Course: B.Tech (Semester V)
Subject: Business Intelligence and Analytics
Subject Code: BCDS051
Maximum Marks: 70
Time: 3 Hours
Paper ID: 310916
Exam Year: 2024–25
Section A (2 × 7 = 14 Marks)
Attempt all questions briefly:
a. Define Business Intelligence (BI).
b. Name one common tool used for Business Intelligence.
c. Define Data Mining and give an example.
d. With example, define Validation in Business Intelligence.
e. Differentiate between Data Validation and Data Cleaning.
f. Why is the Analytics Process important in BI?
g. Define Business Activity Monitoring (BAM).
Section B (Attempt any three × 7 = 21 Marks)
a. Discuss the history and evolution of Business Intelligence, describing its origins and key milestones.
b. Explain the architecture of a Data Mining System and describe the stages in the data mining process.
c. Describe the process of Data Transformation and its significance in preparing data for BI and analytics.
d. Explain Descriptive Analytics in Business Intelligence and how it uses historical data for summary insights.
e. Describe Complex Event Processing (CEP) and its role in BI.
Section C (Attempt any one part from each question – 7 Marks each)
Q3
(a) Explain the importance of effective and timely decision-making in Business Intelligence and how BI enables real-time informed decisions.
OR
(b) Describe the architectural representation of Business Intelligence.
Q4
(a) Discuss functionalities and classifications of Data Mining, including key techniques used.
OR
(b) Define Data Warehousing and explain its importance in BI, along with data warehouse architecture.
Q5
(a) Explain sampling, data selection, and PCA (Principal Component Analysis) and how they reduce computational complexity while preserving data features.
OR
(b) Describe Data Discretization and its role in data preparation.
Q6
(a) Discuss common techniques used in Predictive Analytics.
OR
(b) Explain the importance of Social Media Analytics in Business Intelligence.
Q7
(a) Explain Business Activity Monitoring (BAM) and its role in BI.
OR
(b) Define Root Cause Analysis (RCA) and explain its significance in Business Intelligence.
Key Topics for Revision
Business Intelligence Fundamentals and Architecture
Data Mining and Data Warehousing Concepts
Data Preparation: Transformation, Validation, Cleaning, Discretization
Types of Analytics: Descriptive, Predictive, and Prescriptive
Complex Event Processing (CEP) and Business Activity Monitoring (BAM)
Principal Component Analysis (PCA) and Sampling Techniques
Social Media Analytics and its Role in BI
Root Cause Analysis (RCA) in Decision Making
