Business Intelligence (BI) Concepts PDF

1. Introduction to OLTP and OLAP OLTP (Online Transaction Processing): o Focuses on managing transaction-oriented applications. o Used for day-to-day operations like order processing, retail sales, and financial transactions. o Ch...

1. Introduction to OLTP and OLAP OLTP (Online Transaction Processing): o Focuses on managing transaction-oriented applications. o Used for day-to-day operations like order processing, retail sales, and financial transactions. o Characteristics: High transaction volume, quick response time, normalized database structure. OLAP (Online Analytical Processing): o Designed for analytical queries and decision-making. o Helps in multidimensional analysis of business data for insights and trends. o Characteristics: Data aggregation, complex queries, denormalized database structure. 2. BI Definitions & Concepts Business Intelligence (BI): o Refers to technologies, processes, and applications used to collect, integrate, analyze, and present business data. o Goal: To support better decision-making. o Components: Data warehousing, data mining, dashboards, and reporting tools. Key Concepts in BI: o Data Warehousing: Centralized repository for storing historical data. o Analytics: Using data analysis techniques to find patterns. o Visualization: Presenting data using charts, graphs, and dashboards. o Performance Management: Measuring and optimizing organizational performance. 3. Business Applications of BI Customer Relationship Management (CRM): Understanding customer behavior and preferences. Supply Chain Management (SCM): Optimizing logistics and inventory. Financial Analysis: Budgeting, forecasting, and profitability analysis. Marketing: Campaign performance tracking and segmentation. Human Resources: Workforce analytics and employee performance evaluation. 4. BI Framework Data Sources: Operational systems, external sources, and unstructured data. ETL (Extract, Transform, Load): Processes raw data into usable formats. Data Warehousing: Stores integrated and historical data. BI Tools and Applications: Includes reporting, dashboards, and analytics tools. Users: Business analysts, managers, and executives. 5. Role of Data Warehousing in BI Centralizes data from diverse sources. Supports historical analysis and trend identification. Ensures data consistency and reliability. Enables faster query performance by optimizing data for analysis. 6. BI Infrastructure Components BI Process: Data collection → Integration → Storage → Analysis → Reporting → Decision- making. BI Technology: ETL tools, data warehousing, OLAP engines, visualization tools. BI Roles & Responsibilities: BI analysts, architects, developers, and data engineers manage data integration, create reports, and support decision-making. 7. Extraction, Transformation, and Loading (ETL) Extraction: Retrieving data from various sources. Transformation: Cleaning and converting data into the required format. Loading: Storing the processed data in a data warehouse or repository. 8. Concepts of Data Integration Need: Organizations use diverse systems leading to fragmented data. Integration consolidates data for unified analysis. Advantages: o Improved decision-making with complete datasets. o Increased operational efficiency. o Consistency across applications. 9. Introduction to SSIS (SQL Server Integration Services) A Microsoft tool for data integration and workflow automation. Used for ETL operations. Features: Data cleansing, transformation, and loading into various systems. 10. Introduction to Data Quality and Data Profiling Concepts Data Quality: Refers to accuracy, completeness, consistency, and reliability of data. Data Profiling: Analyzing data to understand its structure and content. o Applications: Identifying anomalies, ensuring data accuracy, and preparing for integration. 11. Power Query A data connection and transformation tool in Excel and Power BI. Features: o Import data from multiple sources. o Transform data using a user-friendly interface. o Automate data cleaning processes. 12. Different Views in Power BI Report View: Create and edit reports using visualizations. Data View: View and transform tabular data. Model View: Manage relationships and hierarchies between tables. 13. Cardinality in Power BI Refers to the uniqueness of data values in a column. Types: o High Cardinality: Many unique values. o Low Cardinality: Few unique values. o Crucial in defining relationships between tables in Power BI. 14. Introduction to Data and Dimension Modeling Data Modeling: Structuring data for efficient analysis. Dimension Modeling: Organizing data into dimensions (attributes) and facts (measurable data). 15. Multidimensional Data Model Organizes data into cubes with dimensions and facts. Enables slicing, dicing, and drill-down operations for detailed analysis. 16. ER Modeling vs. Multidimensional Modeling ER Modeling: o Used in OLTP systems. o Focuses on relational structure. Multidimensional Modeling: o Used in OLAP systems. o Focuses on analytical queries with facts and dimensions. 17. Concepts of Dashboards Definition: A visual interface showing key performance indicators (KPIs). Uses: Real-time monitoring, trend analysis, and quick decision-making. Features: Interactive visualizations, customizable layouts, and data drill-down capabilities.