> ## Documentation Index
> Fetch the complete documentation index at: https://docs.allgoodhq.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Cleaning and Enrichment

allGood's List Upload includes a comprehensive library of pre-built data processing steps that automatically clean, standardize, and enrich your contact data. These AI-powered processes ensure your marketing data is consistent, accurate, and ready for campaign deployment.

## Core Enrichment

* [Data enrichment](/use-cases/list-upload/features/enrich) — fetch missing lead information from Scrapin, Waterfall.io, and other providers to fill job title, company, email, and location gaps.

## Job-Related Processing

* [Job function categorization](/use-cases/list-upload/features/job-function) — standardize job titles into Product, Engineering, Marketing, Sales, and other core functions.
* [Job level assignment](/use-cases/list-upload/features/job-level) — map titles to CxO, VP, Director, Manager, IC, and more.
* [Job level assignment (enriched)](/use-cases/list-upload/features/job-level-enriched) — improved hierarchy detection when enriched titles are available.
* [Job role mapping](/use-cases/list-upload/features/job-role) — align titles with prioritized technical roles like SOC or Incident Response.
* [Job role mapping (enriched)](/use-cases/list-upload/features/job-role-enriched) — use enriched titles for more precise role placement.
* [Job title standardization](/use-cases/list-upload/features/job-title-fix) — fix spelling, punctuation, capitalization, and translations.

## Data Standardization

* [Full name splitting](/use-cases/list-upload/features/name-split) — break full names into first/last components.
* [Phone number formatting](/use-cases/list-upload/features/phone-number-fix) — convert phone numbers to E.164.
* [Syntax standardization](/use-cases/list-upload/features/syntax-fix) — clean categorical values and translations.
* [Location standardization](/use-cases/list-upload/features/country-state-fix) — normalize countries, states, and postal codes.
* [Industry normalization](/use-cases/list-upload/features/industry-alignment) — align industries to standard categories.

## How Pre-Built Steps Work

### Automatic Detection

Mary automatically analyzes your CSV data and recommends appropriate processing steps based on:

* Data quality issues detected
* Missing information that can be enriched
* Inconsistencies in formatting or categorization
* Your campaign requirements and goals

### Intelligent Processing

Each step uses AI-powered algorithms to:

* Identify patterns in your data
* Apply standardization rules
* Enrich missing information
* Validate and clean existing data
* Ensure compatibility with your marketing automation system

### Customizable Application

You can choose which steps to apply based on:

* Your specific data quality needs
* Campaign requirements
* Time constraints
* Data privacy considerations

## Benefits of Pre-Built Steps

### Improved Data Quality

* Consistent formatting across all contact fields
* Reduced data entry errors and inconsistencies
* Enhanced lead scoring accuracy
* Better campaign segmentation capabilities

### Time Savings

* Automated processing eliminates manual data cleanup
* Reduced time from list upload to campaign launch
* Fewer errors requiring manual correction
* Streamlined campaign preparation workflows

### Enhanced Targeting

* Better lead segmentation through standardized categories
* Improved personalization with enriched data
* More accurate account-based marketing targeting
* Enhanced lead scoring and qualification

## Getting Started

To use pre-built steps:

1. Upload your CSV file through the List Upload interface
2. Mary will analyze your data and recommend appropriate steps
3. Review and approve the suggested processing steps
4. Monitor the processing progress and results
5. Review the cleaned and enriched data before final upload

## Next Steps

* Review the [Setup Guide](/use-cases/list-upload/setup) for configuration options
* Explore individual feature pages above for detailed information on each processing step