Set expectation: we will rethink how we access data.
Optional: mention this is a "different perspective" lecture AKA Paradigm shift, hihi 😼
❓ How would you solve this?
❓ What steps would you take?
❓ Which step is hardest?
❓ Where do errors happen?
❓ Where can we get a completely wrong result without any error message 🙀
Tricky parts:
- joins (understanding relationships, correct keys, duplication/missing data)
- aggregation (what to group by, logical errors (different granularity levels, subtotals, ...), hard to debug/validate)
❗ dangerous part — these errors often don’t crash. They just give wrong results.
Mention:
- This is what BI systems do internally
❓ Where is most effort in this pipeline?
In practice, most effort is in ETL… but even after that, accessing the data correctly is still difficult
❓ Which role would you prefer?
❓ Do managers really understand the data?
Point: knowledge is distributed.
Stress tradeoff:
- reliability vs accessibility
Hook:
- AI usage to make it more accessible
Key transition.
Say:
- The problem is not storing data
- The problem is accessing and interpreting it
Core concept slide.
❓ Have you felt this mismatch when writing queries?
❓ What would YOU define as active user?
Then say:
- All valid → problem.
Somebody has to convert: fuzzy business meaning → explicit formal definition
That conversion is the semantic gap.
Key idea: meaning is not stored.
Ex: data o rozvrhu studentů: překrývající se akce mohou být napočítány do zátěže, ale ve skutečnosti mohou chodit jen na jedno z toho. To z dat ale není poznat bez znalosti situace.
Introduce AI calmly.
Frame as attempt to bridge gap.
Draw attention to new layer.
❓ What could go wrong here?
Clarify:
- AI does not know data
- it translates intent
⚠️ Problémy
špatné názvy tabulek/sloupců
špatná metrika („top“ podle čeho?)
halucinace
💬 You can even say:
“This works surprisingly often… and fails silently.”
📝 Actually highlighting diff possible interpretations of top in text
AI už „vidí strukturu“
ale stále nechápe význam
dříve uživatel pouze sestavoval dashboard z katalogu známých prvků
dnes je může i vytvářet dle potřeby bez čekání na implementaci a ověření
* rozhodování není viditelné
* nelze snadno ověřit postup
* debug logy nejsou běžně dostupné
❓ Vadí vám, že nevidíte, jak systém přemýšlí?
❓ Kdy by vám to vadilo? report pro management? osobní explorace?
❓ použili jste něco z toho?
❓ věřili jste výsledku?
What-if: output is set of queries with estimated probability or confidence of model
❓ Poslali byste firemní data do ChatGPT?
❓ A víte, jestli to už někdo ve firmě udělal?
❓ Who benefits from this?
Highlight accessibility.
❓ Would you trust this for business decisions?
❓ Which is worse: wrong or uncertain result?
Tie back to course value.
Let it sit.
❓ Would you deploy this in production?
❓ Jaké jsou core skills pro uživatele?