rosscrooke Competitive Intelligence
Use this when the coordinator asks you to identify competitors and recommend positioning.
Pattern matching: who's in the deal?
| RFP signal | Likely competitor |
|---|---|
| Lots of mentions of "Lakehouse architecture", "MLflow integration", "Delta tables" | Databricks |
| Heavy SQL emphasis, "data marketplace", "secure data sharing", existing Snowflake user mentioned | Snowflake |
| Customer is heavy Microsoft shop, Azure-only, mentions of Power BI integration | Microsoft Fabric |
| Customer is GCP-native, mentions BigQuery ML, Looker | Google BigQuery |
| RFP asks about open-source compatibility / no vendor lock-in | Possibly Databricks, possibly an open-source rival like Trino+Iceberg |
If two or more of these signals appear, both competitors are likely shortlisted.
Battlecards
vs. Databricks
Their strengths:
- Strong ML/AI story (MLflow, Mosaic)
- Lakehouse / Delta is genuinely good for very large-scale workloads
- Open file format reduces lock-in concern
- Brand momentum among data engineering teams
Their weaknesses:
- Total cost of ownership often surprises customers (compute spend ramps fast)
- Less mature on BI / analyst-friendly tooling
- Spark-based query latency for interactive analytics can be poor
Our angles:
- Lead with TCO: produce a 3-year cost projection. We win on predictable spend.
- Position on time-to-insight for analyst personas, not just engineers.
- Don't fight on ML breadth. Concede that and pivot.
Trap to avoid:
- Don't try to out-engineer them on Spark or Iceberg. You'll lose on technical ground.
vs. Snowflake
Their strengths:
- Best-in-class analyst experience
- Mature data sharing
- "Just works" reputation
Their weaknesses:
- Expensive at scale (the standard procurement complaint)
- Less flexible for unstructured / semi-structured / real-time
- ML/AI story is bolted-on, not native
Our angles:
- Lead with workload coverage: real-time, semi-structured, unstructured.
- Highlight ML-native architecture.
- Run a TCO comparison at customer's projected scale — usually wins on year 2+.
Trap to avoid:
- Don't try to out-analyst-tool Snowflake on day 1. They've been polishing that experience for a decade.
vs. Microsoft Fabric
Their strengths:
- E5 license inclusion makes the headline price look free
- Tight Power BI integration
- Already deployed in the customer's tenant
Their weaknesses:
- Maturity gaps in core capabilities (still catching up on basic features)
- Lock-in to Azure-only
- Performance consistency varies
Our angles:
- Honest TCO including Microsoft consulting hours
- Multi-cloud story (don't lock yourself in)
- Maturity: we've been doing this for 8 years; they've been doing it for 18 months.
Trap to avoid:
- Don't compete on Power BI integration. We integrate, they own.
- Don't dismiss the "free with E5" claim. Acknowledge it directly and reframe to TCO.
vs. Google BigQuery
Their strengths:
- Truly serverless analytics — no cluster management
- Strong on standard SQL workloads
- Vertex AI integration is genuinely useful
Their weaknesses:
- GCP-only (deal-breaker for multi-cloud customers)
- Less mature governance / data-mesh story
- Streaming ingest costs add up
Our angles:
- Multi-cloud flexibility
- Governance and data-mesh maturity
- Workload portability
Trap to avoid:
- Don't claim better serverless than BigQuery. We're not.
How to format your output
For each likely competitor:
- Why they're likely in this deal (cite the RFP signals)
- Their strengths AGAINST OUR ANGLES (not generic strengths)
- Our two best positioning angles for THIS RFP specifically
- One trap
Then a one-line summary: "Most likely shortlist: X, Y, Z. Our best opening move: [specific recommendation]."