Company Context

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.

Problem

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.

Deliverables

1. Define the key questions you would ask to understand the prospect’s current data stack and business context.
2. Segment those questions into logical buckets such as users, workloads, architecture, pain points, governance, and success criteria.
3. Explain how the answers would help you identify the best Databricks entry point.
4. Prioritize which questions are must-have in a 45-minute call versus nice-to-have for follow-up.
5. Describe the trade-offs between broad discovery and deep technical qualification.

Constraints

• You have one 45-minute call with limited pre-call information.
• The prospect may include both business and technical stakeholders.
• You cannot assume they want a full platform replacement.
• The output should help the account team decide whether to pursue a platform sale, a point solution, or disqualify the opportunity.