Auction Excellence - Parking Lot Management & Quality Inspection

Overview

Metadata templates provide pre-configured column metadata and calculated fields for common data sources. Instead of manually configuring each column, you can apply a template that automatically sets display names, descriptions, categories, and calculated fields.

System Templates

Pre-built templates for CR Simplified reports

Custom Templates

Create and save your own configurations

Auto-Detection

Templates matched automatically during import

One-Click Apply

Apply complete configuration instantly

What Are Metadata Templates?

A metadata template is a saved configuration that includes:

Column Definitions: Display names, descriptions, and categories for known columns
Calculated Fields: Pre-built formulas for common derived values
Detection Columns: Column names used to auto-match the template

When you import a CSV, the system compares your file’s columns against template detection columns. If there’s a strong match (≥70%), the template is suggested for application.

Unified Dataset System (Epic #659): Metadata templates are now the primary way to identify dataset types. Staffing datasets (Check-In and CR data) are identified by their applied template rather than a separate dataset type table. The applied_template_id column on report_datasets tracks which template was applied during import.

System Templates

System templates are pre-built configurations for common data sources. These are maintained by the system and cannot be modified or deleted.

CR Simplified Templates

Check-In Detail

Purpose: Standard check-in data from CR Simplified reportsDetection Columns: check_in_date, check_in_user, vin, stock_number, sale_type, seller_nameIncludes:

72 column definitions with automotive-specific descriptions
Categories: Vehicle Identification, Check-In Data, Seller Info, Vehicle Details
Calculated field: days_since_checkin

Use When: Importing “Check-In Detail” exports from CR Simplified

Inspector Detail

Purpose: Condition report writer productivity dataDetection Columns: Inspector_Detail_Date_start_date, Inspector_Detail_Date_stop_date, cr_writer, vinIncludes:

66 column definitions for CR timing and quality
Categories: CR Data, Writer Info, Vehicle Info, Quality Metrics
Calculated fields: cr_time_minutes, days_to_cr

Use When: Importing “Inspector Detail” reports for writer analysis

Lot Tracing

Purpose: Vehicle lifecycle tracking through the auctionDetection Columns: check_in_date, cr_date, sale_date, vin, run_numberIncludes:

48 column definitions for vehicle journey tracking
Categories: Check-In, CR, Sale, Vehicle Journey
Calculated fields: days_to_cr, days_to_sale, total_processing_time

Use When: Importing end-to-end vehicle tracking data

Using Templates

During Import

When importing a CSV, the system automatically checks for matching templates:

Upload CSV

Select and upload your CSV file in the Dataset Import wizard

Review Match

If a template matches (≥70% of detection columns found), you’ll see a suggestion:

“This dataset matches the Check-In Detail template (85% match)”

Templates with ≥90% match are auto-selected for your convenience.

Link Compatible Datasets

For complementary dataset types (e.g., Check-In + CR), the wizard suggests linking with existing datasets. This enables cross-dataset queries in the Query Builder.

Apply or Skip

Click Apply Template to use the pre-configured metadata
Click Skip to configure manually or later

Complete Import

Finish the import wizard - metadata is applied automatically and the applied_template_id is set on the dataset

When you apply a template during import, the dataset gains full integration with the Staffing Dashboard and Query Builder. The system knows how to interpret columns like check_in_date and can suggest relevant cross-dataset links.

Applying to Existing Dataset

You can apply templates to datasets that were imported without template configuration:

Open Dataset

Go to Analytics → Datasets → select your dataset

Access Templates

Click Apply Template button in the dataset actions

Select Template

Choose from:

Detected matches (based on column comparison)
All templates (browse available options)

Review & Apply

Preview what will be configured, then click Apply

Template Match Percentage

The match percentage indicates how many of the template’s detection columns were found in your dataset:

Match %	Indication	Recommendation
90-100%	Strong match	Apply template confidently
70-89%	Good match	Review before applying
50-69%	Partial match	May need manual adjustments
Less than 50%	Poor match	Consider different template

A high match percentage doesn’t guarantee all columns will be configured. Only columns that exist in both the template and your dataset are configured.

Custom Templates

Create your own templates to standardize metadata across similar datasets.

Creating a Custom Template

Configure a Dataset

First, fully configure a dataset’s column metadata and calculated fields

Save as Template

Click Save as Template in the dataset actions

Enter Details

Provide:

Template Name: Descriptive name
Source System: Origin of this data format
Report Type: Type of report/export
Description: When to use this template

Set Detection Columns

Select 3-10 columns that uniquely identify this data format

Save

Click Save Template - it’s now available for future imports

Managing Custom Templates

Custom templates are scoped to your auction and can be:

Action	How	Notes
View	Templates panel in Analytics	See all templates with metadata counts
Apply	Dataset actions	Apply to any compatible dataset
Edit	Template details → Edit	Update name, description, columns
Delete	Template details → Delete	Cannot delete system templates

Exporting Templates

Export custom templates as JSON for:

Backup purposes
Sharing between auctions (via manual import)
Version control in your codebase

Template Structure

Understanding the template structure helps when creating custom templates.

