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Information system and Business Analytics Dr. Usman Javed Riphah International University, Faisalabad.
Organising data in traditional file system • Data Hierarchy in Computer Systems • Problems with the traditional file system
Effective Information Systems • Deliver accurate, timely, and relevant information • Accurate: Free from errors or inconsistencies • Timely: Available when decision-makers need it • Relevant: Useful for the task or decision at hand The Challenge in Businesses • Many businesses lack quality information • Common issues: delayed, incorrect, or irrelevant data • Root cause: poorly organized and maintained data
Why Data Management Matters • Ensures data is structured, clean, and accessible • Supports better decisions and efficiency • Core to building reliable information systems Traditional File Management Issues • Data stored in isolated files • Leads to duplication, inconsistency, and inefficiency • Difficult to retrieve and analyze data effectively * Data management is of paramount importance
Data Hierarchy in Computer Systems • Bit: Smallest data unit a computer can process • Byte: Group of bits; represents a character (letter, number, symbol) • Field: Group of characters (e.g., name, age) • Record: Collection of related fields (e.g., student name, course, grade) • File: Group of similar records (e.g., student course file) • Database: Collection of related files (e.g., student records + financial data)
Key Concepts • Entity: A person, place, thing, or event (e.g., student, course) • Attribute: Characteristic of an entity (e.g., Student_ID, Course, Grade) • FieldValues: Specific data stored for each attribute in a record For example… • Entity: COURSE • Attributes: Student_ID, Course, Date, Grade • Record: Specific student’s data for one course • File:All course records • Database: Combines course files with other student data
Problems inTraditional File Environment • Systems developed independently in each department • No centralized planning or integration • Each application had its own files and software programs • Example: Human Resources System • Separate files for: • Personnel, Payroll, Medical insurance, Pension, Mailing list • Leads to hundreds of isolated files over time • Key Problems • Data Redundancy: Same data stored in multiple files • Data Inconsistency: Conflicting data across systems • Difficult Maintenance: Hard to update, manage, or integrate systems • Inefficient Operations:Time-consuming and resource-heavy
Data Redundancy and Inconsistency • Data Redundancy: • Duplicate data stored in multiple files • Collected independently by different departments • Wastes storage and resources • Data Inconsistency: • Same attribute shows different values in different systems • Causes confusion and errors in decision-making
Examples of Inconsistency • Course Data: Date updated in one system but not others • Student_ID: Named differently across systems (e.g., Student_ID vs. ID) • Coding Differences: • Clothing size represented as “extra large” in one system, “XL” in another • Leads to integration issues across departments Impact on Business Systems • Hard to build integrated systems like: • Customer Relationship Management (CRM) • Supply Chain Management (SCM) • Enterprise Systems (ERP) • Results in poor data quality and decision-making
Program-Data Dependence • Data and programs are tightly linked • Each program must define data format and location Key Issues • Any change in program may require changes to the data • Data format changes can break multiple programs • Example: Changing ZIP code from 5-digit to 9-digit • Affects all programs using the old 5-digit format Consequences • High maintenance costs • Lack of flexibility in updating systems • Increases risk of errors and system failures
Lack of Flexibility • Can generate routine, scheduled reports only • Ad hoc reports are difficult and time-consuming to produce • Cannot easily respond to unexpected information needs Challenges • Data is available but hard to access • Requires extensive programming • May take weeks of effort to compile needed data Impact • Slows down decision-making • Increases costs and delays • Limits the organization’s ability to be agile and responsive
Poor Security • Lack of control over data access and usage • Unauthorized access and data changes are possible • No centralized way to track user activity Key Risks • Sensitive data may be exposed or altered • No audit trail to monitor who accessed or modified information • Increases chances of data breaches and internal misuse Impact • Compromises trust and data integrity • Can lead to legal issues and financial losses • Weakens overall information system security
Lack of Data Sharing and Availability • Data is stored in isolated systems across departments • No connection between different data files • Makes data sharing across functions very difficult Key Issues • Slow access to required information • Inconsistent data leads to distrust and confusion • Users may avoid using systems due to unreliable data Impact • Breakdown in communication between departments • Limits collaboration and informed decision-making • Reduces overall efficiency and effectiveness
The database approach to data management • Database management systems (DBMS) • How DBMS solve the problems of traditional file system • Relational DBMS • Operations of relational DBMS • Non-Relational databases in the Cloud • The database approach to data management • Querying and reporting • Designing Databases • Normalization and Entity relationship diagrams
Database Technology: A Better Approach • Centralized collection of organized data • Serves multiple applications efficiently • Eliminates redundancy by storing data once • Key Benefits • Data appears to be in one location for all users • Supports data sharing across departments • Reduces data inconsistency and duplication • Use one centralized Human Resources database • Impact • Improved accuracy, accessibility, and efficiency • Easier maintenance and updates • Enables better decision-making and integration
Database management system:What is a DBMS? • Software that centralizes data and manages it efficiently • Acts as an interface between applications and data files • Provides easy access to data for multiple programs Key Functions • Retrieves data when requested by applications • Separates logical and physical views of data • Logical view: How users see the data • Physical view: How data is stored Benefits of DBMS • Reduces programming effort • Allows multiple users to access customized views • Ensures data consistency and security Example: Human Resources Database • Benefits specialist sees: Name, SSN, Health Insurance, etc.
