Explorer is Scoop's analysis and visualization tool. Use it to explore your data, create charts and tables, and build visualizations for your canvases.

What Explorer Does

Opening Explorer

Click the Explorer icon (second in the main toolbar) to open it. Then:

1. Select a dataset to analyze
2. Choose metrics and dimensions
3. View results as chart or table
4. Save to add to canvases

Components of a Data Summary

Every visualization in Explorer combines these elements:

Source Metrics

Numbers in your dataset that can be aggregated:

Scoop automatically detects numeric columns and suggests appropriate aggregations. If totals exist in your source data, Scoop learns your preferred aggregation method.

Attribute Columns

Dimensions by which you group your data:

• Text attributes: Region, Product, Rep Name, Status
• Date attributes: Created Date, Close Date, Order Date
• Categories: Stage, Type, Tier, Segment

Key Performance Indicators (KPIs)

Custom metrics that give you precise control:

• Override default aggregations
• Create formulas combining multiple metrics
• Define complex calculations (ratios, percentages, growth rates)

See Creating KPIs for details.

Filters

Narrow your analysis to specific subsets:

• Include only certain values (Region = "West")
• Exclude values (Status ≠ "Cancelled")
• Date ranges (Last 90 days)
• Numeric thresholds (Amount > $10,000)

Save filters for reuse across visualizations.

Two Analysis Modes

Time Series Analysis

Analyze how metrics change over time:

Revenue by Month
┌────────────────────────────────────────────┐
│    ▄▄                                      │
│   ████  ▄▄    ▄▄▄▄                        │
│  ██████████  ██████  ▄▄                   │
│ ████████████████████████                   │
│ Jan  Feb  Mar  Apr  May  Jun              │
└────────────────────────────────────────────┘

Scoop has deep date intelligence:

• Multiple date columns to choose from
• Automatic period detection (daily, weekly, monthly)
• Year-over-year comparisons
• Rolling averages

See Visualizing Time Series Data for details.

Category Analysis

Compare metrics across categories:

Revenue by Region
┌────────────────────────────────────────────┐
│ West     ████████████████████  $2.4M      │
│ Central  ████████████████      $1.9M      │
│ East     ██████████████        $1.7M      │
│ South    ████████████          $1.5M      │
└────────────────────────────────────────────┘

Use Advanced Mode for category analysis:

• Select any dimension as your axis
• Group by secondary dimensions
• Shows current state (latest snapshot for snapshot datasets)

See Visualizing Non-Time Series Data for details.

Creating a Visualization

Step 1: Select Dataset

Choose the dataset you want to analyze from the dropdown.

Step 2: Choose Metrics

Select which numbers to display:

• Click metrics from the list
• Multiple metrics can be shown together
• Adjust aggregation if needed

Step 3: Set Grouping

Decide how to group your data:

• By time: Select time period (day, week, month, quarter, year)
• By category: Enable Advanced Mode, select dimension

Step 4: Add Filters (Optional)

Narrow your results:

• Click filter icon
• Select column to filter
• Set conditions
• Apply

Step 5: Choose Visualization Type

Select how to display results:

Step 6: Customize Appearance

Adjust visual styling:

• Colors and themes
• Fonts and sizes
• Labels and legends
• Axis formatting

Step 7: Save

Save your visualization to:

• Add to canvases
• Access later
• Share with others

Working with Results

Drilling Down

Click any data point to drill deeper:

• Click a bar → See breakdown by another dimension
• Click a slice → Explore that segment
• Use breadcrumbs to navigate back

Changing Views

Switch between visualizations without losing your query:

• Toggle between chart types
• Switch to table view
• Adjust time periods

Exporting

Export data for further analysis:

• Download as CSV
• Copy to clipboard
• Screenshot chart

Best Practices

Start Broad, Then Focus

1. View overall trends first
2. Identify anomalies or interesting patterns
3. Drill into specific areas
4. Apply filters to focus

Choose the Right Chart

Use KPIs for Precision

When default aggregations aren't right:

• Create a KPI with exact formula
• Reuse across visualizations
• Combine metrics into ratios

Save Filters for Reuse

For common subsets:

• Create saved filter definitions
• Apply consistently
• Update in one place

Troubleshooting

No Data Showing

• Check filters aren't excluding everything
• Verify date range includes data
• Confirm dataset has been processed

Wrong Aggregation

• Create a KPI with correct aggregation
• Check if metric should be sum vs. count vs. average

Chart Looks Wrong

• Verify correct chart type for your data
• Check axis scales
• Review grouping settings

Related Topics

• Charting Time Series Data - Time-based analysis
• Non-Time Series Data - Category analysis
• Creating KPIs - Custom metrics
• Interactive Charts - Charts on canvases