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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.

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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.

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Why Real Data Matters

Before: Lorem Ipsum Design

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

After: Real Data Design

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

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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
CSV upload interface showing drag and drop area with file format support

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Data Import Process

1

Upload Your Data

Choose your import method:
Data import interface showing different upload methods and connection options
Quick upload:
  • Drag and drop CSV/Excel files
  • Paste Google Sheets share link
  • Connect cloud data source
  • Import from URL endpoint
2

Data Preview & Validation

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)
3

Map Data to Design Context

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
4

Data Integration Confirmation

Review how data will be used:
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)

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Data-Driven Design Applications

Realistic data tables:
Table design showing real customer data with varied name lengths and content
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)

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Privacy & Security

1

Data Anonymization

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]
2

Access Controls

Granular data permissions:
Team Member Access:

Designers: Anonymized data only
Product Managers: Full data access (with consent)
Developers: Structure only, no personal data
Stakeholders: Aggregated metrics only
3

Data Retention

Configurable retention policies:
Import Data Retention:
- Design context: Permanent (anonymized)
- Raw data: 90 days (configurable)
- Aggregated insights: 2 years
- Personal identifiers: 30 days maximum

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Advanced Data Features

Connect related datasets:
Interface showing how to connect customer data with order data and analytics
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

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Best Practices for Data Import

Data Quality

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)

Privacy First

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

Explore Document Processing

Learn how to import PDFs, design specs, and other documents to build comprehensive product context.PDF Processing →