UNIT-1

1. What is Business Intelligence? / What is the main purpose of BI? (4 Marks)

Definition:
Business Intelligence (BI) is a technology-driven process that helps organizations
analyze data and present actionable information to support better decision-making.
Main Purpose:

 To convert raw data into meaningful and useful insights

 To help in decision-making at all levels — strategic, tactical, and operational

 To monitor performance using dashboards and KPIs

 To discover trends, patterns, and hidden insights

 To reduce risks and improve customer satisfaction and efficiency

2. Difference between Data, Information, and Knowledge (4 Marks)

Term Meaning Example

Data Raw facts, unprocessed "250", "John", "Blue"

Processed and structured data with

Information "John scored 250 in Math"
meaning

Information + experience + context, "John is good at Math; offer him

Knowledge
used for decisions tutoring tasks"

 Data is input

 Information is useful output

 Knowledge is using information to act wisely

3. BI Architecture (Draw + Explain) (4 Marks)

Diagram
(Draw boxes from left to right):
👉 Data Sources → ETL → Data Warehouse → OLAP/Analytics → Dashboards

Explanation:

 Data Sources: Business apps (ERP, CRM), files, social media, sensors
 ETL Process: Extract, Transform, Load — cleans and formats data
  Data Warehouse: Central database storing clean, structured data

 OLAP/Analytics Tools: Perform fast and complex queries

 Dashboards & Reports: Display visual insights for users

It ensures data flows from raw to refined insight, step by step.

4. Components of BI (4 Marks)

 Data Warehouse – Collects and stores cleaned historical data

 ETL Tools – Extract from source, clean and load into warehouse
 OLAP Tools – Enables slicing, dicing, drill-down, pivot

 Data Mining Tools – Discover hidden patterns and correlations

 Reporting Tools – Generate charts, graphs, tables for analysis

 Dashboards – Real-time view of key business metrics (KPIs)

 Metadata Repository – Stores data about data (definitions, formats)

Each component plays a role in transforming raw data into decisions.

5. Methodologies of Business Intelligence (7 Marks)

Business Intelligence (BI) methodologies are the techniques and strategies used to
analyze business data and support decision-making. These help convert raw data
into actionable insights.

🔹 Key BI Methodologies:

1. Descriptive Analytics

o Describes past performance.

o Uses historical data to identify trends and patterns.

o Example: Monthly sales reports or customer demographics.

2. Diagnostic Analytics

o Identifies the causes behind outcomes.

o Explores relationships between variables.

o Example: Analyzing why product returns increased.

3. Predictive Analytics

o Uses statistical models and machine learning to forecast future trends.

o Example: Predicting customer churn or future sales.
 4. Prescriptive Analytics

o Recommends actions to achieve desired outcomes.

o Uses optimization techniques and scenario analysis.

o Example: Suggesting best time and price for product promotions.

5. OLAP (Online Analytical Processing)

o Allows multi-dimensional analysis of data.

o Performs operations like slice, dice, drill-down, and pivot.

6. Data Mining

o Finds hidden patterns and correlations in large datasets.

o Example: Market basket analysis in retail.

7. Scorecards and Dashboards

o Monitor Key Performance Indicators (KPIs).

o Help managers track performance visually.

6. Benefits and Challenges of BI (7 Marks)

✅ Benefits of Business Intelligence:

1. Improved Decision Making

o Access to real-time, accurate data helps in making informed decisions.

2. Faster Reporting

o Reduces time spent on manual reports; users can create custom

reports.
3. Better Operational Efficiency

o Identifies process bottlenecks and suggests improvements.

4. Enhanced Customer Insights

o Analyzes behavior to improve customer service and marketing

strategies.
5. Competitive Advantage

o Identifies market trends and helps businesses stay ahead.

6. Cost Reduction

o Optimizes inventory, logistics, and resource usage.

 7. Risk Management

o Detects anomalies or fraud early through analysis.

🚨 Challenges of BI:

1. High Initial Cost

o Implementation and maintenance of BI tools are expensive.

2. Poor Data Quality

o Incomplete or inconsistent data affects the accuracy of results.

3. Resistance to Change

o Employees may be reluctant to adapt to new systems.