How a DBMS Solves Traditional File System Problems • Reduces data redundancy by minimizing repeated data • Helps maintain data consistency across all systems • Decouples programs and data, allowing independent management Key Advantages • Improves data accuracy and reliability • Enables centralized data management and security • Supports ad hoc queries for flexible data access Efficiency Gains • Faster access to information • Lower development and maintenance costs • Enhanced information sharing across departments
Relational DBMS (RDBMS) • Most common type of Database Management System • Stores data in two-dimensional tables (relations) • Each table represents an entity and its attributes Key Features • Easy to organize, access, and update data • Supports relationships between tables using keys • Ideal for small to large-scale applications Popular RDBMS Examples • Microsoft Access – for desktop systems • MySQL – open-source, widely used • Oracle Database, SQL Server, DB2 – for enterprise use • Oracle Database Lite – for mobile devices
Organizing Data in Relational Databases • Data is stored in tables (also called relations) • Each table contains rows (records/tuples) and columns (fields/attributes) • Separate tables are created for different entities (e.g., SUPPLIER, PART) Structure of aTable • Columns = Attributes (e.g., name, address, zip code) • Rows = Records (each row = one instance of the entity) • Fields = Individual data elements Key Concepts • Primary Key: Uniquely identifies each record in a table (e.g., Supplier_Number) • Foreign Key: A field in one table that links to the primary key in another table (e.g., Supplier_Number in PART table) • Enables relationships between tables
• Key Operations of a Relational DBMS • Relational tables can be combined using shared data elements (e.g., Supplier_Number) • Three basic operations: Select, Join, and Project • 1. Select Operation • Retrieves rows (records) that meet a specific condition • Example: Select parts where Part_Number = 137 or 150 • 2. Join Operation • Combines tables based on a common field • Example: Join PART and SUPPLIER tables using Supplier_Number to get supplier info for selected parts
• 3. Project Operation • Retrieves specific columns from a table • Example: Extract only Part_Number, Part_Name, Supplier_Number, Supplier_Name • Use Case Example • Goal: Find suppliers for Part 137 or 150 • Use Select → Join → Project to get clear and focused data output
📦 Non-Relational Databases & Cloud Databases • Need for Alternatives toTraditional Databases • Traditional relational databases: based on tables, columns, and rows • Not ideal for big data, web services, and unstructured data • Rise of cloud computing and massive data volumes demands flexible solutions • What Are Non-Relational (NoSQL) Databases? • Use a flexible data model (no fixed schema) • Designed for large-scale, distributed data processing • Handle structured + unstructured data (e.g., social media, web content, images) • Examples: • Oracle NoSQL Database • Amazon SimpleDB
• Cloud-Based Relational Databases • Offer traditional RDBMS features on a cloud platform • Examples: • Amazon RDS (MySQL, Oracle, SQL Server) • Oracle Database Cloud Service • Microsoft SQL Azure Database • Private Cloud Databases • Private cloud = secure, internal cloud infrastructure • Example: Sabre Holdings (aviation industry SaaS provider) • RunsOracle Database 11g; Supports 100+ projects & 700 users • Reduces costs & improves performance • Key Benefits • Scalability (up or down as needed) • Cost efficiency (pay-per-use model) • Improved data management for web and mobile applications • Better resource utilization through consolidation
🛠️ Core Capabilities of a DBMS • Data Definition Language (DDL) • Defines structure and content of the database • Used to create tables, define fields, and their characteristics • Examples of definitions: data type, field size, constraints • Data Dictionary • A repository of metadata (data about data) • Stores details like: • Field names & descriptions • Data types & sizes • Formats & validation rules • Key fields (e.g., Supplier_Number in Access)
• Advanced Data Dictionary Functions (Large Systems) • Tracks: • Data usage and ownership • Authorization and access levels • Responsible users or departments • Linked programs, reports, and business functions • Example: Microsoft Access • Offers basic data dictionary functionality • Shows: • Field names • Formats • Key fields (indicated by a key icon)
