Understanding Scoop Datasets

When you bring data into Scoop, something powerful happens: Scoop's AI immediately analyzes and understands your data - identifying structure, data types, relationships, and meaning. This is fundamentally different from traditional tools that require manual configuration.

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How Scoop's AI Understands Your Data

Automatic Analysis

When you upload a file or connect a data source, Scoop's AI:

1. Detects Structure - Identifies columns, rows, headers, and embedded totals/subtotals
2. Infers Data Types - Determines which fields are dates, numbers, categories, or text
3. Discovers Relationships - Understands how columns relate to each other
4. Generates Semantics - Creates human-readable descriptions of what each column means
5. Calculates Statistics - Runs summary statistics (min, max, distribution, outliers)

Result: Within seconds, Scoop knows enough about your data to start answering questions - no manual setup required.

The Dataset "Fingerprint"

Scoop creates a unique fingerprint for each dataset based on:

• Column structure and names
• Data types and patterns
• Embedded relationships

This fingerprint allows Scoop to:

• Recognize when new data matches an existing dataset
• Automatically merge updates with historical data
• Track changes over time without manual configuration

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Why This Matters

Traditional BI Approach

1. Upload data
2. Manually define column types
3. Create relationships
4. Build calculated fields
5. Configure time handling
6. Finally... start analyzing

Time to first insight: Hours to days

Scoop's AI Approach

1. Upload data
2. Ask a question

Time to first insight: Seconds

Scoop eliminates the technical overhead that typically requires a data team. Business users can go from raw data to investigation immediately.

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Dataset Types

Basic Datasets

For transactional data where each row is a unique event:

• Sales transactions
• Support tickets
• Log entries

Each new data load adds to the dataset without replacing existing data.

Snapshot Datasets

For data that represents a point-in-time state:

• Pipeline reports (deals change status over time)
• Inventory levels
• Employee rosters

Scoop automatically tracks changes, enabling powerful analysis:

• "What changed since last month?"
• "How long did deals stay in each stage?"
• "Which items moved from X to Y?"

Learn more about Snapshot Datasets

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Intelligent Date Handling

Scoop recognizes that datasets often have multiple dates:

• Load Date - When data was brought into Scoop
• Event Date - When something happened (e.g., sale date)
• Status Date - When a snapshot was taken

Scoop automatically:

• Identifies which columns contain dates
• Understands their meaning
• Allows analysis by any date dimension

Learn more about Date Handling

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Connecting to Data

Scoop can ingest data from:

• File uploads - CSV, Excel, and more
• Connected applications - 100+ SaaS integrations (Salesforce, HubSpot, etc.)
• Databases - Direct SQL connections for custom queries

See Connecting to a Database for database setup.

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In This Section

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Next Steps

Once your data is in Scoop, you can immediately start investigating:

• Ask questions in natural language
• Let the AI find patterns and anomalies
• Generate presentations automatically

The goal is simple: eliminate the technical barrier between you and your data insights.