> ## Documentation Index
> Fetch the complete documentation index at: https://docs.figr.design/llms.txt
> Use this file to discover all available pages before exploring further.
# CSV Import
> Import structured data to create realistic, data-driven designs with actual content and user scenarios.
# CSV & Excel Import
Real data creates real designs. Import your actual spreadsheets, user data, and analytics to help Figr understand your content structure and create designs with realistic, representative data.
**Data-driven design:** Designs created with real data look more convincing, reveal layout challenges early, and help stakeholders envision the final product accurately.
## Why Real Data Matters
**Generic placeholder content leads to:**
* Unrealistic layout assumptions
* Missing edge cases (long names, empty states)
* Stakeholder disconnect from reality
* Implementation surprises
**Example:** "John Doe" fits nicely, but "Alexander von Habsburg-Lothringen III" breaks your layout
**Actual content reveals:**
* True space requirements
* Edge cases and data variations
* Realistic user scenarios
* Implementation requirements
**Example:** Real customer names show you need truncation patterns and tooltip expansions
## Supported Data Formats
**Direct file uploads:**
```
✅ CSV files (.csv)
✅ Excel files (.xlsx, .xls)
✅ Google Sheets (via share link)
✅ TSV files (.tsv)
✅ Apple Numbers (exported as CSV/Excel)
```
**File size limits:**
* Up to 100MB per file
* Up to 1 million rows
* Automatic compression for large datasets
**Connect live data sources:**
```
Connection: OAuth authentication
Sync: Real-time or scheduled updates
Permissions: Read-only access
Use case: Live dashboards, dynamic content
```
```
Connection: API key authentication
Sync: Webhook-triggered updates
Permissions: Base-level access
Use case: CRM data, project management
```
```
Connection: Integration token
Sync: Manual refresh or scheduled
Permissions: Database-specific access
Use case: Content management, documentation
```
**Common database export formats:**
```
Analytics Data:
✅ Google Analytics exports
✅ Mixpanel data exports
✅ Adobe Analytics CSV
✅ Custom dashboard exports
CRM Data:
✅ Salesforce reports
✅ HubSpot exports
✅ Pipedrive data
✅ Custom CRM exports
E-commerce Data:
✅ Shopify order exports
✅ WooCommerce data
✅ Product catalog CSVs
✅ Customer behavior data
```
## Data Import Process
**Choose your import method:**
**Quick upload:**
* Drag and drop CSV/Excel files
* Paste Google Sheets share link
* Connect cloud data source
* Import from URL endpoint
**Figr analyzes your data structure:**
**Automatic identification:**
```
Detected columns:
📧 email_address (Email type)
👤 full_name (Person name)
📅 signup_date (Date)
💰 subscription_value (Currency)
📊 usage_score (Numeric)
🏷️ user_type (Category)
```
**Issues and recommendations:**
```
✅ 1,247 valid rows detected
⚠️ 23 rows with missing email addresses
⚠️ 5 duplicate entries found
✅ Date formats consistent
⚠️ Some currency values missing $ symbol
```
**Representative examples:**
```
Sample entries (showing data variety):
John Smith, john@company.com, Free Plan
María González-López, maria@startup.io, Pro Plan
Dr. Alexander Chen, alex.chen@enterprise.com, Enterprise
Sarah Johnson-Williams, s.johnson@agency.co.uk, Pro Plan
```
**Tell Figr how to use your data:**
**Map columns to UI elements:**
```
full_name → User profile displays, table headers
email_address → Contact information, login references
user_type → Access level indicators, feature availability
usage_score → Progress bars, analytics visualizations
signup_date → Timeline displays, cohort analysis
```
**Define realistic usage scenarios:**
```
Scenario 1: Dashboard for high-usage enterprise customer
Data filter: user_type = "Enterprise" AND usage_score > 80
Scenario 2: Onboarding flow for new free users
Data filter: user_type = "Free" AND signup_date < 7 days ago
Scenario 3: Admin view with diverse user types
Data filter: Mixed sample across all user types
```
**Figr identifies potential design challenges:**
```
Long names: "Dr. Alexander Hamilton-Richardson III"
Special characters: "José María Fernández-O'Brien"
Empty values: Some users without profile photos
Extreme values: Usage scores of 0 or 100+
Date variations: Different signup patterns
```
**Review how data will be used:**
```yaml theme={null}
Data Usage Summary:
Primary Dataset: Customer data (1,247 records)
Design Applications:
- User tables and lists
- Profile displays
- Dashboard metrics
- Analytics visualizations
Privacy Settings: Anonymize emails, blur sensitive data
Update Frequency: Static import (refresh manually)
Retention: 90 days (configurable)
```
## Data-Driven Design Applications
**Realistic data tables:**
**What Figr considers:**
* Column width requirements for real content
* Sorting and filtering needs based on data types
* Pagination requirements for large datasets
* Responsive behavior with actual content lengths
**Example improvements:**
```
Generic design: Equal column widths
Data-driven design: Email column wider, status column narrow
Generic design: "Show 10 items"
Data-driven design: "Show 25 items" (based on typical usage)
```
**Real metrics and KPIs:**
**Based on actual data ranges:**
* Chart scales reflect real value ranges
* Trend lines show actual patterns
* Anomalies and outliers included
* Seasonal patterns represented
**Realistic KPI displays:**
* Progress bars with actual percentages
* Status indicators based on real thresholds
* Alert states triggered by actual conditions
* Comparison baselines from historical data
**Varied user representations:**
```
Real user profile variations:
👤 Standard User
Name: Sarah Johnson
Role: Marketing Manager
Company: TechStartup Inc.
