1. What is Data?
- Definition: Raw material of information systems; a valuable organizational resource that needs effective management.
- Characteristics:
- Generated from various transactions.
- Stored, processed, and analyzed using DBMS.
- Shows relationships between organizational entities (e.g., sales, customers, competitors, market).
2. Types of Data:
- Traditional Alphanumeric Data: Numbers, letters, characters.
- Text Data: Sentences, paragraphs.
- Image Data: Graphics, figures, photographs.
- Video and Audio Data.
3. How is Data Used in Information Systems?
- Storage and Processing: Stored and processed in databases and knowledge bases.
- Analysis: Used to analyze product sales, generate reports, and support decision-making.
- Information Derivation: Derived from processing data, analyzing it, and sharing insights.
4. What is Data Management?
- Definition: Managerial activity applying information system technology to manage organizational data resources.
- Tools: Includes DBMS, data warehousing, data mining.
5. Types of Databases:
- Centralized: Single location.
- Distributed: Resides on network servers, improves access and performance.
- Relational, NoSQL, Cloud, Object-Oriented, Hierarchical, Personal.
- Operational Database: Stores detailed information for business processes (e.g., customer, HR, inventory).
- Web-based Database: Stores information in hypermedia format with hyperlinks and multimedia elements.
6. What is Data Warehousing?
- Definition: Technique to collect and manage data from various sources for business insights.
- Storage: Stores extracted data from operational, external, and other databases.
- Purpose: Provides a central source of cleaned, transformed, and categorized data for analysis and data mining.
7. Types of Data Warehouses:
- Enterprise Data Warehouse: Centralized for decision support across the organization.
- Operational Data Warehouse: Stores data for routine activities.
- Data Mart: Subset of data warehouse for specific business lines (e.g., sales, finance).
8. Components of Data Warehouses:
- Databases: Operational, external, and other.
- Data Acquisition Center.
- Data Management: Including metadata.
- Data Analysis System.
- Web Information System.
9. Benefits of Data Warehousing:
- Access: Quick access to critical data from various sources.
- Consistency: Consistent information for cross-functional activities.
- Performance: Reduced stress on production systems.
- Analysis: Faster analysis and reporting turnaround time.
- User Access: Easier user access for reporting and analysis.
10. Disadvantages of Data Warehousing:
- Unstructured Data: Not suitable for unstructured data.
- Implementation: Time-consuming creation and implementation.
- Data Freshness: Data can become outdated quickly.
- Changes: Difficult to make changes to data types, ranges, sources, and queries.
- Resources: Requires significant resources for training and implementation.
11. What is Data Mining?
- Definition: Analyzing data in a data warehouse to reveal hidden patterns and trends in historical business activities.
- Purpose: Helps managers make strategic decisions to gain a competitive advantage.
- Function: Discovers correlations, patterns, and trends in vast amounts of data.
12. Common Uses of Data Mining:
- Market Analysis: New product identification.
- Root Cause Analysis: Quality and manufacturing issues.
- Customer Management: Attrition prevention and new customer acquisition.
- Profiling: Improved customer profiling.
13. What is Knowledge Management?
- Definition: Process of identifying, capturing, organizing, and utilizing an organization’s collective knowledge to enhance performance and decision-making.
- Types of Knowledge:
- Explicit: Easily articulated and transferred.
- Tacit: Intangible and difficult to transfer.
- Intellectual.
14. Knowledge Management in Information Systems:
- Management: Systematic management of an organization’s knowledge assets.
- Activities: Capturing, organizing, storing, retrieving, and disseminating knowledge.
- Goals: Enhance decision-making, foster innovation, and improve performance.
15. Driving Forces behind Knowledge Management:
- External Factors:
- Globalization: Access to wider markets and resources.
- Customer Demands: Adaptation and value-added services.
- Competitors: Innovative competitors pushing for better services and technology.
- Vendors: Resourceful vendors providing better features and logistics.
- Internal Forces:
- Organizational Effectiveness: Efficient operations and seizing opportunities.
- Technology: Capability for effective business operations.
- Human Resources: Effectiveness and understanding of knowledge base.
16. Benefits of Knowledge Management:
- Sharing: Promotes knowledge sharing within the organization.
- Protection: Protects intellectual capital by centralizing knowledge.
