Data Warehouse, BI, and Data Analysis Fundamentals
What is a Data Warehouse?
A data warehouse is a centralized repository designed to store large volumes of structured, semi-structured, and sometimes unstructured data.
Characteristics of a Data Warehouse
Subject-Oriented
- Organized around major subjects like customers, sales, or products, rather than business operations.
- Focuses on the specific areas of analysis rather than the detailed operations of individual systems.
Integrated
- Data from different sources is cleaned, transformed, and standardized to ensure consistency.
- Resolves data inconsistencies in formats, naming conventions, and units of measure.
Time-Variant
- Stores historical data, making it easier to analyze trends over time.
- Data is timestamped to track changes and allow comparisons over specific periods.
Differentiate DM, ODS, and EDW
Major Components of a Data Warehouse
1. Data Source
- Definition: The origin of raw data to be integrated into the data warehouse.
- Role: Supplies raw transactional and operational data for analysis.
2. Data Extraction
- Definition: The process of retrieving raw data from multiple heterogeneous sources.
- Purpose: Collect data efficiently without disrupting source operations.
3. Data Loading
- Definition: The process of transferring processed data into the data warehouse or staging area.
- Purpose: Ensures that the warehouse has up-to-date and accurate information for analysis.
4. Middleware Tools
- Definition: Software that connects the data warehouse with front-end tools or applications for data access and analysis.
- Purpose: Provides a bridge between the data warehouse and user interfaces.
Simon’s 4 Phases of DM
1. Intelligence Phase
- Definition: In this phase, the decision-maker identifies and defines the problem or opportunity.
- Purpose: Ensure a clear understanding of the problem to set the stage for effective decision-making.
2. Design Phase
- Definition: In this phase, potential solutions or courses of action are developed and analyzed.
- Purpose: Explore all possible options to address the problem effectively.
3. Choice Phase
- Definition: The decision-maker selects the best solution or course of action from the available alternatives.
- Purpose: Make a rational and informed decision that aligns with objectives.
4. Implementation Phase
- Definition: The chosen solution is put into action, and its outcomes are monitored.
Purpose: Translate the decision into tangible results and ensure its success.
Evolution of BI
Business Intelligence (BI) has evolved significantly over the years,
1. Early Days: Data Collection and Reporting (1950s–1970s)
- Focus: Basic data collection and manual reporting.
- Technology: Early mainframe systems were used for data storage and basic computational tasks.
2. Emergence of Decision Support Systems (DSS) (1970s–1980s)
- Focus: Supporting management decisions with structured data and analytical tools.
- Technology: Development of relational databases and DSS tools.
3. Birth of Modern BI (1980s–1990s)
- Focus: Integrated tools for data analysis and visualization.
- Technology: Emergence of data warehouses, OLAP (Online Analytical Processing), and ETL processes.
4. Self-Service BI (2000s)
- Focus: Empowering end-users with tools to analyze and visualize data without IT assistance.
- Technology: Rise of self-service BI tools like Tableau, Power BI, and Qlik.
5. Big Data and Advanced Analytics (2010s)
- Focus: Managing and analyzing vast volumes of structured and unstructured data.
- Technology: Big data platforms like Hadoop, Spark, and cloud-based solutions.
6. Modern BI and Augmented Analytics (2020s and Beyond)
- Focus: Automating insights with AI and enhancing decision-making with augmented analytics.
- Technology: Cloud-native BI platforms, AI-driven tools, and natural language processing (NLP).
Types of Analysis
Descriptive Analytics
- Definition: Focuses on summarizing and interpreting historical data to understand what has happened.
- Purpose: Provides insights into past performance and trends.
- Output: Answers questions like “What happened?” and “What are the trends?”
2. Predictive Analytics
- Definition: Uses statistical models, machine learning, and algorithms to analyze historical data and predict future outcomes.
- Purpose: Helps forecast what is likely to happen.
- Output: Answers questions like “What is likely to happen?” and “What are the potential risks?”
3. Prescriptive Analytics
- Definition: Provides actionable recommendations by analyzing data and evaluating possible outcomes.
- Purpose: Guides decision-making by suggesting optimal actions.
- Output: Answers questions like “What should we do?” and “What is the best course of action?