Working with Datasets in Scoop for Slack
Dataset Mastery: Your Gateway to Insights
Master the art of dataset management to unlock the full power of Scoop's analytics capabilities.
Understanding the Dataset Ecosystem
🌐 Three Types of Datasets
1. Organization Datasets 🏢
- Company-wide data sources
- Live connections to business systems
- Automatic refresh schedules
- Shared across teams
- Examples: CRM, ERP, Marketing platforms
2. Personal Datasets 👤
- Your uploaded files
- Private by default
- Full control over sharing
- Perfect for ad-hoc analysis
- Examples: Excel reports, CSV exports
3. Channel Datasets 📣
- Auto-mapped to specific channels
- Context-aware selection
- Team-aligned data
- Admin configured
- Examples: Sales data in #sales
![Screenshot: Dataset selector showing different dataset types]
Navigating Datasets
🎯 Quick Selection Commands
See Available Datasets
@Scoop show datasets
@Scoop list all data sources
@Scoop what data can I analyze?
Switch Datasets
@Scoop use sales dataset
@Scoop switch to marketing data
@Scoop change to customer analytics
Check Current Dataset
@Scoop current dataset
@Scoop what am I analyzing?
@Scoop status
![Screenshot: Dataset selection dropdown interface]
📊 Understanding Dataset Cards
Each dataset displays rich metadata:
📊 Customer Analytics Dataset
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Type: 🏢 Organization Dataset
Source: Salesforce + Support System
Records: 45,832 customers
Updated: 2 hours ago (Live sync)
Quality: 98% complete
Key Metrics:
• Total Revenue: $45.2M
• Active Customers: 3,421
• Avg Customer Value: $13,200
• Churn Rate: 12%
Top Tables:
• accounts (customer master)
• opportunities (sales pipeline)
• cases (support tickets)
• activities (engagement log)
[📥 Use This Dataset] [ℹ️ More Info]
Organization Datasets Deep Dive
🔗 Connected Systems
CRM Platforms
- Salesforce: Accounts, Opportunities, Leads
- HubSpot: Contacts, Deals, Activities
- Pipedrive: Deals, Organizations, People
- Microsoft Dynamics: Customers, Sales
Support Systems
- Zendesk: Tickets, Satisfaction, Agents
- Intercom: Conversations, Users, Tags
- Freshdesk: Tickets, Contacts, Groups
Marketing Tools
- Google Analytics: Traffic, Conversions
- Marketo: Campaigns, Leads, Programs
- Mailchimp: Campaigns, Subscribers
Financial Systems
- QuickBooks: Invoices, Customers
- Stripe: Payments, Subscriptions
- NetSuite: Transactions, Accounts
🔄 Data Freshness
Dataset: Sales Pipeline
Last Sync: 10 minutes ago
Next Sync: In 20 minutes
Sync Status: ✅ Healthy
Recent Changes:
• 12 new opportunities
• 34 updated stages
• 5 closed deals
[🔄 Refresh Now] [⚙️ Sync Settings]
🔐 Permission Model
Access Levels:
- Full Access: All data, no restrictions
- Department: Your team's data only
- Role-Based: Based on Slack groups
- Custom: Admin-defined rules
Security Features:
- Row-level security
- Column masking for PII
- Audit trail of access
- Compliance controls
Personal Dataset Management
📤 Creating Personal Datasets
From File Upload:
You: [Uploading quarterly_review.xlsx]
Scoop: 📊 Creating personal dataset...
✅ "Q4 Review Data" ready for analysis
This dataset includes:
• 15,420 records
• 12 analysis-ready columns
• Date range: Oct-Dec 2024
What would you like to explore?
From Analysis Results:
You: Save this filtered view as a dataset
Scoop: 💾 Saved as "High-Value Customers"
This personal dataset contains:
• 342 customers
• Filtered: LTV > $50,000
• All original columns preserved
[📊 Switch to New Dataset] [🔙 Keep Current]
![Screenshot: Personal dataset created from uploaded file]
🗂️ Organizing Personal Datasets
Naming Best Practices:
✅ Good Names:
• "2024_Q4_Sales_Analysis"
• "Customer_Segmentation_Dec"
• "Marketing_Campaign_Results"
❌ Avoid:
• "data"
• "test"
• "final_final_v2"
Dataset Actions:
@Scoop rename dataset to "Executive Dashboard Data"
@Scoop add description "Monthly KPIs for board meeting"
@Scoop tag dataset with #finance #monthly
@Scoop delete old datasets
🔄 Dataset Lifecycle
Personal Dataset: Marketing Leads
Created: Dec 1, 2024
Last Used: Dec 15, 2024
Size: 2.4 MB
⚠️ This dataset hasn't been used in 14 days
Options:
[📊 Use Dataset] [🔄 Update Data] [🗑️ Delete] [📤 Share]
Channel-Mapped Datasets
🎯 Automatic Context
How Mapping Works:
#sales-team → CRM Dataset
#marketing → Campaign Dataset
#support → Ticket Dataset
#finance → Revenue Dataset
#product → Usage Dataset
Smart Detection:
- Channel name analysis
- Member role detection
- Historical query patterns
- Admin preferences
🔧 Configuration
For Admins:
@Scoop map dataset "Enterprise CRM" to #enterprise-sales
@Scoop set default dataset for #analytics
@Scoop show channel mappings
Override When Needed:
In #sales channel:
@Scoop use marketing dataset for this query
@Scoop temporarily switch to support data
Advanced Dataset Features
🔍 Dataset Discovery
You: Which dataset has customer churn data?
