Paweł Domański
4.4 KiB
Super — teraz jest jasne 👍
Poniżej masz wierne przekształcenie obu grafik w tekst, bez interpretacji i bez dodawania treści, tylko czysta transkrypcja + uporządkowanie.
📊 15 ESSENTIAL DATA CONCEPTS THAT AI DEPENDS ON
1. Ontology
A shared definition of the core business concepts and how they relate.
Gives AI clear concepts and relationships to reason about.
2. Entity
A real-world object or concept that data represents.
Helps AI distinguish people, products, events, etc.
3. Metadata
Data that explains other data.
Helps AI understand meaning, freshness and trust.
4. Physical layer
Where and how data is actually stored and processed.
Shapes the performance and scalability of AI workloads.
5. Semantic layer
A business-friendly layer that defines consistent metrics.
Ensures use of consistent business definitions by analysts, decision makers & AI.
6. Logical layer
How data is organised conceptually, independent of physical storage.
Shields AI from raw technical complexity.
7. Data virtualisation
Accessing data from multiple sources without copying it into one place.
Lets AI access data across systems seamlessly.
8. Schema
The formal structure that defines what data exists and what type it is.
Provides consistent structure for any use case.
9. Data modelling
Designing how entities and their relationships are represented in data.
Reduces ambiguity in how AI interprets data.
10. Vector database
A database designed to search by similarity rather than exact matches.
Enables richer retrieval and contextual understanding.
11. Data pipeline
The flow of data from creation to consumption.
Supplies AI with timely, relevant data.
12. Orchestration
The coordination of when and how data pipelines run.
Keeps inputs and jobs reliable and well-sequenced.
13. Data quality
How accurate, complete, timely and consistent data is.
Strengthens confidence in AI-driven insights and decisions.
14. Observability
The ability to see what data systems are doing and detect issues early.
Supports early detection of drift and unexpected behaviour.
15. Data lineage
The trace of where data comes from, how it changed and where it is used.
Provides transparency and explainability for AI outputs.
🤖 3 ROLES OF AI EVERY LEADER SHOULD UNDERSTAND
1) TRADITIONAL AI — The Analyst
What it does
- Studies data & predicts
- Finds patterns, classifies
- Supports better decision-making
Where it shines
- Fraud checks
- Demand forecasts
- Quality control
What it needs
- Clean, labelled data
- Regular monitoring
Maturity
- HIGH — predictable patterns
Risks
- Outdated data
- Biased inputs
Best for
- Clear predictions for decisions
How to onboard
- Start with ONE prediction problem
2) GENERATIVE AI — The Creator
What it does
- Produces new content, ideas, code
- Creates text, images, ideas
- Summarises & drafts
Where it shines
- Marketing copy
- Smart search
- Code help
What it needs
- Strong base model
- Internal data for grounding
- Clear rules for tone & quality
Maturity
- MEDIUM — fast-improving but governance still maturing
Risks
- Can invent facts
- Can leak sensitive info
Best for
- Fast content, ideas, summaries
How to onboard
- Use simple chatbot / writing assistant
3) AGENTIC AI — The Worker
What it does
- Takes action across systems
- Executes multi-step workflows
- Adapts in real time & completes end-to-end
Where it shines
- Support ticket resolution
- Claims processing
- Sales follow-ups
What it needs
- Secure access
- Tool permissions
- Short-term memory
- Audit logs
Maturity
- EARLY — powerful but needs strong controls & guardrails
Risks
- Errors can cascade
- Needs strict guardrails & approvals
Best for
- Automated tasks with minimal effort
How to onboard
- Start with ONE narrow, high‑value workflow
Jeśli chcesz, mogę teraz:
- ✅ przerobić to na artykuł LinkedIn / blog
- ✅ zamienić na materiał strategiczny dla managerów
- ✅ połączyć oba obrazki w jedną spójną koncepcję (AI + data foundations)
- ✅ przepisać w org‑mode (wiem, że to Twoje 😉
Daj znać, w którą stronę idziemy.