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?