What is Business Intelligence (BI)?
Business Intelligence (BI) is the set of tools, visualization methods, and analytical practices that turn raw corporate data into understandable, actionable insights. If the Data Lakehouse is the engine room of a ship generating power, the Business Intelligence platform is the instrument panel and radar screen leadership uses to steer the company.
Before BI tools became widespread, many business decisions rested on intuition, experience, or monthly spreadsheets that were already out of date. Business Intelligence changed this. By connecting visual platforms (like Tableau, Power BI, or Apache Superset) to fast analytical databases, BI lets decisions, from supply chain logistics to marketing spend, be grounded in current, verifiable data.
The Architecture of the BI Stack
A modern enterprise Business Intelligence deployment relies on several integrated layers.
1. The Data Source (The Semantic Layer)
BI tools do not inherently know what “Gross Margin” means. They generate and run SQL. That is why modern BI architectures often rely on a Semantic Layer (for example, dbt’s semantic layer or Dremio). The Semantic Layer holds the business logic. When the BI tool asks for “Gross Margin,” the Semantic Layer applies the predefined joins and calculations against the Apache Iceberg tables in the Lakehouse and returns the aggregated result to the BI tool.
2. The Analytical Engine (OLAP)
BI users expect fast responses. If an executive clicks a filter and the dashboard takes three minutes to load, they are likely to stop using it. To keep dashboards responsive, BI platforms query OLAP (Online Analytical Processing) engines. These columnar engines (like Snowflake, Dremio, or Trino) use caching, data skipping, and vectorized execution to aggregate large tables quickly, keeping dashboards interactive.
3. The Visualization Layer (The Dashboard)
This is the interface the executive sees. The BI tool turns aggregated numbers into charts such as scatter plots, heat maps, and geographic maps. Modern BI platforms let users “slice and dice” data by dragging and dropping filters (for example, “Europe” or “Q3”), and the tool generates the underlying SQL without the user writing code.
The Evolution: Augmented Analytics
BI is now changing through the addition of AI, often called Augmented Analytics.
Traditionally, if an executive saw a sharp dip in a line chart, they asked a data analyst to investigate why with custom SQL. Many modern BI tools now integrate machine learning and LLM-based assistants. An executive can select the dip, click “Explain,” and the assistant runs a root-cause analysis over the underlying data, returning a plain-language explanation (for example, that the dip traces to a supply chain disruption in one country) that an analyst can then verify.
Summary of Technical Value
Business Intelligence is the translation layer of the enterprise data stack. By pairing interactive visual tools with fast analytical databases, BI broadens access to data. It lets non-technical business leaders explore large volumes of corporate data, so strategy can be guided by current, verifiable insights.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.