4. Complex Integration

o Difficult to connect BI with legacy systems or multiple data sources.

5. Security Risks

o BI systems deal with sensitive data; must be secured properly.

6. Skill Shortage

o Requires trained professionals to analyze and interpret data.

7. Best Practices of BI (7 Marks)

Following best practices ensures successful BI implementation and long-term value.

🔹 Key Best Practices:

1. Define Clear Objectives

o Set specific goals (e.g., increase sales by 10%, reduce customer

complaints).
2. Ensure High-Quality Data

o Validate, clean, and standardize data before analysis.

3. Choose the Right Tools

o Use BI tools that fit your company’s size, needs, and technical skill
level (e.g., Tableau, Power BI).
4. Involve Stakeholders

o Include business users, not just IT, to ensure practical and useful
outputs.
 5. Self-Service BI

o Allow users to explore data independently with easy-to-use

dashboards.
6. Train and Support Users

o Provide continuous training for staff to use BI tools effectively.

7. Data Governance & Security

o Define who can access which data. Protect against leaks and misuse.
8. Mobile BI

o Make reports accessible from smartphones or tablets for real-time

decisions.
9. Monitor Performance

o Regularly update dashboards and improve KPIs based on changing

needs.
8. Data Analytics Techniques (7 Marks)

Data analytics involves examining data to discover useful insights, and it includes
various techniques:

🔹 Types of Analytics:

1. Descriptive Analytics

o Summarizes past data using charts and dashboards.

o Example: Monthly revenue trends.

2. Diagnostic Analytics

o Examines data to understand causes behind events.

o Tools: Drill-down analysis, correlation checks.

3. Predictive Analytics

o Uses machine learning or statistical modeling.

o Example: Predicting product demand or customer churn.

4. Prescriptive Analytics

o Suggests actions based on predictions.

o Example: Recommending pricing strategies.

5. Data Mining
 o Identifies patterns using algorithms.

o Techniques: Classification, clustering, association rule mining.

6. Text Analytics

o Analyzes textual data like reviews or comments.

o Example: Sentiment analysis of product feedback.

7. Time-Series Analysis

o Analyzes data points over time (stock prices, sales).

o Helps in forecasting.
8. Visual Analytics

o Uses visual tools like heatmaps and graphs to explore patterns.

9. Why SaaS is a Distributed Model in Vendor-Hosted Service (10 Marks)

SaaS (Software as a Service) is a cloud-based software delivery model where the

vendor hosts and manages the software, and users access it via the internet.

🔹 Why It Is a Distributed Model:

1. Remote Hosting

o Software and data are hosted on cloud servers (not on the user's
machine).

o Example: Google Docs, Salesforce.

2. Multi-Tenancy

o Multiple users (organizations) use the same infrastructure securely.

o Resources are shared but data is isolated.

3. Centralized Updates & Maintenance

o Vendor handles software updates, bug fixes, and maintenance.

4. Global Access

o Users can access the system from any location or device.

5. Elastic Scalability

o Resources can scale up/down as per user demand.

6. Data Distribution

o Data can be replicated across multiple servers for reliability.

 7. Redundancy and Failover

o If one server fails, another handles the workload — distributed failover.

8. Usage-Based Billing

o Pay as you use, making it cost-effective.

10. Future of Business Intelligence (10 Marks)

The future of BI is shaped by automation, artificial intelligence, and the demand for
faster and smarter decisions.

🔮 Key Trends Shaping the Future:

1. AI-Powered BI

o BI tools will use AI to automatically detect trends and make

recommendations.
2. Natural Language Querying

o Users can ask questions in plain English.

o Example: “Show me the top 5 products by revenue this month.”

3. Self-Service BI

o Non-technical users will create their own reports and dashboards.

4. Real-Time Analytics

o Instant data processing enables faster responses to business events.

5. Cloud-Based BI

o Enables mobility, collaboration, and scalability.

6. Embedded BI

o BI will be integrated into daily apps (CRM, ERP, etc.).

7. Data Storytelling & Visualizations

o Reports will use storytelling techniques to explain insights.

8. Mobile BI

o Managers and field staff will access dashboards on smartphones.

9. Advanced Predictive Models

o More accurate forecasting using AI/ML.

