Yönetime hikâyeyi anlat

Yarının Board and Beyond liderlik toplantısı: CEO ile 60 saniye. Sıkı bir yazılı hikâyeye ihtiyacın var: bir öneri, bunu destekleyen bir sayı ve dürüstçe işaretlenmiş bir risk.

En sert eleştiriyi, zaten inanmak istediğin bulgulara yönelt. Yalakalığın saklandığı yer orasıdır. Öneri verilerle desteklenmeli, ama dürüst versiyon riski de adlandırır. Tek paragraflık hikâyeyi yaz.

Hangi unsur, hikâyeyi yalakalıktan çıkarıp dürüst ve karar vermeye hazır hale getirir?

⭐ Bonus: Kurs kapanışının ardından Snowflake Cortex ile AI destekli veri çalışması üzerine bonus derse kal. Ayrıca kendin denemeye başlamak için ücretsiz Snowflake denemesi talep edebilirsin.

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Veri Analistleri için AI

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Veri Analistleri için AI

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Kursa Ücretsiz Başla

Set up your AI-augmented analyst toolkit. Learn where AI fits across the five-stage analysis cycle, master the GCSE prompting framework for turning vague asks into actionable recommendations, and choose the right way to connect AI to your data: flat files, MCP, or a governed semantic layer.

Exercise 1: The AI-augmented analyst Exercise 2: Get to know The Daily Grind Exercise 3: Same prompt, different answer Exercise 4: Writing prompts that work Exercise 5: Pick a prompt apart with GCSE Exercise 6: Vague vs. structured prompt Exercise 7: Connecting to your data Exercise 8: Flat file, MCP, or semantic layer?Exercise 9: Context unlocks meaning

Move from raw data to insights you can defend. Interrogate data quality for fuzzy duplicates, missing values, and impossible timestamps; enrich raw fields with AI as both doer and advisor; then find insights worth chasing across trends, distributions, differences, and outliers, and verify each one before it reaches a stakeholder.

Exercise 1: The interrogation phase: data quality Exercise 2: What can AI flag on its own?Exercise 3: Interrogate the coffee shop data Exercise 4: Interrogate the AI's deduplication Exercise 5: Enriching your data Exercise 6: Extract intents from support tickets Exercise 7: Doer or advisor?Exercise 8: Finding insights you can trust Exercise 9: Where would you start exploring?Exercise 10: Drill into a finding

Turn findings into dashboards and stories that land. Compress dashboard discovery and prototyping from weeks to an afternoon, tailor data stories to the audience and decision in front of you, and protect against polished-but-wrong output with the S.P.O.T. verification framework.

Exercise 1: Building dashboards with AI Exercise 2: From interviews to user stories Exercise 3: Prototype and iterate Exercise 4: Telling data stories with AI Exercise 5: Same data, two stories Exercise 6: Honest framing vs. misleading Exercise 7: Story for the CFO Exercise 8: Verifiability and trust Exercise 9: Which S.P.O.T. check catches this?Exercise 10: Does this number feel right?Exercise 11: Spot the polished-but-wrong dashboard

Run a complete AI-first analysis on Board and Beyond, a US retail chain. Audit data quality, identify the enrichments a category manager would actually use, surface and verify a headline finding, build a dashboard that backs an expansion decision, and deliver a one-paragraph story to leadership.

Exercise 1: Bitirme Projesi: Board and Beyond Exercise 2: Board and Beyond'u Tanı Exercise 3: Hangi karmaşa bekleyemez?Exercise 4: Bir kategori yöneticisi gerçekte neyi kullanır?Exercise 5: Manşet niteliğindeki bir bulguyu doğrula Exercise 6: Nereye genişlemeliyiz?Exercise 7: Yönetime hikâyeyi anlat

Geçerli egzersiz

Exercise 8: Tebrikler!Exercise 9: Bonus: Snowflake Cortex ile Veri Analizi