Scoop: 🔍 Found churn data in 3 datasets:
1. Customer Analytics (Primary)
• churn_flag column
• churn_date column
• churn_reason field
2. Sales CRM
• account_status (includes "Churned")
• close_lost_reason
3. Support Tickets
• cancellation_tickets table
Recommend using Customer Analytics for comprehensive analysis.
[Use Customer Analytics] [Compare All Three]
🔄 Dataset Relationships
You: Show relationships in this dataset
Scoop: 🔗 Analyzing data structure...
Key Relationships Found:
• Customer ID links to multiple orders
• Product categories have hierarchies
• Time-based patterns detected
Understanding these helps you:
- Ask more sophisticated questions
- Navigate data relationships
- Build better queries
[Explore Relationships] [View Schema]
📊 Dataset Profiling
@Scoop profile this dataset
Dataset Profile: Sales Pipeline
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Table Structure:
• 8 tables, 127 total columns
• Primary table: opportunities (45K rows)
• Most joined: accounts (92% queries)
Data Quality:
• Completeness: 94%
• Missing: phone (45%), website (23%)
• Duplicates: 0.2% (78 records)
Usage Patterns:
• Top queries: Revenue, Pipeline, Forecast
• Peak usage: Mon 9-11 AM
• Power users: Sarah, Mike, Lisa
Optimization Suggestions:
• Add index on close_date
• Archive records > 2 years
• Standardize industry field
🎨 Custom Views
Create Filtered Views:
You: Create a view of enterprise accounts only
Scoop: 🎨 Creating custom view...
View: "Enterprise Accounts"
Filters:
- revenue > $1M
- employees > 500
- type = "Enterprise"
Results: 342 accounts (8% of total)
This view is now available as a virtual dataset.
[Use Enterprise View] [Edit Filters]
Saved Calculations:
You: Add customer health score to this dataset
Scoop: 🧮 Adding calculated field...
Health Score =
(Usage × 0.4) +
(NPS × 0.3) +
(Support × 0.2) +
(Payment × 0.1)
✅ Field added and available for all queries
Dataset Best Practices
🎯 Choosing the Right Dataset
Match Dataset to Question:
Revenue questions → Financial dataset
Customer behavior → CRM dataset
Campaign performance → Marketing dataset
Product usage → Analytics dataset
Start Broad, Then Narrow:
1. Use comprehensive dataset
2. Explore available fields
3. Create filtered view if needed
4. Save as personal dataset for reuse
🔍 Dataset Exploration
First Time with Dataset:
"describe this dataset"
"show me all tables"
"what are the key metrics?"
"show sample records"
"what questions can I answer?"
Understanding Relationships:
"how are tables connected?"
"show me the data model"
"what are the primary keys?"
"explain the relationships"
⚡ Performance Tips
Query Optimization:
- Start with recent data
- Use dataset-specific filters
- Leverage pre-aggregated fields
- Avoid SELECT * patterns
Large Dataset Strategies:
"sample 1000 records first"
"analyze last 30 days only"
"use the summary table"
"what's the optimized query?"
Troubleshooting Datasets
🚨 Common Issues
"Dataset not found"
Solutions:
1. Check exact dataset name
2. Verify permissions
3. Confirm workspace access
4. Try partial name match
"No data returned"
Debug steps:
@Scoop check dataset status
@Scoop when was last refresh?
@Scoop show sample data
@Scoop verify my filters
"Access denied"
Options:
• Request access from admin
• Check Slack group membership
• Verify organization settings
• Use personal dataset instead
🔧 Dataset Maintenance
For Admins:
@Scoop audit dataset usage
@Scoop clean unused datasets
@Scoop optimize slow datasets
@Scoop check sync health
For Users:
@Scoop report data issue
@Scoop suggest new dataset
@Scoop request refresh
@Scoop why is data missing?
Advanced Workflows
🔄 Dataset Comparison
You: Compare sales in CRM vs Finance dataset
Scoop: 📊 Comparing datasets...
CRM Sales Dataset:
• Total: $12.4M
• Deals: 1,234
• Period: Real-time
Finance Dataset:
• Total: $11.9M
• Invoices: 1,198
• Period: Month-end close
Variance: $500K (4%)
Missing in Finance: 36 deals (pending invoice)
[View Details] [Reconcile] [Export Differences]
🎭 Dataset Personas
Create Role-Based Views:
Executive View:
- High-level KPIs only
- Trends and forecasts
- Exception reporting
Analyst View:
- All detailed data
- Custom calculations
- Raw access
Sales View:
- Territory filtered
- Real-time pipeline
- Commission relevant
🤖 Smart Dataset Selection
You: Analyze customer satisfaction
Scoop: 🤔 Found relevant data in multiple datasets:
1. Support Tickets (CSAT scores)
2. NPS Survey Results
3. Product Usage (engagement)
4. CRM (renewal data)
Would you like to:
[Analyze Support CSAT] [Combine All Sources] [Compare Datasets]
Next Steps
Ready to become a dataset power user?
- 📤 Upload Your First File - Create personal datasets
- 📊 Master Visualizations - Beautiful charts from any dataset
- 🤖 ML on Datasets - Advanced analytics
- 🚀 Advanced Features - Deep reasoning capabilities
Pro tip: The right dataset makes all the difference. Spend 30 seconds choosing the correct dataset and save 30 minutes of analysis time! 🎯
Updated 19 days ago