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.

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

Supported Data Formats

Spreadsheet Files
Cloud Integration
Database Exports

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

Connect live data sources:

Google Sheets

Connection: OAuth authentication
Sync: Real-time or scheduled updates
Permissions: Read-only access
Use case: Live dashboards, dynamic content

Airtable

Connection: API key authentication
Sync: Webhook-triggered updates
Permissions: Base-level access
Use case: CRM data, project management

Notion Databases

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

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

Data Preview & Validation

Figr analyzes your data structure:

Column Detection
Data Quality Check
Sample Data

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

Map Data to Design Context

Tell Figr how to use your data:

Design Context Mapping

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

Scenario Creation

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

Edge Case Identification

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

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)

Data-Driven Design Applications

Tables & Lists
Dashboards & Analytics
User Profiles & Cards
Forms & Input Fields

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)

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:

Input Field Sizing

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

Validation Rules

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

Auto-complete Suggestions

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 Anonymization

Automatic privacy protection:

Personal Information
Sensitive Data
Custom Rules

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]

Anonymization Rules:

email: "mask_middle"
phone: "mask_except_last_4"  
ssn: "full_mask"
credit_card: "mask_except_last_4"
custom_field: "hash_value"

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

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

Advanced Data Features

Data Relationships
Dynamic Data Updates
Data Synthesis

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

Keep designs current with live data:

Scheduled Refresh

Update Schedule:
- Daily: Critical business metrics
- Weekly: User behavior data
- Monthly: Demographic data
- On-demand: Campaign or event data

Webhook Integration

Trigger Events:
- New data available in source system
- Significant data changes detected
- Manual refresh requested
- Design iteration created

Version Control

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

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 →

How to Begin?

Canvas

Inputs and Research

Product Intelligence

Export and Share

Security & Legal

CSV Import

CSV & Excel Import

Why Real Data Matters

Before: Lorem Ipsum Design

After: Real Data Design

Supported Data Formats

Data Import Process

Data-Driven Design Applications

Metric Visualization

Performance Indicators

Privacy & Security

Advanced Data Features

Best Practices for Data Import

Data Quality

Privacy First

Explore Document Processing

​CSV & Excel Import

​Why Real Data Matters

Before: Lorem Ipsum Design

After: Real Data Design

​Supported Data Formats

​Data Import Process

​Data-Driven Design Applications

Metric Visualization

Performance Indicators

​Privacy & Security

​Advanced Data Features

​Best Practices for Data Import

Data Quality

Privacy First

Explore Document Processing

CSV & Excel Import

Why Real Data Matters

Supported Data Formats

Data Import Process

Data-Driven Design Applications

Privacy & Security

Advanced Data Features

Best Practices for Data Import