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