05 - BI and Warehousing - slides
Course Information
Institution: Mittuniversitetet, Mid Sweden University
Course: Introduction to Business Intelligence
Teacher: Ivika Jäger
Date: November 2024
Managerial Decision Making
Making decisions based on goals includes:
Historically: Intuition and experience used.
Modern Approach: Increasing focus on data-driven decisions.
Importance of detailed on-demand reporting and advanced analytics to project potential outcomes.
Decision-Making Process
Intelligence: Define and analyze the problem.
Design: Develop models to test solutions.
Choice: Select the most viable solution.
Implementation: Execute the solution and monitor progress.
Feedback and progress monitoring are crucial for success.
Effective Problem Solving
Types of Problems:
Structured (well-defined)
Unstructured (complex)
Strategies:
Decompose problems for easier management.
Assign ownership and authority appropriately.
Decision Support Systems (DSS)
Definition: High-performance computer dashboard aiding in data analysis.
Integrates data from various sources, enabling simulation of scenarios.
Not limited to one specific function; encompasses business reporting, data analysis, etc.
Evolution of DSS
1970s: Focus on periodic reporting.
1980s: Growth of on-demand reporting due to digitization.
1990s: Introduction of BI systems and data warehousing.
2000s: Emergence of data mining and SaaS for accessible analytics.
2010s: Trends towards big data and real-time analytics.
2020s: Adoption of AI, deep learning, and IoT for automated analysis.
Business Intelligence (BI)
Definition: Architecture, tools, and methodologies that provide insights through data manipulation and analysis.
Focuses primarily on descriptive analytics to convert data into actionable information.
Data Analytics and Business Intelligence
Types of Analytics:
Descriptive: What happened?
Predictive: What will happen?
Prescriptive: What should I do?
Utilization of dashboards, scorecards, and implementation of data mining for better strategic decisions.
Tools for Business Intelligence
Popular BI tools include:
Excel: For data analysis.
SQL: For database management.
PowerBI: For visualizations.
Python and R: For data science applications.
Data Science vs. Analytics
Analytics (BI): Reporting focused (Excel, SQL, PowerBI).
Data Science: Predictions and optimization driven (Python, R).
Note: Both fields complement each other.
Careers in Data
Related roles include:
Analyst (business, strategic, quantitative)
Statistician/Researcher
BI/ML/AI Engineer
Data Scientist
Database Administrator
Data Warehousing
Definition: Stores data for analysis and decision-making across multiple sources.
Designed for large-scale analytical processing rather than transaction processing.
Benefits and Costs of Data Warehousing
Benefits:
Flexible and faster decision-making.
Consolidation of corporate data.
Reduced processing costs.
Costs:
Hardware, software, and network expenses.
Training costs for users.
Keys to Success in DW Implementation
Prioritize user involvement in development.
Ensure proper training and technical support.
Maintain continuous feedback during the process.
Data Integration and Management
ETL Pipeline:
Utilize APIs and web scraping for data extraction.
Clean and organize data before loading into the warehouse at scheduled intervals.
Maintain metadata and error management for system integrity.
Data Warehouse Administrator Role
Management of both technical and business aspects of DW.
Requires a mix of technical skills, business knowledge, and communication.
AI may assist in documentation but cannot replace human oversight.
Data Lakes
Definition: Store raw, unstructured data for flexible analysis.
Ideal for technical users; complement data warehousing for non-technical users.