• 🔍Querying and Reporting with DBMS • Data Manipulation Language (DML) • Used to add, update, delete, and retrieve data • Allows end users and developers to extract and work with data • Most popular DML: SQL (Structured Query Language) • SQL – Structured Query Language • Industry-standard for querying relational databases • Example SQL:
• DBMSTools for Querying • DB2, Oracle, SQL Server: use SQL • MicrosoftAccess: • Uses SQL behind the scenes • Provides graphical tools for easy query building • Users select tables, fields, and set conditions visually • Reporting Features • Organize retrieved data into polished reports • Access simplifies reporting with built-in templates and layouts
6.3: USING DATABASESTO IMPROVE BUSINESS PERFORMANCEAND DECISION MAKING • The challenge of big data • Business intelligence infrastructure • Data warehouse and marts; Hadoop; In-memory computing;Analytic platforms • ANALYTICALTOOLS: RELATIONSHIPS, PATTERNS, TRENDS • Online analytical processing • Data mining • Text mining and web mining
• Businesses use databases to keep track of basic transactions, such as paying suppliers, processing orders, keeping track of customers, and paying employees. • But they also need databases to provide information that will help the company run the business more efficiently, and help managers and employees make better decisions. • If a company wants to know which product is the most popular or who is its most profitable customer, the answer lies in the data.
🌐The Challenge of Big Data • Traditional Data Limitations • Mostly transaction-based, fitting neatly in rows & columns • Managed well by relational DBMS • Examples: invoices, payments, payroll records Explosion of New Data Sources • Web traffic, social media posts (tweets, status updates) • Email messages, sensor outputs, and machine-generated data • Unstructured/semi-structured formats incompatible with traditional DBMS
• What is Big Data? • Massive volumes: petabytes to exabytes • Data types: structured, semi-structured, and unstructured • Produced rapidly and continuously • E.g., 10 TB from one jet engine in 30 mins • Why Big Data Matters • Reveals deep patterns, trends, and anomalies • Can drive insights into: • Customer behavior; Weather trends; Market activity • Need for New Tools & Technologies • Big data requires: • Advanced analytics; Non-relational systems; Cloud and distributed platforms • Goal: Extract business value from complex, diverse data
🧠 Business Intelligence Infrastructure • Goal of Business Intelligence (BI) • Provide concise, reliable info about: • Current operations • Emerging trends • Business changes • Enable better decision-making across departments • The Data Challenge • Data spread across multiple systems: • Sales, manufacturing, accounting • External sources: demographics, competitors • Big data often required for full insights
• 🛠️ BI Infrastructure Components • DataWarehouses & Data Marts • Centralized repositories for structured data • Support reporting and analysis • Hadoop • Open-source framework for processing massive datasets • Handles structured & unstructured data across distributed systems • In-Memory Computing • Analyzes big data instantly in RAM • Speeds up complex calculations and queries • Analytical Platforms • High-performance systems for data mining, predictive analytics, and machine learning • Handle large-scale structured/unstructured data analysis
🏢 DataWarehouses & Data Marts • What is a Data Warehouse? • A central database storing: • Current and historical data • From multiple sources (e.g., sales, customer service, manufacturing, websites) • Supports decision making across the company • Data Flow Process • Extract data from operational systems • Clean & transform (correct errors, fill gaps) • Load into warehouse for analysis • Data is read-only (cannot be altered)
• Key Features • Enterprise-wide access • Supports queries and analysis • Includes: • Ad hoc queries • Standardized reporting tools • Graphical dashboards • What is a Data Mart? • A subset of a data warehouse • Contains focused, summarized data • Created for specific user groups or departments
⚙️ What is Hadoop? • Open-source software framework • Managed by Apache Software Foundation • Designed for distributed parallel processing • Handles structured, semi-structured, & unstructured big data • Key Components • HDFS (Hadoop Distributed File System) Connects file systems across nodes to form a unified data system • MapReduce Breaks big data into sub-tasks, processes them in parallel, then aggregates results • HBase A non-relational database for fast access & real-time big data applications