Template Components

{
  "name": "Check-In Detail",
  "description": "Standard check-in data from CR Simplified",
  "source_system": "cr_simplified",
  "report_type": "check_in_detail",
  "column_definitions": [
    {
      "column_name": "check_in_date",
      "display_name": "Check-In Date",
      "description": "Date vehicle arrived at auction",
      "category": "Check-In Data",
      "data_type": "date",
      "is_date_field": true
    }
    // ... more columns
  ],
  "calculated_fields": [
    {
      "field_name": "days_since_checkin",
      "display_name": "Days Since Check-In",
      "formula": "CURRENT_DATE - check_in_date",
      "result_type": "number"
    }
    // ... more fields
  ],
  "detection_columns": [
    "check_in_date",
    "check_in_user",
    "vin",
    "stock_number"
  ],
  "detection_threshold": 0.7
}

Column Definition Fields

Field	Type	Purpose
`column_name`	string	Original column name to match
`display_name`	string	Human-readable name
`description`	string	Business context for AI
`category`	string	Grouping category
`data_type`	enum	string, number, date, boolean, timestamp
`is_date_field`	boolean	Indicates date columns for smart suggestions
`is_primary_identifier`	boolean	Marks key identifiers (VIN, stock)
`is_categorical`	boolean	Indicates limited value sets
`enum_values`	string[]	Known valid values for categorical columns

Detection Columns Best Practices

When selecting detection columns:

Choose unique columns - Names specific to this data format
Include 4-8 columns - Enough for reliable matching, not too many
Mix column types - Dates, identifiers, categories
Avoid generic names - Skip id, date, name alone
Include required columns - Columns that should always be present

Template Matching Logic

How Matching Works

When you import a CSV:

System extracts column names from the CSV header
Each template’s detection columns are compared
Match ratio = (matched columns) / (detection columns)
Templates with ratio ≥ threshold are suggested

Match Algorithm

For each template:
  matched = 0
  for each detection_column in template:
    if detection_column exists in csv_columns:
      matched += 1

  match_ratio = matched / count(detection_columns)

  if match_ratio >= detection_threshold:
    suggest template

Adjusting Detection Threshold

The default threshold is 70% (0.7). When creating custom templates:

Higher threshold (0.8-0.9): More precise matching, fewer false positives
Lower threshold (0.5-0.7): More flexible matching, may match unintended datasets

Best Practices

Creating Effective Templates

Do

Document the source system clearly
Include calculated fields users need
Test template with real datasets
Keep detection columns distinctive

Don't

Create overlapping templates
Use generic detection columns
Forget to set data types
Leave descriptions empty

Template Naming Conventions

Source System	Report Type	Example Name
CR Simplified	Check-In Export	”CR Simplified - Check-In Detail”
DealerTrack	Inventory Report	”DealerTrack - Inventory Export”
Custom ERP	Sales Report	”ERP - Weekly Sales”

When to Create Custom Templates

Create a custom template when:

You import the same report format regularly
Multiple users need consistent metadata
The report format is specific to your operation
System templates don’t match your data source

Troubleshooting

Template not detected during import

Cause: Column names don’t match detection columnsSolutions:

Check if CSV column names match exactly (case-sensitive)
Lower the detection threshold for the template
Update detection columns to match your CSV format

Wrong template suggested

Cause: Multiple templates have overlapping detection columnsSolutions:

Make detection columns more specific to each template
Increase detection threshold for the incorrect template
Add more unique columns to differentiate templates

Applied template missing columns

Cause: Template columns don’t exist in your datasetThis is expected. Templates only configure columns that exist in both the template definition and your actual dataset. Missing columns are skipped.Solution: Manually configure any additional columns needed

Cannot delete template

Cause: Attempting to delete a system templateSystem templates are read-only and cannot be deleted. Only custom templates created by your auction can be removed.

Column Metadata

Configure individual column metadata

Calculated Fields

Create derived columns with formulas

Dataset Import

Import CSV datasets

Query Builder

Use configured metadata in queries

Getting Started

User & Location Management

Operations

Analytics Module

Account

Data Management

Configuration

​Overview

System Templates

Custom Templates

Auto-Detection

One-Click Apply

​What Are Metadata Templates?

​System Templates

​CR Simplified Templates

​Using Templates

​During Import

​Applying to Existing Dataset

​Template Match Percentage

​Custom Templates

​Creating a Custom Template

​Managing Custom Templates

​Exporting Templates

​Template Structure

​Template Components

​Column Definition Fields

​Detection Columns Best Practices

​Template Matching Logic

​How Matching Works

​Match Algorithm

​Adjusting Detection Threshold

​Best Practices

​Creating Effective Templates

Do

Don't

​Template Naming Conventions

​When to Create Custom Templates

​Troubleshooting

​Related Documentation

Column Metadata

Calculated Fields

Dataset Import

Query Builder

Overview

What Are Metadata Templates?

System Templates

CR Simplified Templates

Using Templates

During Import

Applying to Existing Dataset

Template Match Percentage

Custom Templates

Creating a Custom Template

Managing Custom Templates

Exporting Templates

Template Structure

Template Components

Column Definition Fields

Detection Columns Best Practices

Template Matching Logic

How Matching Works

Match Algorithm

Adjusting Detection Threshold

Best Practices

Creating Effective Templates

Template Naming Conventions

When to Create Custom Templates

Troubleshooting

Related Documentation