Topics
Agentic Analytics in Supply Chain
Time Series Analysis
Agentic Flows
Market Access Analytics
Analytics Stack
Search-Driven Analytics
Analytics Copilot
Knowledge Layer
Semantic Search in Analytics
AI Orchestration
Agent Composer
Insight Graph
Guided Insights
Agentic Analytics
Business Metrics
AI Agents
Prescriptive Analytics
Predictive Analytics
Natural Language Generation (NLG)
Insight Narratives
Natural Language Query (NLQ)
Workflow Automation
Data Integration
Business Logic
Data Preparation
Semantic Layer
Federated Data Governance
Data Products
Data Fabric
API Integrations
Data Democratization
White-Label BI
Ad Hoc Analysis
AI Analytics
Business Intelligence (BI)
Conversational Analytics
Zero Data Movement
Data Lakehouse
Cloud Data Warehouse
Live Query / Federated Query
In-Memory Query Engine
ETL / ELT (Extract, Transform, Load)
Data Transformation
Data Pipeline
Data Modeling
Data Mesh
Embedded Analytics
Search-Driven Analytics
Decision Intelligence
Data Storytelling
Dashboard
KPI (Key Performance Indicator)
Exploratory Data Analysis (EDA)
Self-Service Analytics

Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is the process of visually and statistically examining datasets to summarize their key characteristics. It involves identifying patterns, detecting outliers, uncovering trends, and checking assumptions—often using visualizations and descriptive statistics. In platforms like Tellius, EDA is powered by AI, enabling faster discovery of relationships and anomalies across complex datasets.

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EDA is the bedrock of effective analytics. Without it, deeper analysis risks being built on flawed assumptions or misunderstood data. With it, organizations gain clarity, discover valuable patterns, and make smarter downstream decisions—turning raw data into insight-ready intelligence.