👤 Executive User
Name: Dr. Alexander Chen
Title: Chief Technology Officer
Company: Fortune 500 Enterprise Solutions
👤 International User
Name: María José Fernández-González
Location: Barcelona, Spain
Language: Español
```
**Design considerations revealed:**
* Name truncation strategies
* Multi-language support needs
* Profile photo fallback patterns
* Contact information display variations
**Realistic form validation:**
**Based on actual data patterns:**
```
Company name field: 15-85 characters (real range)
Email field: 8-45 characters (typical range)
Phone field: Various international formats
Address field: Country-specific variations
```
**Informed by real data issues:**
```
Email validation: Catches actual common mistakes
Name validation: Handles international characters
Phone validation: Supports global formats
Date validation: Considers realistic date ranges
```
**Based on existing data patterns:**
```
Company suggestions: From existing customer list
Location suggestions: Based on user geography
Category suggestions: From actual usage patterns
```
## Privacy & Security
**Data protection:** Figr treats your data with enterprise-grade security and gives you complete control over privacy settings.
**Automatic privacy protection:**
```
Original: john.smith@company.com
Anonymized: j***@company.com
Original: 555-123-4567
Anonymized: 555-***-***7
Original: 123 Main Street, Apt 4B
Anonymized: [Address]
```
```
Financial data: $XX,XXX (preserves magnitude)
ID numbers: XXX-XX-1234 (pattern preserved)
Medical info: [Protected Health Information]
```
```yaml theme={null}
Anonymization Rules:
email: "mask_middle"
phone: "mask_except_last_4"
ssn: "full_mask"
credit_card: "mask_except_last_4"
custom_field: "hash_value"
```
**Granular data permissions:**
```yaml theme={null}
Team Member Access:
Designers: Anonymized data only
Product Managers: Full data access (with consent)
Developers: Structure only, no personal data
Stakeholders: Aggregated metrics only
```
**Configurable retention policies:**
```
Import Data Retention:
- Design context: Permanent (anonymized)
- Raw data: 90 days (configurable)
- Aggregated insights: 2 years
- Personal identifiers: 30 days maximum
```
## Advanced Data Features
**Connect related datasets:**
**Example relationships:**
```
Customer Data + Order History:
- Customer profiles with purchase patterns
- Lifetime value calculations
- Behavior-based segmentation
User Data + Analytics:
- Activity-based user profiles
- Feature usage patterns
- Engagement scoring
```
**Keep designs current with live data:**
```yaml theme={null}
Update Schedule:
- Daily: Critical business metrics
- Weekly: User behavior data
- Monthly: Demographic data
- On-demand: Campaign or event data
```
```yaml theme={null}
Trigger Events:
- New data available in source system
- Significant data changes detected
- Manual refresh requested
- Design iteration created
```
**Track data changes over time:**
```
Data Version 1.0: Launch baseline (1,000 users)
Data Version 1.1: Post-marketing campaign (1,500 users)
Data Version 1.2: Feature release impact (1,800 users)
```
**Generate additional realistic data:**
```
When your dataset is limited, Figr can:
✅ Generate similar synthetic records
✅ Create variations of existing patterns
✅ Add realistic edge cases
✅ Expand datasets for testing layouts
✅ Create multi-language versions
Example: 50 real customers → 500 synthetic customers
with similar patterns for stress-testing designs
```
## Best Practices for Data Import
**Prepare quality data:**
✅ **Clean data** before import (remove duplicates, fix errors)
✅ **Representative sample** (include edge cases and variations)
✅ **Current data** (recent enough to be relevant)
✅ **Complete records** (minimal missing values)
✅ **Diverse examples** (different user types, scenarios)
**Protect sensitive information:**
✅ **Remove unnecessary PII** before upload
✅ **Use test/demo data** when possible
✅ **Enable anonymization** for real data
✅ **Check team permissions** before sharing
✅ **Review data retention** settings regularly
***
Learn how to import PDFs, design specs, and other documents to build comprehensive product context.
[PDF Processing →](/designing/input/pdf-processing)