q7. Monitoring and Root Cause
a. Explain Root Cause Analysis principles and methodologies in BI.
b. Discuss challenges and benefits of real-time monitoring in Business Activity Monitoring with examples.
