Лист за преговор: Digital Transformation and Ecosystems

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

Item	Key Features	Notes / Differences
Digital Ecosystem	Interconnected, interoperable resources	Foundation of digital services
Network Effect	More users = higher value, e.g., Waze	Positive feedback loop
Data vs. Information	Raw vs. processed, meaningful data	Data forms the basis of info
Storage Hierarchy	Database (OLTP), Data Warehouse (OLAP), Data Lake (big data)	Hierarchical data organization
Big Data Vs	Volume (size), Velocity (speed), Variety (types)	Different management approaches
Analytics	Descriptive, Diagnostic, Predictive, Prescriptive	Increasing complexity and value
AI Hierarchy	AI > ML > DL	Neural network-based advances
Generative AI	Creates new content based on prompts	Examples: ChatGPT, DALL-E
Context Data	Adds situational background to raw data	Enhances meaning and accuracy
Knowledge Types	Tacit (experience) vs. Explicit (documents)	Crucial for learning organizations

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.

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