Your question is Discover a Prospect’s Data Stack. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Databricks sells the Data Intelligence Platform to enterprise customers running analytics, data engineering, AI, and governance workloads across fragmented data environments. In many deals, the first product challenge is not pitching features — it is diagnosing the prospect’s current stack, pain points, and readiness for consolidation onto Databricks SQL, Delta Lake, Unity Catalog, and Mosaic AI.
You are working with a prospect that says, “Our data stack is complicated, and we’re evaluating modernization.” They use multiple tools across ingestion, storage, transformation, BI, governance, and machine learning, but have not clearly articulated where the biggest friction is. Your goal is to design a discovery approach that helps uncover the prospect’s real jobs-to-be-done, current architecture, buying criteria, and migration blockers.
Assume the account team has only one 45-minute discovery call before deciding whether to position Databricks as a lakehouse consolidation play, an AI platform opportunity, or a narrower workload-specific entry point. The risk is asking generic technical questions and missing the actual business driver.