Digital Transformation & Data Ecosystems Revision Sheet

1. 📌 Essentials

• Digital ecosystem: network of interconnected IT resources promoting interoperability.
• Network effect: increased users enhance service value.
• Data raw facts; Information: processed, meaningful data.
• Storage hierarchy: Database (OLTP), Data Warehouse (OLAP), Data Lake.
• Big Data Vs: Volume, Velocity, Variety, (additional Vs: Veracity, Value).
• Analytics:criptive, Diagnostic, Predictive, Prescriptive.
• AI hierarchy: AI > ML > DL.
• Generative AI: produces new content based on prompts.
• Context data: situational background enhancing understanding.
• Tacit knowledge: experience-based, hard to articulate.
• Explicit knowledge: documented and shareable info.
• Sustainability issues: energy efficiency, e-waste, ethics, inclusion.

2. 🧩 Key Structures & Components

• Digital Ecosystem — interconnected systems sharing data and services.
• Data Management — involves databases, data warehouses, lakes.
• Data Mining & Business Intelligence — pattern detection and decision support.
• Big Data Vs — compare key characteristics.
• Analytics Types — stages of extracting insights.
• AI Hierarchy — levels from general AI to deep learning.
• Generative AI — creates text, images, videos.
• Context Data — situational info (location, time).
• Knowledge Types — tacit (experiential), explicit (documented).
• Sustainability — environmental, ethical, social considerations.

3. 🔬 Functions, Mechanisms & Relationships

• Digital ecosystems rely on interoperability for seamless resource sharing.
• Increasing network users amplify service value (network effect).
• Raw data is processed into information to support decisions.
• Hierarchical storage enables efficient data handling:
  • OLTP to manage transactions (Databases).
  • OLAP for multidimensional analysis (Data Warehouses).
  • Big Data Lakes for raw, unstructured data.
• Data mining detects patterns, BI informs strategic decisions.
• Response to data volume growth: shift from traditional DBs to Data Lakes.
• Progression of analytics:
  • Descriptive explains "what happened."
  • Diagnostic analyzes "why."
  • Predictive anticipates "what will happen."
  • Prescriptive recommends "actions."
• AI progresses from rule-based systems (AI) to learning algorithms (ML) to neural networks (DL).
• Generative AI creates new content, e.g., chatbots, images.
• Context data contextualizes raw data simplifying interpretation.
• Tacit knowledge resides in individuals’ experience; explicit knowledge is stored in documents.
• Challenges in sustainability focus on energy, waste, and ethical practices.

4. 📊 Comparative Table

5. 🗂️ Hierarchical Diagram

Digital Ecosystem
 ├─ Interoperability
 └─ Network Effect
Data Management
 ├─ Data
 │    ├─ Raw Facts
 │    └─ Processed Info
 ├─ Storage Hierarchy
 │    ├─ Database (OLTP)
 │    ├─ Data Warehouse (OLAP)
 │    └─ Data Lake
 ├─ Data Mining & BI
 │    ├─ Pattern Detection
 │    └─ Decision Support
Big Data & AI
 ├─ Vs of Big Data
 │    ├─ Volume
 │    ├─ Velocity
 │    └─ Variety
 └─ Analytics Levels
      ├─ Descriptive
      ├─ Diagnostic
      ├─ Predictive
      └─ Prescriptive
AI Hierarchy
 ├─ AI
 ├─ Machine Learning
 └─ Deep Learning
Context & Knowledge
 ├─ Context Data
 └─ Tacit & Explicit Knowledge
Sustainability
 ├─ Energy
 ├─ E-waste
 ├─ Ethics
 └─ Inclusion

6. ⚠️ High-Yield Pitfalls & Confusions

• Confusing data (raw) with information (processed).
• Overlooking the differences between Data Lake and Data Warehouse.
• Mistaking AI for ML; ML as a subset of AI.
• Assuming more data quality guarantees better insights without considering veracity.
• Ignoring the importance of context in data interpretation.
• Confusing descriptive analytics with prescriptive.
• Underestimating the sustainability challenges of Big Data infrastructures.
• Overlooking the role of tacit versus explicit knowledge in organizational learning.

7. ✅ Final Exam Checklist

• Understand the concept and components of a digital ecosystem.
• Explain the network effect with real-world examples.
• Differentiate between data and information.
• Know the storage hierarchy: database, data warehouse, data lake.
• Recognize the 3 Vs (or 5 Vs) of Big Data.
• Describe the four levels of analytics and their roles.
• Define AI, ML, DL; understand their hierarchy.
• Identify what generative AI does and examples.
• Explain the importance of context data.
• Distinguish between tacit and explicit knowledge.
• Discuss sustainability issues related to digital technologies.
• Recognize key features of digital transformation types.
• Comprehend the role of data mining and BI in decision-making.
• Identify common pitfalls in data management.
• Be familiar with the ASCII hierarchy of data systems.
• Know how network effects influence platform growth.
• Recall key challenges to achieving sustainable digital ecosystems.