- Value: Values employees and their knowledge contributions.
- Storage: Captures and stores knowledge for future use.
17. Challenges of Knowledge Management:
- Culture: Creating a flexible culture of collaboration and overcoming resistance to change.
- Security: Ensuring security of sensitive information and intellectual capital.
- Measurement: Measuring and quantifying knowledge, especially tacit knowledge.
- Storage: Efficiently storing and managing documents.
- Dissemination: Effectively disseminating knowledge and making it accessible.
- Improvement: Continuously improving the knowledge management system.
18. Working Models of Knowledge Management:
- People-Centric: Focuses on relationships, learning communities, and informal knowledge sharing.
- Tech-Centric: Emphasizes technology for knowledge storage, transfer, and sharing.
- Process-Centric: Develops standard processes for knowledge management, including organizational hierarchy and culture.
19. What is Business Intelligence (BI)?
- Definition: Processes, technologies, and strategies used to gather, analyze, and transform raw data into valuable insights for informed business decisions.
- Purpose: Helps companies understand performance, identify trends, and gain a competitive advantage.
- Origin: Coined by IBM researcher Hans Peter Luhn in 1958.
20. Components of BI:
- Data Mining
- Online Analytical Processing (OLAP)
- Querying
- Reporting
21. Purpose of BI:
- Insights: Gain valuable insights from large volumes of data.
- Transformation: Transform raw data into meaningful information.
- Decision-Making: Help executives, managers, and workers make better business decisions.
22. Benefits of BI:
- Decision-Making: Accelerated and improved decision-making.
- Processes: Optimized internal business processes.
- Efficiency: Increased operational efficiency.
- Revenue Models: Identification of new work areas and revenue models.
- Competitive Advantage: Competitive advantage through data analysis.
- Market Trends: Identification of market trends for better decision-making.
- Problem Identification: Quick identification of business problems.
23. How BI is Used in Organizations:
- Customer Analysis: Analyzing customer behavior, buying patterns, and sales trends.
- Performance Measurement: Measuring, tracking, and predicting sales and financial performance.
- Planning: Budgeting, financial planning, and sales forecasting.
- Campaign Tracking: Performance tracking of marketing campaigns.
- Optimization: Optimizing processes and operational performance.
- Supply Chain: Improving delivery and supply chain effectiveness.
- E-Commerce: Conducting web and e-commerce data analytics.
- CRM: Enhancing customer relationship management.
- Risk Analysis: Performing risk analysis in uncertain market environments.
24. Tools & Techniques of BI:
- Online Analytical Processing (OLAP): Visualizes and summarizes data for multi-dimensional analysis.
- Market Basket Analysis: Analyzes relationships between frequently purchased products for cross-selling and personalized recommendations.
25. Process of BI System Creation:
- Acquisition: Data acquisition from various sources.
- Integration: Data integration.
- Cleanup: Data cleanup and formatting.
- Searching: Searching for relevant data.
- Analysis: Data analysis for insights.
- Formatting: Formatting data for easy understanding.
- Loading: Loading data into the BI data warehouse.
26. BI Tools:
- Sisense: Collects, analyzes, and uses information for better decision-making.
- Looker: Provides data discovery and intuitive data exploration.
- Datapine: BI and data visualization tool for action plans and smart decisions.
- Zoho Analytics: Cloud-based platform for online reporting and drill-down analytics.
27. Choosing the Right BI Software:
- Decision Making: Analyze how executives make decisions.
- Information Needs: Identify needs for quick and accurate decision-making.
- Data Quality: Pay attention to data quality.
- Performance Metrics: Define relevant performance metrics.
- Influencing Factors: Identify factors influencing performance.
28. Steps to Implement BI System:
- Data Accuracy: Ensure data is accurate, relevant, and complete.
- Training: Train users on BI application functions.
- Deployment: Deploy BI quickly and make adjustments as needed.
- Objectives: Focus on achieving business objectives.
- Integration: Take an integrated approach to data warehouse building.
- ROI: Define return on investment before implementation.
29. Problems Faced in BI Implementation:
- Resistance: User resistance due to fear and cultural differences.
- Data Quality: Irrelevant and poor quality data.
- Tool Selection: Wrong selection of BI tools.
- Business Processes: Lack of understanding of business processes.
- Management Practices: Absence of standard management practices.