• How Hadoop Works? • Break down a large data problem • Distribute across many low-cost servers • Process in parallel • Combine results into analyzable output • Real-World Applications • Facebook: Stores over 100 petabytes on Hadoop • Yahoo: Uses Hadoop to track & personalize user experience • NextBio: Supports genomic research with Hadoop + HBase • Vendor Support • Hadoop tools provided by: • IBM; Oracle; HP; Microsoft
⚡ In-Memory Computing: Supercharging Big Data Analytics • What is In-Memory Computing? • Stores data in main memory (RAM) instead of disks • Bypasses slow disk I/O bottlenecks • Enables real-time data access and lightning-fast analytics • Key Benefits • Faster query responses — from hours to seconds • Supports large datasets (e.g., data marts, small warehouses) • Works even on handheld/mobile devices • Ideal for complex business calculations
• Tech Enablers • High-speed multicore processors • Cheaper and more powerful RAM • Advances in hardware optimization and parallel processing • Leading In-Memory Solutions • SAP HANA (High Performance Analytics Appliance) • Oracle Exalytics • Real-World Example: Centrica • Energy utility uses SAP HANA to: • Analyze smart meter data every 15 minutes • Gain detailed insights by neighborhood, home size, business type • Show customers real-time energy usage via web/mobile tools
• 📊 Analytic Platforms: Power Tools for Big Data Insights • What Are Analytic Platforms? • High-speed systems optimized for complex analytics • Use relational + non-relational technologies • Designed for large-scale data analysis • Key Features • Preconfigured hardware + software integration • Faster query processing (10–100x vs traditional DBMS) • Support for: • In-memory computing • NoSQL databases
• 🛠️ Leading Solutions • IBM Netezza • Integrated system for database, server, and storage • Ideal for advanced analytic queries • Oracle Exadata • Engineered for enterprise-scale performance • How It Fits in BI Infrastructure • Integrates data from: • Operational systems; Web & social media; Machines & sensors; External sources • Outputs Delivered • Interactive dashboards • Standard and ad hoc reports • Fast, data-driven business insights
📊AnalyticalTools: Discovering Insights in Data • Purpose of AnalyticalTools • Go beyond data storage to extract actionable insights • Identify relationships, spot patterns, and track trends • KeyTypes of AnalyticalTools • DatabaseQuerying & Reporting • Use SQL or visual query tools • Generate summaries, reports, and answers to specific questions • OLAP (Online Analytical Processing) • Enables multidimensional analysis • Analyze data across multiple dimensions (e.g., time, region, product) • Data Mining • Uses AI and statistical methods to uncover hidden patterns • Predict outcomes (e.g., customer churn, fraud)
What is Data Mining? • Discovery-driven analysis • Finds hidden patterns, relationships, and rules in large datasets • Predicts future behavior to guide strategic decisions. • It is used in organization to do more informed decision making. • Business Benefits • Targeted marketing (e.g., 1-to-1 campaigns) • Customer segmentation • Churn prediction and retention strategies • Revenue optimization
📄 Text Mining & Web Mining: Unlocking Unstructured Data • Text Mining • Purpose: Extracts insights from unstructured data like emails, memos, call transcripts, surveys • Benefits: • Identifies patterns & relationships; Summarizes large volumes of text; Aids in decision-making • Sentiment Analysis • Detects positive, negative, or neutral sentiments in text (emails, social media, blogs, etc.) • Example: Charles Schwab uses Attensity Analyze to: • Analyze customer interactions • Detect dissatisfaction early • Act quickly to retain customers
• Web Mining: Discovery of patterns and insights from the WorldWideWeb
6.4 Managing Data resources: establishing an information policy and ensuring data quality (Page: 265) Read yourself, write a note on it, and upload on Moelium.

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Lect. 7 - MIS and business analytics.pdf

  • 1.
    Information system and BusinessAnalytics Dr. Usman Javed Riphah International University, Faisalabad.
  • 2.
    Organising data in traditionalfile system • Data Hierarchy in Computer Systems • Problems with the traditional file system
  • 3.
