BI Introduction

What is Business Intelligence?

Business Intelligence (BI) is a broad term that refers to the set of processes and technologies used to collect, manage, analyze, and leverage organizational data in order to support better strategic and/or operational decision-making.

Contrary to popular belief, this discipline goes far beyond data extraction, transformation, modeling, and visualization. The frequent association with these aspects stems largely from the names of leading tools on the market — Power BI, Tableau, Looker, Qlik Sense — which are primarily designed to process and present information through visualization and storytelling.

In reality, business intelligence combines business analytics, data mining, data modeling, and visualization, along with the tools and infrastructure that ensure reliable governance and enable truly data-driven decisions.

The Objectives of Business Intelligence

The goal of BI is to transform raw data into meaningful insights that guide both strategic and operational decision-making.

To achieve this, BI follows a series of steps that draw on various components of data science. The misconception of reducing BI to extraction, transformation, modeling, and visualization processes often comes from the fact that many organizations mainly rely on dashboard-oriented tools, suited for operational analysis.

While BI indeed incorporates the principles of ETL (extract, transform, load) and is mostly descriptive, it also involves a strong data analysis component — using data mining (i.e., machine learning and statistical methods to explore large datasets) and data modeling to structure information for effective visualization and interpretation.

The Business Intelligence Process

Business Intelligence generally follows a multi-step process:

1. Data sources: Identify the data to be analyzed, coming from heterogeneous sources.
2. Data collection and preparation: Gather and clean the data; this step relies on ETL tools to produce a consistent, reliable, and analysis-ready dataset.
3. Data analysis: Search for trends, anomalies, and insights. This may be descriptive (univariate statistics, time series) or use statistical/algorithmic methods to reveal deeper patterns and relationships.
4. Visualization: Present results through charts, tables, or interactive dashboards. Tools such as Power BI, Tableau, Looker, or Qlik Sense facilitate exploration and interpretation.
5. Actionable insights: Findings must guide decisions and lead to concrete changes that improve business performance.

Difference Between Business Intelligence and Artificial Intelligence

Although BI and AI share certain techniques (statistics, data mining, machine learning), they differ in their purpose:

• BI helps organizations understand and manage their activities by turning raw data into actionable insights, within a governance and business analytics framework.
• AI pursues a broader ambition: imitating and extending human capabilities (learning, perception, reasoning, adaptation), often with a focus on automation and prediction.

While BI is centered on organizational decision-making and the exploitation of enterprise data, AI represents a wider scientific and technological field, with applications that extend well beyond decision-making (robotics, natural language processing, computer vision, autonomous agents, etc.).

BI & AI : Agentic Workflows

The boundary between BI and AI blurs within agentic workflows:

• On the BI side: structured pipelines (ETL, dashboards, reporting) ensure governed and traceable data.
• On the AI side: models can reason on these datasets, detect patterns, and trigger automated actions based on learned rules or contexts.
• Together: BI + AI enable autonomous agents capable of continuously monitoring performance indicators, detecting anomalies and opportunities, generating insights automatically, and even suggesting — or in controlled scenarios, executing — corrective actions (price adjustments, resource reallocation, automated alerts).

These hybrid workflows, where BI provides data structure and quality and AI contributes adaptive and proactive intelligence, pave the way for automated and reliable data exploitation.

They represent an orchestration where multiple agents (BI, AI, business rules, external APIs) cooperate to steer and optimize operations without constant human intervention, while preserving governance and accountability.