INFORMATION SYSTEM AND ANALYTICS
FOR MANAGEMENT DECISION-MAKING
Data Mining and Its Role in Business
Intelligence
Prepared By:
1. Patel Aayu 06
2.
3.
Patel Disha
Patel Om
13
26
Prepared For:
4. Rana Sakshi 48
Dr. Vishal J Mali
5. Rathod Meet 50 Associate Professor
Table Of Contents:
1. Meaning
2. Introduction To Data Mining
3. Overview Of Data Mining Techniques
4. Clustering Techniques
5. Classification Techniques
6. Regression Techniques
7. Association Rule Mining
8. Cases
9. Benefits Of Data Mining In Business Intelligence
10. Challenges In Data Mining
11. Advanced Data Mining Techniques
12. Real-world Success Stories
13. The Future Of Data Mining
DATA MINING AND BUSINESS
INTELLIGENCE
Transforming Raw Data into Strategic Decisions serves as our
gateway to understanding how modern businesses leverage data for
competitive advantage. The intersection of data mining and
business intelligence represents a crucial evolution in how
organisations make decisions, optimise operations, and create value
in today's digital economy.
INTRODUCTION TO DATA MINING
Data mining is the process of analyzing large datasets to find
patterns, trends, and insights. It helps turn raw data into useful
information for better decision-making.
In business intelligence, data mining supports tasks like
understanding customer behavior, predicting trends, and
detecting fraud. It is widely used in industries like retail,
healthcare, and banking to solve problems and improve
efficiency.
OVERVIEW OF DATA MINING TECHNIQUES
Data mining techniques help analyze and organize data in meaningful ways:
1.Clustering: Groups similar data points into clusters based on their characteristics.
For example, segmenting customers into groups like high, medium, and low
spenders.
2.Classification: Categorizes data into predefined groups. For instance, classifying
emails as spam or not spam.
3.Regression: Predicts continuous outcomes, such as forecasting sales revenue
based on past trends.
4.Association Rules: Identifies relationships between variables.
For example, discovering that customers who buy bread often buy butter as well
(market basket analysis).
These techniques help businesses make sense of complex data for better decision-
making.
CLUSTERING TECHNIQUES
• Clustering algorithms group objects
based on similarity, revealing natural
groupings in data.
• In retail, clustering can segment
customers based on demographics,
purchase behavior, and preferences,
enabling targeted marketing campaigns.
CLASSIFICATION TECHNIQUES
• Classification assigns data points to predefined
categories based on learned patterns.
• Classification uses methods like decision
trees, logistic regression, or support vector
machines to create models for predicting
outcomes.
• Email spam detection is a classic example,
where classifiers learn from labelled emails to
REGRESSION TECHNIQUES
• Regression models predict continuous
outcomes based on relationships
between variables
• Predicting sales revenue based on
historical sales data allows businesses to
anticipate future trends and optimize
inventory and marketing efforts.
ASSOCIATION RULE MINING
• Association rule mining discovers
relationships between variables in datasets,
uncovering patterns and connections.
• Market basket analysis, where retailers
identify products frequently purchased
together, is a prime example, leading to
strategic product placement and
promotions.
USE CASE IN RETAIL
• Data mining in retail allows businesses to analyze
customer purchase history and provide tailored
recommendations.
• Amazon's recommendation system leverages customer
data to suggest products they might be interested in,
increasing sales and customer satisfaction.
USE CASE IN BANKING
• Data mining helps detect fraudulent transactions by identifying
unusual patterns in financial data.
• Banks use data mining to analyze transaction anomalies, such as large
withdrawals from inactive accounts, preventing financial losses and
protecting customers.
BENEFITS OF DATA MINING IN BUSINESS
INTELLIGENCE
• Enhanced Decision-making: Data-driven
insights lead to more informed, strategic
decisions.
• Improved Customer Experience:
Personalized recommendations and services
enhance customer satisfaction.
• Operational Efficiency: Optimizing
processes and resource allocation for
CHALLENGES IN DATA MINING
• Data Quality: Inaccurate, incomplete, or
inconsistent data can lead to flawed
insights.
• Privacy Concerns: Protecting sensitive data
while leveraging its value is crucial.
• Integration with Business: Ensuring data
mining findings are effectively implemented
ADVANCED DATA MINING TECHNIQUES
• Neural Networks: Powerful algorithms for deep learning, enabling complex pattern
recognition.
• Natural Language Processing: Analyzing and understanding human language for
insights from text data.
• Predictive Analytics: Forecasting future trends and outcomes based on historical data.
REAL-WORLD SUCCESS STORIES
• Uses data mining to personalize movie
• Employs data mining for market
and TV show recommendations,
basket analysis, optimizing inventory
driving user engagement and management, and predicting customer
satisfaction. demand.
THE FUTURE OF DATA MINING
AI & IoT
Increasing integration with AI and IoT, enabling more
1
intelligent and automated data analysis.
Real-time Data
Real-time data mining applications,
2
provide immediate insights for
faster decision-making.
THANK
YOU