10. Automated Decision-Making
  Some decisions (like stock replenishment) will be handled automatically by
the BI system.

11. Discuss OLAP Techniques and Explain How OLAP Performs Complex
Multidimensional Queries. (10 Marks)

OLAP (Online Analytical Processing) is a BI tool that allows users to analyze

large datasets from multiple dimensions quickly and interactively. It supports
decision-making by enabling multidimensional queries.

🔹 Types of OLAP:

1. MOLAP (Multidimensional OLAP)

o Uses pre-built data cubes

o Fast performance, but less flexible

2. ROLAP (Relational OLAP)

o Uses relational databases

o Handles large data, slower than MOLAP

3. HOLAP (Hybrid OLAP)

o Combines MOLAP + ROLAP

o Balanced speed and scalability

🔹 OLAP Operations (Techniques):

1. Slice – Select a single layer of data

→ E.g., Sales in 2024
2. Dice – Create a sub-cube by selecting 2+ dimensions
→ E.g., Q1 sales in South India for electronics

3. Drill-Down – View more detailed data

→ Year → Quarter → Month → Day

4. Roll-Up – View summarized data

→ Day → Month → Quarter → Year

5. Pivot (Rotate) – Switch dimensions to view from a new angle

→ Swap rows and columns

🔹 How OLAP Handles Complex Queries:

  OLAP stores pre-aggregated data, so queries run fast.

 Users can explore data without writing SQL.

 Enables real-time answers for managers and analysts.

 Useful for "what-if" analysis, trends, forecasting.

 Queries can be based on time, location, product, customer, etc.

12. Discuss in detail the History of Business Intelligence and how BI is

leveraged in the present world of needs. (10 Marks)

Business Intelligence (BI) refers to the technologies, processes, and practices that
organizations use to collect, integrate, analyze, and present business data. The goal
is to support better decision-making and drive business performance.

🕰️ History of Business Intelligence

The concept of BI has evolved significantly over time:

🔹 1865 – Origin of the Term

 The term "Business Intelligence" was first used by Richard Millar Devens
in his book Cyclopædia of Commercial and Business Anecdotes.

 He described how a banker gained an advantage by collecting information

ahead of his competitors.

🔹 1950s–1970s – Early Decision Support Systems (DSS)

 The foundation for BI began with Decision Support Systems, used by

businesses to aid complex decision-making.

 These systems used computers to analyze and report business data.

🔹 1980s – Executive Information Systems (EIS)

 EIS were designed for top executives to access key metrics and performance
summaries.

 Simple dashboards and reporting tools started emerging.

🔹 1990s – Emergence of Modern BI

 The term "Business Intelligence" became widely used, thanks to Howard

Dresner (Gartner Group).
 Introduction of Data Warehousing and OLAP (Online Analytical
Processing) revolutionized data analysis.
  BI tools were mostly used by IT professionals.

🔹 2000s – Dashboard & Data Mining Tools

 BI became more visual and interactive with the introduction of dashboards

and data mining tools.
 Software like SAP BusinessObjects, IBM Cognos, and MicroStrategy
gained popularity.

 Real-time reporting began to evolve.

🔹 2010s – Cloud & Self-Service BI

 Shift from on-premise to cloud-based BI tools (e.g., Tableau, Power BI, Qlik).

 Focus on self-service BI, where business users could create their own
dashboards without IT help.
 Rise of predictive analytics and basic machine learning.

🔹 2020s – AI and Automation in BI

 BI now uses Artificial Intelligence (AI) and Natural Language Processing

(NLP).

 Integration of real-time data streams, mobile BI, and embedded analytics.

BI today is deeply integrated into various industries and departments. It is used for:

🔹 1. Decision Making

 Helps executives, managers, and analysts make data-driven decisions.

 Example: Choosing where to open a new store based on sales trends.

🔹 2. Predictive Analytics

 Forecasts future trends using historical data and machine learning.

 Example: Predicting which customers are likely to stop using a service (churn
prediction).

🔹 3. Performance Management

 Monitors KPIs using real-time dashboards.

 Example: Tracking daily sales performance or customer complaints.

🔹 4. Marketing & Customer Insights

 Analyzes customer behavior, preferences, and campaign performance.

  Example: Which advertisement brought the most conversions.

