Repérer le tableau de bord soigné… mais erroné

Vous passez en revue quatre tableaux de bord « finalisés » qu'un assistant IA a générés pour différentes équipes chez HarborTech. Ils sont tous soignés : typographie nette, couleurs pertinentes, respect de la charte. Mais trois sont corrects, et un présente un défaut dissimulé sous le vernis. Des graphiques impeccables peuvent reposer sur des données complètement fausses.

Lequel de ces tableaux de bord contient le défaut caché ?

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<cours>L'IA pour les data analysts</cours>

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Cet exercice fait partie du cours

<cours>L'IA pour les data analysts</cours>

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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: Créer des tableaux de bord avec l'IA Exercise 2: Des entretiens aux user stories Exercise 3: Prototyper et itérer Exercise 4: Raconter des histoires avec les données grâce à l'IA Exercise 5: Même données, deux récits Exercise 6: Cadrage honnête vs. trompeur Exercise 7: Histoire pour la Directrice Financière (CFO)Exercise 8: Vérifiabilité et confiance Exercise 9: Quel contrôle S.P.O.T. l'attrape ?Exercise 10: Ce chiffre vous paraît-il crédible ?Exercise 11: Repérer le tableau de bord soigné… mais erroné

Exercice actuel

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: Capstone Project: Board and Beyond Exercise 2: Get to know Board and Beyond Exercise 3: Which mess can't wait?Exercise 4: What would a category manager actually use?Exercise 5: Verify a headline finding Exercise 6: Where should we expand?Exercise 7: Tell the story to leadership Exercise 8: Congratulations!Exercise 9: Bonus: Data Analysis with Snowflake Cortex