Game Data for All: How AI Is Reinventing Game Analytics
summary
ClarityQ demonstrates an AI pipeline that builds game-specific context then answers data questions, connecting to BigQuery Snowflake Databricks and BI tools like Tableau Looker Power BI and Confluence, auto-generating the exact SQL it runs and exposing the chain of thought. The system uses a context builder agent and a conversational agent plus skills and memory to encode event glossaries and metric formulas, analyzed thousands of customer questions (one third on monetization, 27% exploratory), and can auto-create reports dashboards and even trigger AB tests.
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chapters (10)
- 0:00AI-driven game analytics intro
- 1:30Daily game analytics challenges
- 2:35GenAI without context limits
- 3:37Context layer for AI agents
- 4:59Game events and metrics
- 7:46Skills and memory in analytics
- 9:12Self-service AI analytics
- 10:18AI-powered analysis example
- 13:26Game teams' AI questions
- 14:52Future of proactive analytics
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