🔹 5. Fraud Detection

 Financial institutions use BI to spot unusual transactions and prevent fraud.

🔹 6. Inventory & Supply Chain Optimization

 Helps in maintaining stock levels, reducing waste, and tracking shipments.

🔹 7. Healthcare Analytics

 Hospitals use BI to improve patient outcomes and manage resources

efficiently.

🔹 8. Education & Government

 BI helps in budgeting, resource planning, and policy-making in public sectors

and institutions.

UNIT-2
1. Explain OLAP Cubes for Analytical Tools (4 Marks)

An OLAP cube is a multi-dimensional data structure used in Online Analytical

Processing to quickly analyze complex datasets. Unlike flat tables, OLAP cubes
allow data to be modeled and viewed in multiple dimensions — such as time,
geography, product, and sales.

🔹 Key Features:

 Dimensions: Each axis (e.g., time, product, region) is a dimension.

 Measures: Numerical data like sales, profit, etc., are stored in the cells of the
cube.
 Fast Performance: Pre-aggregated data enables fast querying.

 Used for: Trend analysis, budgeting, forecasting, sales analysis.

🔹 Example:
A cube may analyze:

 Sales (measure)

 Across Region, Time, and Product (dimensions)

Users can perform:

 Slicing: View sales in a specific region

 Dicing: Compare regions in Q1

 Drill-down: From year → month → day

2. What are Data Insights? Explain in Detail (4 Marks)

Data Insights are the useful, actionable conclusions drawn from analyzing data.
Insights go beyond data summaries — they explain patterns, behavior, trends,
and potential actions.

🔹 Characteristics:

 Are based on analysis, not just observation

 Help businesses make strategic or tactical decisions

 Often visualized in dashboards and reports

 Can reveal hidden trends or early warnings

🔹 Types of Insights:

 Descriptive (what happened)

 Diagnostic (why it happened)

 Predictive (what might happen)

 Prescriptive (what should be done)

🔹 Example:

From customer data:

 Raw data shows low repeat purchases

 Insight: Most churn occurs within 15 days

 Action: Launch targeted engagement campaign after purchase

3. What is Cron Job? (4 Marks)

A Cron Job is a time-based task scheduler in Unix/Linux environments. It
automates repetitive tasks like backups, report generation, and data loading.

🔹 Examples:

 Refresh BI dashboard every 6 hours

 Email weekly reports to management

 Schedule ETL jobs to load new data every night

🔹 Benefits:

 Saves time

 Reduces manual errors

 Ensures consistency in BI processes

4. What is Ad Hoc Query? (4 Marks)

Definition:
An Ad Hoc Query is a spontaneous, user-defined query created to extract
specific data from a database, typically without relying on pre-built reports.
Explanation:

 It allows business users to ask unique, on-the-spot questions like "What

were the top 3 products sold in March 2025 in Delhi?"
 Created using BI tools like Power BI or Tableau via drag-and-drop or query
builders.
 Supports self-service BI by allowing users to analyze data without coding or
IT support.
 Useful for exploring unexpected patterns or solving immediate business
problems.

5. Explain the Properties of Connection Pools (7 Marks)

Definition:
A Connection Pool is a set of reusable database connections maintained by the BI
system to handle multiple user queries efficiently.
Explanation:

 It reduces the overhead of opening and closing database connections.

 Important for performance, scalability, and resource control.
Key Properties:
 1. Max Connections – Maximum simultaneous DB connections

2. Timeout – Closes idle connections after a time limit

3. Retry Interval – Retry failed connection after delay

4. Isolation Level – Controls how transactions interact

5. Persistence – Keeps connections alive or resets them

6. Authentication – Static or dynamic user validation

7. Query Timeout – Prevents long-running queries from hogging resources

These properties ensure stable, secure, and fast access to databases.

6. Dashboards and Scorecards (7 Marks)

Definition:

 A Dashboard is a real-time visual interface showing key business data.

 A Scorecard is a tool that compares actual performance to strategic goals

using KPIs.
Explanation:

 Dashboards display charts, graphs, tables to help managers monitor

operations.

 Example: Sales dashboard shows daily sales, returns, and targets.

 Scorecards are used to evaluate long-term performance against benchmarks
or objectives.

