AI Analysts vs Dashboards: What Role Does Each Play? скачать в хорошем качестве

AI Analysts vs Dashboards: What Role Does Each Play?

As AI transforms analytics, a critical question emerges: what role should AI analysts play versus traditional dashboards? This video from Atlan's Great Data Debate explores the fundamental difference: dashboards serve recurring metrics, while AI analysts handle ad-hoc questions with consistent definitions across multiple users. The key insight: next-generation analytics isn't about replacing dashboards, but understanding when each tool fits. AI analysts excel at delivering the same consistent answer to different stakeholders asking the same question in different ways, with outputs that can flow into emails and multiple interaction points. But this creates a new governance challenge: who's responsible for the answers when agents deliver insights instead of humans? This discussion features data leaders solving real-world challenges at the intersection of AI, governance, and analytics delivery. Key Takeaways Dashboards solve for recurring metrics; AI analysts solve for ad-hoc questions with consistent definitions. The use cases are complementary, not competitive. Dashboards provide static monitoring while AI analysts handle dynamic exploration with governed semantics. Next-generation analytics delivers consistent answers across multiple interaction modes. When multiple stakeholders ask the same question, AI systems should return identical answers but allow different consumption paths (email, visualization, API), this requires semantic consistency at the foundation. The accountability question becomes critical when agents deliver insights. As organizations shift from """"people delivering insights"""" to """"people building agents that deliver insights,"""" governance must clarify: who owns the answer quality, accuracy, and business outcomes? Consistent data definitions become non-negotiable infrastructure for AI analytics. Unlike human analysts who can caveat and contextualize, AI agents require explicit semantic layers so multiple users get reliable, comparable answers. The future is hybrid: dashboards for monitoring, AI analysts for exploration. Mature analytics orgs will use dashboards for KPIs and trend tracking, while deploying AI analysts for investigation, root cause analysis, and questions that don't fit pre-built templates."