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Uploading Datasets

Learn how to upload and configure datasets in the Meado platform for supply chain optimization.

This comprehensive guide will walk you through the process of uploading datasets to the Meado platform. You'll learn about supported file types, how to configure column mappings, and how to validate your data for optimal results.

Getting Started with Dataset Upload

To upload a dataset in Meado:

  1. Navigate to your project dashboard
  2. Click on the "Datasets" tab in the project navigation
  3. Click the "Upload Dataset" button
  4. Follow the two-step upload process: file selection and column mapping

Upload Dataset Button

This button appears in the datasets section of your project

Supported File Types

Meado supports several file formats and dataset types:

File Formats

  • Excel Files: .xlsx format (recommended)
  • CSV Files: .csv format
  • Shapefiles: .zip format containing shapefile components

Dataset Types

Facility

Warehouses, distribution centers, and storage facilities

Order

Customer orders and delivery requirements

Customer

Customer information and locations

Warehouse

Warehouse capacity and utilization data

Route

Route balancing and optimization data

Shipment

Shipment tracking and logistics data

Vehicle

Fleet information and vehicle specifications

Stop

Delivery stops and time windows

Step 1: File Selection and Type Configuration

The first step in uploading a dataset involves selecting your file and configuring the dataset type:

File Type Selection

Choose the appropriate dataset type for your data

File Upload Area

Drag & drop an Excel file (.xlsx, .csv) here

or click to select

Drag and drop your file or click to browse

Special Case: Shapefile Upload

For shapefile datasets, you have two options:

  • Standard Upload: Upload as Excel/CSV and map columns manually
  • Direct Upload: Upload as a zipped shapefile (.zip) and skip column mapping

Shapefile Upload Option

Check this option for direct shapefile uploads

Step 2: Column Mapping

After uploading your file, Meado will analyze the columns and help you map them to the appropriate fields. The system provides intelligent auto-mapping based on column names and data types.

Auto-Mapping Features

  • Intelligent Recognition: Automatically detects common column names and maps them to appropriate fields
  • Geospatial Mapping: Recognizes address and location columns for geospatial data
  • Data Type Validation: Ensures data types match expected field requirements
  • Visual Feedback: Green backgrounds indicate successfully mapped fields

Column Mapping Interface

Geospatial Mapping

2 mapped
Mapped

Field Mapping

3 mapped

Expandable sections for different mapping types

Mapping Types

Geospatial Mapping

Maps address and location data to geospatial fields like city, country, latitude, longitude, etc. Essential for location-based optimization.

Field Mapping

Maps business data fields like names, IDs, quantities, dates, and other operational data specific to your dataset type.

Adding Custom Fields

You can add additional fields that weren't automatically detected:

  1. Click the "Add Field" or "Add Geospatial Field" button
  2. Select the field type from the dropdown menu
  3. Map it to the appropriate column in your data
  4. The field will be added to your mapping configuration

Add Field Button

Use this button to add fields not automatically detected

Data Validation and Error Handling

Meado performs comprehensive data validation during the upload process to ensure data quality and consistency.

Validation Features

  • Data Type Validation: Ensures numbers are numeric, dates are properly formatted
  • Required Field Validation: Checks that essential fields are populated

Validation Error Display

Validation Errors:
  • Row 5: Field 'weight' expected a number but got 'N/A'
  • Row 12: Required field 'name' is missing
  • Row 18: Invalid date format in 'deliveryDate'

Detailed error messages help you fix data issues

Best Practices for Dataset Upload

Data Preparation

  • Clean Your Data: Remove empty rows, fix formatting issues, and ensure consistency
  • Use Descriptive Column Names: Clear column names improve auto-mapping accuracy
  • Standardize Formats: Use consistent date formats, number formats, and text casing
  • Validate Required Fields: Ensure all essential fields are populated

Column Naming Conventions

Recommended Column Names

Geospatial Fields:

  • • city, country, address
  • • latitude, longitude
  • • postalCode, province

Business Fields:

  • • name, id, description
  • • weight, volume, capacity
  • • date, time, status

File Size and Performance

  • Optimal File Size: Keep files under 50MB for best performance
  • Row Limits: Recommended maximum of 100,000 rows per dataset
  • Column Limits: Limit to essential columns to improve processing speed
  • Data Types: Use appropriate data types (numbers, dates, text) for better validation

Troubleshooting Common Issues

Upload Fails

Solution: Check file format (.xlsx, .csv, or .zip), ensure file size is under 50MB, and verify that the file isn't corrupted or password-protected.

Auto-Mapping Issues

Solution: Use more descriptive column names, manually map columns that weren't detected, and ensure your data follows standard naming conventions.

Validation Errors

Solution: Review error messages, fix data formatting issues, ensure required fields are populated, and check that data types match expected formats.

Performance Issues

Solution: Reduce file size, limit the number of columns, remove unnecessary data, and consider splitting large datasets into smaller chunks.

Success: Once your dataset is successfully uploaded and validated, you can use it for supply chain optimization, route planning, and other Meado platform features. The dataset will appear in your project's dataset list and be available for analysis and optimization tasks.