 Example: A marketing scorecard tracks campaign ROI, lead conversions.

Comparison:

Feature Dashboard Scorecard

Purpose Operational view Strategic comparison

Data Type Real-time Periodic performance

Visual Style Charts & graphs KPI vs. Goal tables

7. Oracle BI Disconnected Analytics Client System (7 Marks)

Definition:
Oracle BI Disconnected Analytics is a system that allows users to analyze data
offline using locally stored metadata and datasets.

Explanation:

 Used when users (like field agents) don’t have constant internet access.
 Architecture includes:

1. BI Server – Online data processing

2. Disconnected Client – Installed locally

3. Data Sync Tool – Updates local data when online

4. Presentation Layer – Dashboards and reports offline

 Users download data, work offline, then sync again.

 Ensures continuous access to BI insights even in remote areas.

8. RTBI System & Architecture (7 Marks)

Definition:
Real-Time Business Intelligence (RTBI) is a method of analyzing live data instantly
to support immediate decision-making.

Explanation:

 Processes data as it is generated (e.g., sales, sensors, transactions).

 Architecture includes:
1. Live Data Sources – Streaming input (IoT, transactions)

2. Streaming ETL Tools – Process data instantly (Kafka, Flink)

3. In-Memory Analytics – Quick data access

4. Dashboards – Auto-update KPIs in real time

 Used in fraud detection, stock trading, customer service.

 Helps businesses react quickly to dynamic situations.

9. How is Business Automation Significant in Business Intelligence? (10

Marks)

Definition:
Business Automation in BI refers to the use of software tools and technologies to
automatically perform repetitive data-related tasks like ETL processing, report
generation, alerts, and decision workflows — reducing human intervention and
speeding up insights.

🔹 Why is Automation Important in BI?

1. Saves Time and Resources

o Automates routine jobs like refreshing data, emailing reports, or

checking KPIs.
2. Improves Accuracy

o Reduces errors in manual data entry and report generation.

3. Enhances Speed and Decision-Making

o Automation ensures real-time or scheduled updates so that decisions

are made using current data.
4. Boosts Productivity

o Analysts can focus on insights instead of repetitive tasks.

5. Enables 24/7 Monitoring

o Systems can run tasks, alerts, and reports even when staff is offline.

🔹 Key Areas Where Automation is Applied:

1. ETL Automation

o Automatically extract, transform, and load data on a schedule (e.g.,

every night at 2 AM).

o Tools: Apache Nifi, Talend, SSIS.

2. Report Scheduling

o Scheduled delivery of dashboards or PDFs via email to stakeholders.

o Tools: Power BI Service, Tableau Server, Oracle BI Publisher.
3. Trigger-Based Alerts

o BI tools send automatic alerts if certain thresholds are crossed.

o Example: Notify sales manager if daily revenue drops below ₹50,000.

4. Data Quality Checks
 o Run scripts to clean data automatically — fix duplicates, handle nulls,
etc.
5. Model Re-Training (ML Automation)

o In predictive BI, models can automatically retrain using new data

(AutoML).
6. Workflow Automation

o Integration with tools like Zapier, Power Automate to trigger workflows

based on BI insights.

🔹 Real-Life Example:

In a retail company:

 Inventory data is updated nightly via ETL.

 If stock drops below a threshold, BI alerts the warehouse.

 Daily sales reports are emailed to managers before 8 AM.

All this happens without manual effort — fully automated.

✅ Conclusion:

Business Automation in BI enhances speed, reliability, and responsiveness. It helps

organizations reduce costs, eliminate human error, and make faster, smarter
decisions — making BI more scalable and powerful.

10. Short Notes (10 Marks – 2 marks each)

Write these with definitions + examples, keeping each around 6–7 lines.

a) Predictive Model

Definition: A predictive model uses statistical techniques and historical data to

forecast future events.
Explanation:

 Common methods: regression, decision trees, time-series, machine learning.

 Used in sales forecasting, customer churn prediction, and demand planning.

Example: A telecom company uses a predictive model to estimate which customers
are likely to cancel their plans.

b) Pattern Recognition and Learning Model

Definition: These models identify patterns, trends, or structures within data using AI
or ML.
Explanation:

 Recognize recurring behaviors or shapes in large datasets.