    Effective Information Systems •Deliver accurate, timely, and relevant information • Accurate: Free from errors or inconsistencies • Timely: Available when decision-makers need it • Relevant: Useful for the task or decision at hand The Challenge in Businesses • Many businesses lack quality information • Common issues: delayed, incorrect, or irrelevant data • Root cause: poorly organized and maintained data
  • 4.
    Why Data ManagementMatters • Ensures data is structured, clean, and accessible • Supports better decisions and efficiency • Core to building reliable information systems Traditional File Management Issues • Data stored in isolated files • Leads to duplication, inconsistency, and inefficiency • Difficult to retrieve and analyze data effectively * Data management is of paramount importance
  • 5.
    Data Hierarchy inComputer Systems • Bit: Smallest data unit a computer can process • Byte: Group of bits; represents a character (letter, number, symbol) • Field: Group of characters (e.g., name, age) • Record: Collection of related fields (e.g., student name, course, grade) • File: Group of similar records (e.g., student course file) • Database: Collection of related files (e.g., student records + financial data)
  • 6.
    Key Concepts • Entity:A person, place, thing, or event (e.g., student, course) • Attribute: Characteristic of an entity (e.g., Student_ID, Course, Grade) • FieldValues: Specific data stored for each attribute in a record For example… • Entity: COURSE • Attributes: Student_ID, Course, Date, Grade • Record: Specific student’s data for one course • File:All course records • Database: Combines course files with other student data
  • 8.
    Problems inTraditional FileEnvironment • Systems developed independently in each department • No centralized planning or integration • Each application had its own files and software programs • Example: Human Resources System • Separate files for: • Personnel, Payroll, Medical insurance, Pension, Mailing list • Leads to hundreds of isolated files over time • Key Problems • Data Redundancy: Same data stored in multiple files • Data Inconsistency: Conflicting data across systems • Difficult Maintenance: Hard to update, manage, or integrate systems • Inefficient Operations:Time-consuming and resource-heavy
  • 10.
    Data Redundancy andInconsistency • Data Redundancy: • Duplicate data stored in multiple files • Collected independently by different departments • Wastes storage and resources • Data Inconsistency: • Same attribute shows different values in different systems • Causes confusion and errors in decision-making
  • 11.
    Examples of Inconsistency •Course Data: Date updated in one system but not others • Student_ID: Named differently across systems (e.g., Student_ID vs. ID) • Coding Differences: • Clothing size represented as “extra large” in one system, “XL” in another • Leads to integration issues across departments Impact on Business Systems • Hard to build integrated systems like: • Customer Relationship Management (CRM) • Supply Chain Management (SCM) • Enterprise Systems (ERP) • Results in poor data quality and decision-making
  • 12.
    Program-Data Dependence • Dataand programs are tightly linked • Each program must define data format and location Key Issues • Any change in program may require changes to the data • Data format changes can break multiple programs • Example: Changing ZIP code from 5-digit to 9-digit • Affects all programs using the old 5-digit format Consequences • High maintenance costs • Lack of flexibility in updating systems • Increases risk of errors and system failures
  • 13.
    Lack of Flexibility •Can generate routine, scheduled reports only • Ad hoc reports are difficult and time-consuming to produce • Cannot easily respond to unexpected information needs Challenges • Data is available but hard to access • Requires extensive programming • May take weeks of effort to compile needed data Impact • Slows down decision-making • Increases costs and delays • Limits the organization’s ability to be agile and responsive
  • 14.
    Poor Security • Lackof control over data access and usage • Unauthorized access and data changes are possible • No centralized way to track user activity Key Risks • Sensitive data may be exposed or altered • No audit trail to monitor who accessed or modified information • Increases chances of data breaches and internal misuse Impact • Compromises trust and data integrity • Can lead to legal issues and financial losses • Weakens overall information system security
  • 15.
    Lack of DataSharing and Availability • Data is stored in isolated systems across departments • No connection between different data files • Makes data sharing across functions very difficult Key Issues • Slow access to required information • Inconsistent data leads to distrust and confusion • Users may avoid using systems due to unreliable data Impact • Breakdown in communication between departments • Limits collaboration and informed decision-making • Reduces overall efficiency and effectiveness
  • 16.
    The database approachto data management • Database management systems (DBMS) • How DBMS solve the problems of traditional file system • Relational DBMS • Operations of relational DBMS • Non-Relational databases in the Cloud • The database approach to data management • Querying and reporting • Designing Databases • Normalization and Entity relationship diagrams
  • 17.
