Introduction to Data Analytics
and Its Importance
By
Dr. Alok Bansal
Dr. Alok Bansal, PIMR, Indore
1
Introduction to Data Analytics
1.1 What is Data Analytics?
• Data analytics refers to the process of examining, cleaning, transforming,
and modelling data to extract useful insights, support decision-making, and
drive business strategies.
1.2 Components of Data Analytics
• Data Collection: Gathering raw data from various sources (e.g., social
media, transactions, IoT devices).
• Data Cleaning: Removing errors, handling missing values, and standardizing
formats.
• Data Transformation: Structuring and organizing data for analysis.
• Data Modelling: Applying statistical and machine learning techniques.
• Data Visualization: Representing insights in charts, graphs, and dashboards.
Dr. Alok Bansal, PIMR, Indore
2
Understanding Big Data
2.1 What is Big Data?
• Big Data refers to large and complex datasets that traditional data processing
methods cannot handle efficiently.
2.2 Characteristics of Big Data (5Vs)
• Volume: Large amounts of data generated every second.
• Velocity: Speed at which data is generated and processed.
• Variety: Different types of data (structured, semi-structured, unstructured).
• Veracity: Accuracy and reliability of data.
• Value: Extracting meaningful insights from data.
2.3 Examples of Big Data Applications
• Retail: Personalized recommendations (Amazon, Netflix).
• Healthcare: Predictive diagnosis and treatment recommendations.
• Finance: Fraud detection in banking transactions.
• Manufacturing: Predictive maintenance of machinery.
Dr. Alok Bansal, PIMR, Indore
3
Data Science vs. Data Analytics
Feature
Definition
Focus
Techniques
Used
Tools
Output
Example
Data Science
Broader field focusing on
algorithms, AI, and ML to extract
insights.
Predictive modelling, machine
learning, AI.
Data Analytics
Process of examining data to find
trends and patterns.
Statistical analysis, reporting,
visualization.
Deep learning, NLP, clustering.
Data mining, dashboards, BI tools.
Python, R, TensorFlow.
Predictive models and AI
applications.
Developing self-driving car
algorithms.
Excel, SQL, Tableau, Power BI.
Actionable business insights.
Analyzing customer churn for telecom
companies.
Dr. Alok Bansal, PIMR, Indore
4
Importance of Data Analytics in Business
4.1 Why is Data Analytics Important?
• Improves Decision-Making: Enables data-driven decisions rather than relying on
intuition.
• Enhances Customer Experience: Provides personalized experiences based on data
insights.
• Boosts Operational Efficiency: Helps optimize supply chain, reduce costs, and improve
productivity.
• Identifies Market Trends: Helps businesses stay ahead of competitors.
• Fraud Detection & Risk Management: Identifies anomalies and reduces financial risks.
4.2 Use Cases of Data Analytics in Industries
• Retail: Demand forecasting, customer segmentation.
• Healthcare: Disease prediction, personalized treatment.
• Banking & Finance: Credit risk modelling, algorithmic trading.
• Marketing: Sentiment analysis, targeted advertising.
• Manufacturing: Quality control, supply chain optimization.
5
Dr. Alok Bansal, PIMR, Indore
Thank You
Dr. Alok Bansal, PIMR, Indore
6