Standard "Analytics" lists are full of marketing agencies and generic IT consultants. Our AI agents verify Snowflake usage, dbt implementation, and Data Engineering roles to find verified Enterprise Data Teams, Analytics Consultancies, and Modern Data Stack users.
Why generic keyword filters fail for data sales.
Targeting "Data Analytics" often returns marketing agencies running Google Analytics. If you sell warehouse compute, ETL tools, or reverse ETL, you need the engineers.
You need to filter out the dashboard viewers and find the Pipeline Builders with cloud infrastructure budgets.
| Metric | Standard "Data" List | Our ICP Database |
|---|---|---|
| Classification | Keyword Match | MDS vs. Legacy vs. Agency |
| Tech Stack | None | Snowflake, dbt, Looker, Airflow |
| Buying Center | CMO / Analyst | VP of Data / Data Engineering |
| Scale | Unknown | Team Size & Role Ratios |
Target data teams by their stack maturity.
Using Snowflake/dbt/Fivetran. High maturity. Targets for observability, catalog, and reverse ETL tools.
Heavy Tableau/PowerBI users. Targets for semantic layer tech and governance software.
Implementation partners. Targets for partnership programs and white-label tools.
Informatica/Matillion competitors. Targets for cloud infrastructure and connector maintenance.
Databricks/Spark users. Handling petabytes. Targets for compute optimization and storage tiers.
Selling datasets to hedge funds. Targets for web scraping tech and data clean rooms.
Monte Carlo/Datafold competitors. Targets for integration partnerships and enterprise sales.
Collibra/BigID users. Targets for compliance automation and lineage tools.
Building custom ML models. Targets for MLOps platforms and feature stores.
Redshift/BigQuery architects. Targets for cost management and security overlays.
GIS and location intelligence. Targets for satellite imagery and mapping APIs.
Shopper behavior analysis. Targets for POS data integration and visual AI.
Segment/Tealium implementers. Targets for identity resolution and activation tools.
Neo4j/TigerGraph teams. Targets for fraud detection and network analysis tools.
Manufacturing sensor data. Targets for time-series DBs and edge compute.
Bloomberg/FactSet ecosystem. Targets for low-latency feeds and terminal apps.
Genomics and pharma R&D. Targets for massive storage and specialized compute.
DMPs and bidstream analysis. Targets for privacy sandboxes and cookie-less tech.
Survey and panel data. Targets for automation and visualization reporting.
Training future analysts. Targets for student licenses and curriculum partnerships.
In Data & Analytics, the "Infrastructure Stack" defines the maturity. A team running dbt on top of Snowflake with Airflow orchestration is a sophisticated engineering organization.
We extract these "Pipeline Signals" to help you find the builders.
If you sell a data catalog, filter for companies with >10 Data Engineers but no visible Governance tool.
Pitch: "Stop the slack DMs asking 'what does this column mean?' Automate your data dictionary..."
We distinguish between companies that have data and companies that use data.
This ensures you pitch to modern data teams, not legacy IT departments.
Get the data that powers the world's most advanced data organizations.
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