    Database Technology: ABetter Approach • Centralized collection of organized data • Serves multiple applications efficiently • Eliminates redundancy by storing data once • Key Benefits • Data appears to be in one location for all users • Supports data sharing across departments • Reduces data inconsistency and duplication • Use one centralized Human Resources database • Impact • Improved accuracy, accessibility, and efficiency • Easier maintenance and updates • Enables better decision-making and integration
  • 18.
    Database management system:Whatis a DBMS? • Software that centralizes data and manages it efficiently • Acts as an interface between applications and data files • Provides easy access to data for multiple programs Key Functions • Retrieves data when requested by applications • Separates logical and physical views of data • Logical view: How users see the data • Physical view: How data is stored Benefits of DBMS • Reduces programming effort • Allows multiple users to access customized views • Ensures data consistency and security Example: Human Resources Database • Benefits specialist sees: Name, SSN, Health Insurance, etc.
  • 20.
    How a DBMSSolves Traditional File System Problems • Reduces data redundancy by minimizing repeated data • Helps maintain data consistency across all systems • Decouples programs and data, allowing independent management Key Advantages • Improves data accuracy and reliability • Enables centralized data management and security • Supports ad hoc queries for flexible data access Efficiency Gains • Faster access to information • Lower development and maintenance costs • Enhanced information sharing across departments
  • 22.
    Relational DBMS (RDBMS) •Most common type of Database Management System • Stores data in two-dimensional tables (relations) • Each table represents an entity and its attributes Key Features • Easy to organize, access, and update data • Supports relationships between tables using keys • Ideal for small to large-scale applications Popular RDBMS Examples • Microsoft Access – for desktop systems • MySQL – open-source, widely used • Oracle Database, SQL Server, DB2 – for enterprise use • Oracle Database Lite – for mobile devices
  • 23.
    Organizing Data inRelational Databases • Data is stored in tables (also called relations) • Each table contains rows (records/tuples) and columns (fields/attributes) • Separate tables are created for different entities (e.g., SUPPLIER, PART) Structure of aTable • Columns = Attributes (e.g., name, address, zip code) • Rows = Records (each row = one instance of the entity) • Fields = Individual data elements Key Concepts • Primary Key: Uniquely identifies each record in a table (e.g., Supplier_Number) • Foreign Key: A field in one table that links to the primary key in another table (e.g., Supplier_Number in PART table) • Enables relationships between tables
  • 25.
    • Key Operationsof a Relational DBMS • Relational tables can be combined using shared data elements (e.g., Supplier_Number) • Three basic operations: Select, Join, and Project • 1. Select Operation • Retrieves rows (records) that meet a specific condition • Example: Select parts where Part_Number = 137 or 150 • 2. Join Operation • Combines tables based on a common field • Example: Join PART and SUPPLIER tables using Supplier_Number to get supplier info for selected parts
  • 26.
    • 3. ProjectOperation • Retrieves specific columns from a table • Example: Extract only Part_Number, Part_Name, Supplier_Number, Supplier_Name • Use Case Example • Goal: Find suppliers for Part 137 or 150 • Use Select → Join → Project to get clear and focused data output
  • 27.
    📦 Non-Relational Databases& Cloud Databases • Need for Alternatives toTraditional Databases • Traditional relational databases: based on tables, columns, and rows • Not ideal for big data, web services, and unstructured data • Rise of cloud computing and massive data volumes demands flexible solutions • What Are Non-Relational (NoSQL) Databases? • Use a flexible data model (no fixed schema) • Designed for large-scale, distributed data processing • Handle structured + unstructured data (e.g., social media, web content, images) • Examples: • Oracle NoSQL Database • Amazon SimpleDB
  • 28.
    • Cloud-Based RelationalDatabases • Offer traditional RDBMS features on a cloud platform • Examples: • Amazon RDS (MySQL, Oracle, SQL Server) • Oracle Database Cloud Service • Microsoft SQL Azure Database • Private Cloud Databases • Private cloud = secure, internal cloud infrastructure • Example: Sabre Holdings (aviation industry SaaS provider) • RunsOracle Database 11g; Supports 100+ projects & 700 users • Reduces costs & improves performance • Key Benefits • Scalability (up or down as needed) • Cost efficiency (pay-per-use model) • Improved data management for web and mobile applications • Better resource utilization through consolidation
  • 29.
