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MM - Data Analytical Architecture

Creating a mind map for Data Analytical Architecture involves identifying key concepts and their relationships. Here are the keywords and short sentences for each major component and their sub-components:

Central Node

  • Data Analytical Architecture

Branches

1. Different Data Sources

  • Keywords: Diverse origins, raw data, databases, sensors, social media.
  • Short Sentences: Various origins of data. Raw data from multiple sources.

2. Department Warehouse

  • Keywords: Department-specific, storage, reporting, tailored data.
  • Short Sentences: Storage solution for department needs. Generates department-specific reports.

3. Enterprise Data Warehouse (EDW)

  • Keywords: Centralized repository, integrated data, holistic view.
  • Short Sentences: Consolidates data from various departments. Provides a unified data view.

4. Reports

  • Keywords: Insights, historical data, actionable information.
  • Short Sentences: Generated from stored data. Provides actionable insights.

5. Dashboards

  • Keywords: Visual interface, real-time insights, KPIs, metrics.
  • Short Sentences: Displays key performance indicators. Provides real-time data visualization.

6. Alerts

  • Keywords: Notifications, critical events, thresholds.
  • Short Sentences: Notifies users of critical data events. Triggers based on defined thresholds.

7. Data Science Users

  • Keywords: Analysts, data scientists, advanced analytics, modeling.
  • Short Sentences: Utilizes data for in-depth analysis. Conducts predictive and prescriptive analytics.

Sub-Branches (for each major branch)

Different Data Sources

  • Databases: Transactional systems, data storage.
  • Sensors: IoT devices, environmental data.
  • Social Media: External data sources, public datasets.

Department Warehouse

  • Storage: Department-specific data storage.
  • Reporting: Tailored reports for departmental use.

Enterprise Data Warehouse (EDW)

  • Central Repository: Integrates multiple data sources.
  • Holistic View: Comprehensive organizational data view.

Reports

  • Historical Data: Summarizes past performance.
  • Actionable Insights: Supports business decisions.

Dashboards

  • KPIs: Key metrics tracking.
  • Real-Time Insights: Up-to-date data visualization.

Alerts

  • Critical Events: Immediate notifications.
  • Thresholds: Defined event triggers.

Data Science Users

  • Advanced Analytics: In-depth data examination.
  • Modeling: Predictive and prescriptive models.

Flow of Data (Connect branches to show flow)

  1. Data Collection:

    • From Different Data Sources to Department Warehouse and EDW.

  2. Storage and Management:

    • Department Warehouse stores specific data.
    • EDW integrates department data.

  3. Data Utilization:

    • Reports generated from Department Warehouse and EDW.
    • Dashboards and Alerts pull from EDW.

  4. End-User Interaction:

    • Data Science Users access EDW for analysis.
    • Business users view Dashboards and Alerts.

Using these keywords and short sentences, you can create a comprehensive mind map to recall the essential aspects and structure of Data Analytical Architecture effectively.