What is business intelligence?

Summary

• Business intelligence (BI) is the infrastructure, tools, and best practices organizations use to turn raw data into actionable insights for strategic decision-making and organizational growth.
• Traditional BI focuses on extracting, transforming, and analyzing historical data through dashboards, reports, and analytics, typically built by data professionals.
• Modern BI is transitioning from static, report-centric models to dynamic, conversation-driven capabilities that adapt to how business leaders actually think and ask questions.
• Databricks AI/BI brings a compound AI system to business intelligence, enabling self-service analytics for everyone by understanding your data's semantics, lineage, and usage patterns across the full Databricks platform.
• Two complementary experiences deliver AI-powered insights: AI/BI Dashboards for low-code visualization and Genie Agents for conversational, natural language questions about your data.

What is business intelligence?

Business intelligence is the set of infrastructure, tools, applications, and best practices that organizations use to turn raw data into actionable insights for strategic decision-making. The traditional BI lifecycle begins with extracting, transforming, and loading data via ETL processes, continues with data warehousing, and culminates in dashboards, reporting systems, and analytics that inform business decisions. A comprehensive BI implementation includes data governance, master data management, and strong access control to ensure data quality and security.

The evolution of BI in the AI era

Business intelligence is undergoing a fundamental transition. Historically, BI has been report-centric, built around pre-answered questions that data professionals design for business users. Today, BI is shifting toward dynamic, conversation-driven capabilities that adapt to how business leaders actually think. Predictive analytics, machine learning, and AI are becoming baseline expectations for modern BI systems. Organizations now expect instant, personalized insights from their data, not static reports updated on fixed schedules.
This shift creates new requirements: BI systems must understand organizational context, adapt to evolving business questions, and enable non-technical users to explore data independently. Traditional BI tools that bolt AI onto legacy architectures struggle with real-world data complexities like semantic understanding, data lineage, and governance at scale.

Databricks AI/BI: BI for the AI era

Databricks AI/BI is a new type of business intelligence product designed to provide deep understanding of your data's semantics, enabling self-service analytics for everyone in your organization. Built on a compound AI system, it draws insights from the full lifecycle of your data across the Databricks platform, including ETL pipelines, lineage, and historical queries.
Databricks AI/BI captures semantic understanding from interactions across the Databricks Data + AI Platform and integrates this context into real-time analytics. This architecture addresses the core limitation of traditional BI: the semantic gap between how data professionals understand the data and how business users need to explore it.

Key capabilities of modern BI

Capability	Why it matters
Self-service analytics	Non-technical users can explore data and answer their own questions without requesting reports from data teams.
Natural language interface	Asking questions in everyday language, not SQL or technical syntax, lowers the barrier to data exploration.
Semantic understanding	The BI system understands context: what data means, how it's related, how it's been used historically.
Governed data access	Users only see data they're authorized to access; governance travels with the data.
Real-time insights	Dashboards and queries reflect current data, not yesterday's extracts.
Visual explanation	Insights come with context: why a number matters, what drove it, what changed.

Databricks AI/BI capabilities

Databricks AI/BI powers two complementary product experiences:
AI/BI Dashboards is an AI-powered, low-code dashboarding solution that includes conventional BI capabilities for answering a fixed set of business questions. Users describe what they want in natural language, and AI automatically generates dashboards using more than 20 visualization types, cross-filtering, and periodic PDF snapshots delivered via email. Dashboards run natively on the Databricks platform and scale to large datasets without sampling or extract limits. Data teams configure which data is available, and dashboards respect Unity Catalog permissions.
Genie Agents are conversational workspaces where users ask questions about their data in natural language. Genie can answer a much broader set of ad hoc business questions through compound reasoning while also providing certified answers for query patterns specified by data teams. Genie responds with adaptable visualizations, seeks clarification when needed, and improves over time through human feedback.

Databricks approach to BI governance and governance

As a native solution within the Databricks Data + AI Platform, Databricks AI/BI provides:

• Unified governance and lineage: AI/BI adheres to Unity Catalog policies and lets data producers trace how their data assets are used within AI/BI back to ingestion.
• Governed data access: Genie respects Unity Catalog permissions, so users only see data they're authorized to access.
• No separate data extract: AI/BI works with your existing data, removing the need to extract datasets into a separate BI engine. This improves freshness and simplifies governance.
• Unified semantics: Data teams can define consistent, reusable metrics through Unity Catalog semantics that Genie and dashboards use as a single source of truth.
• Instant insights at scale: Built on the Databricks lakehouse, AI/BI provides instant performance without sampling, extract limits, or separate BI infrastructure.

Getting started with BI

1. Assess your current BI infrastructure: What data sources feed it? Who are your primary users? What decisions does it support?
2. Define your semantic model: What business metrics and data relationships matter most to your organization?
3. Start with exploratory analytics: Use Genie or AI/BI Dashboards to let business users ask questions and discover insights.
4. Progressively build governed semantic layers: Create trusted metrics and dimensions that teams can reuse.
5. Expand self-service: As governance and semantic understanding deepen, enable more users to explore independently.

FAQs

What's the difference between BI and analytics?

Business intelligence focuses on answering pre-defined business questions through dashboards and reports, while analytics is the broader practice of exploring data to discover new insights. Modern BI systems blur this line by enabling self-service exploration and discovery.

Do business users need SQL to use BI tools?

No. Modern BI tools like Databricks AI/BI and Genie Agents let users ask questions in natural language. SQL remains valuable for data professionals building pipelines and defining semantic layers, but end users no longer need it.

How does governance work in AI/BI?

Databricks AI/BI is integrated with Unity Catalog, so it respects data permissions and lineage and uses governed semantics. Users only see data they're authorized to access, and all queries remain governed within your workspace.