    🛠️ Core Capabilitiesof a DBMS • Data Definition Language (DDL) • Defines structure and content of the database • Used to create tables, define fields, and their characteristics • Examples of definitions: data type, field size, constraints • Data Dictionary • A repository of metadata (data about data) • Stores details like: • Field names & descriptions • Data types & sizes • Formats & validation rules • Key fields (e.g., Supplier_Number in Access)
  • 30.
    • Advanced DataDictionary Functions (Large Systems) • Tracks: • Data usage and ownership • Authorization and access levels • Responsible users or departments • Linked programs, reports, and business functions • Example: Microsoft Access • Offers basic data dictionary functionality • Shows: • Field names • Formats • Key fields (indicated by a key icon)
  • 31.
    • 🔍Querying andReporting with DBMS • Data Manipulation Language (DML) • Used to add, update, delete, and retrieve data • Allows end users and developers to extract and work with data • Most popular DML: SQL (Structured Query Language) • SQL – Structured Query Language • Industry-standard for querying relational databases • Example SQL:
  • 32.
    • DBMSTools forQuerying • DB2, Oracle, SQL Server: use SQL • MicrosoftAccess: • Uses SQL behind the scenes • Provides graphical tools for easy query building • Users select tables, fields, and set conditions visually • Reporting Features • Organize retrieved data into polished reports • Access simplifies reporting with built-in templates and layouts
  • 33.
    6.3: USING DATABASESTOIMPROVE BUSINESS PERFORMANCEAND DECISION MAKING • The challenge of big data • Business intelligence infrastructure • Data warehouse and marts; Hadoop; In-memory computing;Analytic platforms • ANALYTICALTOOLS: RELATIONSHIPS, PATTERNS, TRENDS • Online analytical processing • Data mining • Text mining and web mining
  • 34.
    • Businesses usedatabases to keep track of basic transactions, such as paying suppliers, processing orders, keeping track of customers, and paying employees. • But they also need databases to provide information that will help the company run the business more efficiently, and help managers and employees make better decisions. • If a company wants to know which product is the most popular or who is its most profitable customer, the answer lies in the data.
  • 35.
    🌐The Challenge ofBig Data • Traditional Data Limitations • Mostly transaction-based, fitting neatly in rows & columns • Managed well by relational DBMS • Examples: invoices, payments, payroll records Explosion of New Data Sources • Web traffic, social media posts (tweets, status updates) • Email messages, sensor outputs, and machine-generated data • Unstructured/semi-structured formats incompatible with traditional DBMS
  • 36.
    • What isBig Data? • Massive volumes: petabytes to exabytes • Data types: structured, semi-structured, and unstructured • Produced rapidly and continuously • E.g., 10 TB from one jet engine in 30 mins • Why Big Data Matters • Reveals deep patterns, trends, and anomalies • Can drive insights into: • Customer behavior; Weather trends; Market activity • Need for New Tools & Technologies • Big data requires: • Advanced analytics; Non-relational systems; Cloud and distributed platforms • Goal: Extract business value from complex, diverse data
  • 37.
    🧠 Business IntelligenceInfrastructure • Goal of Business Intelligence (BI) • Provide concise, reliable info about: • Current operations • Emerging trends • Business changes • Enable better decision-making across departments • The Data Challenge • Data spread across multiple systems: • Sales, manufacturing, accounting • External sources: demographics, competitors • Big data often required for full insights
  • 38.
    • 🛠️ BIInfrastructure Components • DataWarehouses & Data Marts • Centralized repositories for structured data • Support reporting and analysis • Hadoop • Open-source framework for processing massive datasets • Handles structured & unstructured data across distributed systems • In-Memory Computing • Analyzes big data instantly in RAM • Speeds up complex calculations and queries • Analytical Platforms • High-performance systems for data mining, predictive analytics, and machine learning • Handle large-scale structured/unstructured data analysis
  • 39.
    🏢 DataWarehouses &Data Marts • What is a Data Warehouse? • A central database storing: • Current and historical data • From multiple sources (e.g., sales, customer service, manufacturing, websites) • Supports decision making across the company • Data Flow Process • Extract data from operational systems • Clean & transform (correct errors, fill gaps) • Load into warehouse for analysis • Data is read-only (cannot be altered)
  • 40.
    • Key Features •Enterprise-wide access • Supports queries and analysis • Includes: • Ad hoc queries • Standardized reporting tools • Graphical dashboards • What is a Data Mart? • A subset of a data warehouse • Contains focused, summarized data • Created for specific user groups or departments
  • 41.
