AI Analytics
How Scoop's AI investigates and analyzes your data
AI Analytics
Scoop is an autonomous AI data analyst that thinks, investigates, and discovers insights like a senior analyst would. This section explains how Scoop's AI capabilities work.
How Scoop's AI Works
The Investigation Approach
Unlike traditional BI tools that just show you charts, Scoop investigates your data:
- You ask a question - in plain English, like "Why did sales drop in March?"
- Scoop plans an investigation - determines what analyses will find the root cause
- Multiple probes execute - each uncovering different aspects of the answer
- AI synthesizes findings - combining evidence into actionable insights
This is fundamentally different from dashboards or chat-based SQL tools. Scoop doesn't just run queries - it reasons about your data.
Key AI Capabilities
| Capability | What It Does | Example |
|---|---|---|
| Automated Analysis | Instantly understands your data structure, types, and relationships | Upload a CSV → Scoop identifies dates, metrics, dimensions automatically |
| Natural Language | Ask questions in plain English | "What's driving customer churn?" |
| Investigation | Multi-step analysis that finds root causes | Discovers that churn is driven by support response times > 7 days |
| Machine Learning | Clustering, prediction, and comparison without code | Automatically segments customers into behavioral groups |
| Narrative Generation | Writes business-friendly summaries | "Revenue increased 23% primarily due to enterprise segment growth" |
AI-Powered Features
Automated Dataset Analysis
When you upload or connect data, Scoop automatically:
- Detects data types and column meanings
- Identifies relationships between fields
- Generates summary statistics
- Suggests relevant analyses
Learn more about Automated Dataset Analysis
Machine Learning Analytics
Scoop makes enterprise ML accessible without coding:
- Predictive Analysis - What factors predict an outcome?
- Segmentation/Clustering - Find natural groups in your data
- Group Comparisons - What differentiates high vs low performers?
- Time Period Analysis - What changed between periods?
AI-Powered Presentations
Scoop doesn't just find insights - it presents them:
- Auto-generates executive slide decks
- Writes narrative summaries for each chart
- Applies your brand colors and templates
- Creates presentation-ready visualizations
Understanding Scoop's AI
Transparency
Every AI analysis in Scoop is transparent:
- You can see the queries that were run
- ML model rules are shown in plain English
- Confidence levels are provided
- All calculations are auditable
Accuracy
Scoop's AI is built on real analytics, not just language models:
- Queries execute against your actual data
- ML uses proven algorithms (decision trees, clustering)
- Results are validated, not hallucinated
- Statistical significance is calculated
When AI Says "No Pattern Found"
This is valuable information! It means:
- The factors you're looking at don't explain the outcome
- You should investigate other variables
- Saves you from chasing false correlations
Getting Started with AI Analytics
- Upload or connect data - Scoop's AI immediately starts analyzing
- Ask a question - Try "Why did [metric] change?" or "What predicts [outcome]?"
- Review the investigation - Watch Scoop's multi-probe analysis unfold
- Act on insights - Get specific recommendations, not just data
Tip: Start with "why" questions. They trigger Scoop's most powerful investigation capabilities.
Updated about 23 hours ago