Augmented Analytics: The Intelligence Layer GenAI Assistants Need

What Is Augmented Analytics?

Augmented analytics isn’t a feature, it’s an intelligence system. It brings together the essential layers needed to turn raw data into insight that’s not only accurate, but explainable, timely, and ready to use. At its core, augmented analytics integrates three essential components: 

• Trusted, curated datasets that are clean and governed
• Business context, derived from existing processes, analytics workflows, and machine learning models
• An orchestration layer that enables GenAI applications and AI agents to coordinate complex analyses — leveraging both the trusted data and embedded business logic

These parts work together to do what traditional dashboards and basic assistants can’t.

• Without trusted, governed data, insights are unreliable.
• Without business context, assistants can’t explain why something happened.
• Without orchestration, GenAI stays disconnected from analysis, context, and decision-making.

When integrated, these layers form the intelligence behind GenAI — delivering context-rich, traceable, and actionable insights where decisions happen.

Where AI Assistants Stop — and Augmented Analytics Begins

Assistants are great for surfacing data. But when deeper analysis is required, they fall short. Without augmented analytics, they can’t:

• Uncover the true cause behind a change
• Explain results with business context
• Perform multi-step root cause analysis
• Provide traceable, governed insights
• Power the decisions AI agents are expected to make

That’s where this intelligence layer makes the difference. Ask, “Why did Q3 revenue drop?”

• Without it: Here’s a dashboard.
• With it: “Revenue declined 12% in Q3 due to increased churn in the Northeast, tied to fewer promotions and competitor discounting. See chart.”

Imagine an AI agent that detects that same revenue dip — and instantly surfaces root causes, explains business drivers, and triggers pricing adjustments. That’s not just automation. That’s augmented analytics in motion.

This is the gap augmented analytics fills — turning access into understanding, and understanding into action.

Why Dataiku Is a GenAI-Critical Infrastructure for Augmented Analytics

AI assistants make data easier to reach. Dataiku turns access into action — with logic, transparency, and control. As The Universal AI Platform™, Dataiku powers the full intelligence layer behind GenAI — helping organizations go beyond interaction and build scalable systems of insight. With deep integration across your trusted datasets, analytics workflows, ML models, and GenAI applications, Dataiku powers AI — and AI agents — that truly understand your unique business drivers and context. 

With Dataiku, you get more than answers:

• Assistants that guide, not just generate: Analysts speed up SQL, data prep, and flow design — all grounded in business context.
• Explainable AI: AutoML with built-in transparency surfaces key drivers and strengthens decision-making.
• Collaborative workflows: Visual and code-first design connects technical experts and business users on governed data.
• RAG-powered GenAI with guardrails: Ground outputs in your own documents, logic, and domain expertise — no hallucinations.
• Insight in motion: Use Dataiku Stories to turn findings into dynamic, trustworthy presentations in seconds.
  
• Enterprise governance from the start: End-to-end lineage, traceability, and compliance, from notebook to production.

Organizations using Dataiku have transformed the way data teams deliver value — cutting manual tasks, accelerating time to insight, and creating reusable systems that scale from assistant to AI agent.

Whether you're piloting your first GenAI assistant or scaling AI agents across your business, Dataiku is the foundation that enables it — and the intelligence layer that ensures it works.

Why Augmented Analytics Matters in the Age of AI Agents

Enterprise AI is evolving — from interaction to autonomy. AI agents are emerging to do more than answer questions. They monitor KPIs, flag anomalies, trigger workflows, and make decisions. But for agents to act responsibly, they need:

• Access to trusted data and ML models, so their decisions are grounded in reliable, governed inputs.
• Awareness of existing data and analytics context, so they can interpret results accurately and act in line with business logic.
• The ability to collaborate with other agents and GenAI applications, enabling them to operate as part of a coordinated, intelligent system.

That’s the role of augmented analytics. It’s not a bolt-on feature — it’s the intelligence infrastructure behind responsible agents. It provides verified insight, clear reasoning, full traceability, and built-in safeguards.

With Dataiku, you don’t just deploy AI agents — you equip them to act with intelligence, transparency, and enterprise-grade confidence.