Module 2 · Proof: build useful evidenceMon, Sept 1460 min

Data, context, and access

Trace the data and context required by the workflow. Name the owners, access paths, quality issues, and decisions that remain unresolved.

Faculty: Dr. Saara Hyvönen · Eike von Seggern

Learning objectives

Data quality dimensionsData silos & availabilityWarehouse vs lake vs pipelines vs APIsData governance

🧠 Data readiness🧠 Silos kill AI🧠 Garbage in, garbage out

1. Read
Read the session guide above. Note the ideas that change how you see your organization's current AI question.
2. Test
Take the module quiz in Learn & Memorize to check understanding.
3. Drill
Run the spaced-repetition flashcards to lock in the terms from this module.

Question 1 of 4Data, context, and access

Supervised vs unsupervised learning
Supervised = model learns from labeled examples (X→Y). Unsupervised = model finds patterns in unlabeled data (clustering, segmentation).
LLM
Large Language Model: a deep network trained on vast text to predict next tokens; the engine behind generative AI. Limits: hallucination, context window, no true reasoning.
Agentic AI
Systems that use a model plus tools, memory, and loops to pursue a goal with limited supervision: vs a single model call. Example: Anthropic's agent patterns.
Data silos
Data trapped in separate systems/teams. The #1 blocker to AI: if data isn't accessible and quality, no model can save the use case.
RAG
Retrieval-Augmented Generation: feeding an LLM relevant retrieved context at query time to ground answers in your own data and reduce hallucination.
Build vs buy vs partner
Build = custom IP/control; Buy = vendor SaaS (faster, but vendor lock-in & data risk); Partner = co-build with consultants. Decision depends on data reality and core competency.

Build for Module 2: Use Case Map + Technical Brief + Prototype Demo