 Support classification, clustering, and anomaly detection.

Example: Fraud detection systems use these models to identify unusual transaction
patterns.

c) Optimization Model

Definition: An optimization model finds the best solution to a problem under given
constraints.
Explanation:

 Used to minimize cost, maximize profit, or improve efficiency.

 Solved using linear programming, integer programming, etc.

Example: A delivery company uses an optimization model to minimize fuel costs and
delivery time.

d) Project Management Model

Definition: These models help in planning, scheduling, and controlling projects.

Explanation:

 Include Gantt charts, Critical Path Method (CPM), and PERT analysis.

 Track tasks, resources, costs, and deadlines.

Example: IT companies use Gantt charts to manage software development projects.

e) Risk Analysis Model

Definition: This model identifies potential risks in a business process and assesses
their likelihood and impact.
Explanation:

 Used for risk scoring, prioritization, and mitigation planning.

 Applied in finance, insurance, construction, etc.

Example: A bank uses risk models to evaluate loan applicants based on credit
history.

f) Waiting Line (Queuing) Model

Definition: A queuing model analyzes service systems where customers wait in line.

Explanation:

 Calculates average wait time, queue length, and service efficiency.

 Based on arrival rate, service rate, and queue discipline.

Example: Hospitals use queuing models to reduce patient wait times at emergency
rooms.

11. Describe Reports and Ad Hoc Queries in BI (10 Marks)

Definition:

 Reports are predefined summaries or visualizations of business data

generated on a regular basis.
 Ad Hoc Queries are spontaneous, user-generated queries created to answer
specific, temporary business questions.

🔹 Reports:

 Built and published by BI developers

 Can be visual (charts, graphs) or tabular (tables, KPIs)
 Generated daily, weekly, monthly, or on trigger

 Used for operational reporting (sales, expenses, HR)

Example:
Monthly sales report emailed to regional heads every 1st of the month.

🔹 Ad Hoc Queries:
  Created on the fly by end users

 Help explore unexpected or specific issues

 Don’t require coding — drag-and-drop interfaces

 Enable quick decisions and problem-solving

Example:
“What were the top 10 products sold in Hyderabad during the last weekend?”

🔹 Comparison Table:

Feature Report Ad Hoc Query

Purpose Routine reporting Quick decision-making

Created By IT or BI team Business user

Frequency Scheduled On-demand

Flexibility Fixed High

✅ Conclusion:

Reports provide consistency and standardization, while ad hoc queries offer flexibility
and speed. Together, they give businesses full control over both routine monitoring
and dynamic exploration.

12. Discuss Power BI Bridge Connectors (10 Marks)

Definition:
Power BI Bridge Connectors are built-in or custom tools that allow Power BI to
connect to external data sources, enabling users to extract, transform, and analyze
data from various platforms.

🔹 Types of Data Sources:

 On-premises: SQL Server, Excel, Oracle, Access

 Cloud: Azure, Salesforce, SharePoint, Google Analytics

 Web/API: REST APIs, OData feeds, JSON, CSV

🔹 Connector Components:

1. Data Access Layer

o Establishes connection and retrieves raw data.

2. Power Query Editor

o Cleans and transforms the data (remove nulls, rename fields, split
columns).
3. Data Model Layer

o Builds relationships and calculations (measures, KPIs).

4. Scheduled Refresh

o Automatically updates data at set intervals.

5. Gateway Support

o On-premise gateway bridges local databases to Power BI Service.

🔹 Security & Authentication:

 OAuth 2.0, API Keys, Azure Active Directory

 Ensures secure access to sensitive business data

🔹 Custom Connectors:

 Developers can build connectors using M language or Power Query SDK

 Useful for internal business tools or niche applications

✅ Benefits of Power BI Bridge Connectors:

 Connects to hundreds of data sources seamlessly

 Supports both import and live (direct) query modes

 Helps centralize, analyze, and visualize data from various departments

 Makes data accessible and actionable in real-time

✅ Conclusion:
Bridge Connectors are vital for Power BI’s success as a powerful BI tool. They offer
secure, flexible, and fast access to data from anywhere — enabling organizations to
make data-driven decisions across departments.