SI - Parte 2 - DW-2

Overview of Data Warehousing

  • Definition: A Data Warehouse (DW) is a collection of data that is themed, integrated, non-volatile, and variant over time to support managerial decision-making processes.

    • Inmon: "A Data Warehouse is a collection of data, oriented by themes... for management decision support."

    • Devlin: "A Data Warehouse is a unique, complete, and consistent repository of data from various sources that is available to end users in a comprehensible form."

Characteristics of Data Warehousing

  • Integration of Techniques and Technologies:

    • Focused on providing systematic access to information distributed across multiple organizational systems.

    • Gathers heterogeneous data sources including historical data for reporting and decision support.

  • Ongoing Process:

    • Collects data over time, giving users the ability to perform structured queries, ad-hoc reporting, and analytical support.

Purpose of a Data Warehouse

  • Enhancement of Information Utilization:

    • A DW is not the end; it serves as a means for companies to analyze data, aiding both current processes and future improvements.

  • Centralized View:

    • Integrates corporate data into a single repository for holistic access.

    • Provides a unified and centralized view of data that may be dispersed across various databases.

  • User Empowerment:

    • Enables end-users to run queries, create reports, and perform analyses without reliance on IT or technical resources.

Importance of Data Warehousing

  • Strategic Resource:

    • Information derived from historical data on sales, production, customers, etc., becomes crucial for strategic decision-making.

    • Enhances the company's competitive edge through data-driven insights.

  • Addressing Information Challenges:

    • Companies face difficulties in extracting valuable information as data volume increases and source inconsistency prevails.

    • A DW is critical to manage complexities arising from data growing volume and variety.

Essential Features of Data Warehousing

  • Integration of Multiple Sources:

    • Combines data from diverse systems while ensuring high-quality information that meets various user needs.

    • Facilitates analyses without affecting operational data environments.

  • Flexibility and Agility:

    • Adapts easily to new requirements for analysis, ensuring continuous support for decision-making processes.

Evolution of Data Warehousing

  • Historical Context:

    • Evolution through four generations of computing environments:

      1. Formation: Initial applications tailored to immediate business needs.

      2. Proliferation: Emphasis on effectiveness and efficiency, leading to interconnected systems.

      3. Dispersion: Emergence of end-users using personal tools for information extraction.

      4. Unification: Adoption of a new organizational approach through Data Warehousing.

Data Warehouse Structure and Analysis

  • Operational vs. Informational Databases:

    • Accessing data within operational databases is timely but often limited to current snapshots.

    • Informational databases, contrastingly, allow for historical analysis and support complex queries and decision-making.

  • Data Warehouse Objectives:

    • To make organizational information more accessible, presenting data consistently while maintaining quality and supporting inevitable changes in business requirements.

Benefits of Implementing a Data Warehouse

  1. Subject Orientation: Data organized by subject rather than by application, making navigation intuitive.

  2. Integration of Diverse Data: Combines information from many sources, thereby enhancing analytic capability.

  3. Temporal Analysis: Enables analysis over different time horizons, essential for trend analysis.

  4. Ad-Hoc Reporting: Users can generate reports flexibly without heavy reliance on IT support.

  5. Enhanced Analytical Capability: Provides advanced tools for multidimensional analysis, assisting decision-makers.

  6. Reduced IT Development Overload: Allows users to independently obtain information previously inaccessible which lessens the IT burden.

  7. Improved Performance: Streamlined queries lead to better performance during complex analytic tasks, allowing repeatable, predictable analysis.

  8. Non-intrusive to Operational Systems: Minimizes the impact on transaction processing in operational systems.

  9. Transformation of Data to Strategic Information: Empowers companies to effectively leverage information for competitive advantage.

  10. Facilitates Business Process Reengineering: Provides access to underlying data, fostering opportunities for innovation.