    ⚙️ What isHadoop? • Open-source software framework • Managed by Apache Software Foundation • Designed for distributed parallel processing • Handles structured, semi-structured, & unstructured big data • Key Components • HDFS (Hadoop Distributed File System) Connects file systems across nodes to form a unified data system • MapReduce Breaks big data into sub-tasks, processes them in parallel, then aggregates results • HBase A non-relational database for fast access & real-time big data applications
  • 42.
    • How HadoopWorks? • Break down a large data problem • Distribute across many low-cost servers • Process in parallel • Combine results into analyzable output • Real-World Applications • Facebook: Stores over 100 petabytes on Hadoop • Yahoo: Uses Hadoop to track & personalize user experience • NextBio: Supports genomic research with Hadoop + HBase • Vendor Support • Hadoop tools provided by: • IBM; Oracle; HP; Microsoft
  • 43.
    ⚡ In-Memory Computing:Supercharging Big Data Analytics • What is In-Memory Computing? • Stores data in main memory (RAM) instead of disks • Bypasses slow disk I/O bottlenecks • Enables real-time data access and lightning-fast analytics • Key Benefits • Faster query responses — from hours to seconds • Supports large datasets (e.g., data marts, small warehouses) • Works even on handheld/mobile devices • Ideal for complex business calculations
  • 44.
    • Tech Enablers •High-speed multicore processors • Cheaper and more powerful RAM • Advances in hardware optimization and parallel processing • Leading In-Memory Solutions • SAP HANA (High Performance Analytics Appliance) • Oracle Exalytics • Real-World Example: Centrica • Energy utility uses SAP HANA to: • Analyze smart meter data every 15 minutes • Gain detailed insights by neighborhood, home size, business type • Show customers real-time energy usage via web/mobile tools
  • 45.
    • 📊 AnalyticPlatforms: Power Tools for Big Data Insights • What Are Analytic Platforms? • High-speed systems optimized for complex analytics • Use relational + non-relational technologies • Designed for large-scale data analysis • Key Features • Preconfigured hardware + software integration • Faster query processing (10–100x vs traditional DBMS) • Support for: • In-memory computing • NoSQL databases
  • 46.
    • 🛠️ LeadingSolutions • IBM Netezza • Integrated system for database, server, and storage • Ideal for advanced analytic queries • Oracle Exadata • Engineered for enterprise-scale performance • How It Fits in BI Infrastructure • Integrates data from: • Operational systems; Web & social media; Machines & sensors; External sources • Outputs Delivered • Interactive dashboards • Standard and ad hoc reports • Fast, data-driven business insights
  • 47.
    📊AnalyticalTools: Discovering Insightsin Data • Purpose of AnalyticalTools • Go beyond data storage to extract actionable insights • Identify relationships, spot patterns, and track trends • KeyTypes of AnalyticalTools • DatabaseQuerying & Reporting • Use SQL or visual query tools • Generate summaries, reports, and answers to specific questions • OLAP (Online Analytical Processing) • Enables multidimensional analysis • Analyze data across multiple dimensions (e.g., time, region, product) • Data Mining • Uses AI and statistical methods to uncover hidden patterns • Predict outcomes (e.g., customer churn, fraud)
  • 48.
    What is DataMining? • Discovery-driven analysis • Finds hidden patterns, relationships, and rules in large datasets • Predicts future behavior to guide strategic decisions. • It is used in organization to do more informed decision making. • Business Benefits • Targeted marketing (e.g., 1-to-1 campaigns) • Customer segmentation • Churn prediction and retention strategies • Revenue optimization
  • 50.
    📄 Text Mining& Web Mining: Unlocking Unstructured Data • Text Mining • Purpose: Extracts insights from unstructured data like emails, memos, call transcripts, surveys • Benefits: • Identifies patterns & relationships; Summarizes large volumes of text; Aids in decision-making • Sentiment Analysis • Detects positive, negative, or neutral sentiments in text (emails, social media, blogs, etc.) • Example: Charles Schwab uses Attensity Analyze to: • Analyze customer interactions • Detect dissatisfaction early • Act quickly to retain customers
  • 51.
    • Web Mining:Discovery of patterns and insights from the WorldWideWeb
  • 52.
    6.4 Managing Dataresources: establishing an information policy and ensuring data quality (Page: 265) Read yourself, write a note on it, and upload on Moelium.