# What are the Data Quality dimensions for Address

{% hint style="info" %}

* [Address analysis support level can differ based on the country](/delpha-for-salesforce/how-to-faq/delpha-data-quality/what-countries-are-supported-for-address-data-quality.md)
* Address analysis applies on the standard fields: 
  * `Contact.MaillingAddress` 
  * `Account.BillingAddress`
  * `Account.ShippingAddress`
* For custom addresses, leverage the Delpha API for reliable data quality checks. 
  {% endhint %}

## Completeness 

All required fields—`street`, `state`, `city`, `postal_code`, `country`—must be present and non-empty.

{% hint style="warning" %}

* For certain countries (`France`, `Japan`, `Germany`, `United Kingdom`), the `state` field is not required. It is mandatory for any other country. 
  {% endhint %}

If any required field is missing or empty, completeness is 0; otherwise, 1.

*Examples:*

* Input: `street=1 rue du Louvre`, `city=Paris`, `postal_code=75001`, `country=France` → Completeness = 1
* Input: `street=123 Main St`, `city=New York`, `postal_code=10001`, `country=USA` , `state=` (missing) → Completeness = 0

## Validity

Validity determines if the [normalized address ](#address-normalization)matches a real-world address with high confidence and completeness.

**How validity is computed:**

* If there is **no normalized match** from the geocoding analysis, validity is `0`.
* If a match exists:

  **Check Validity:** The following conditions must all be true for validity to be `1`:

  * The normalized address and the geocoded match are **not different**
  * The geocoding match's **score** is above the threshold (`0.75` by default).
  * The match is **complete and mapped**: all required fields (`street_number`, `street`, `postal_code`, `country`, `city`, and for some countries, `state`) are present in both the input and the match.

  If any of these conditions fail, validity is `0`.

## Consistency

Consistency checks if, for each required field (street, city, postal code, state, country), the original and normalized values are equal after strong normalization (removing accents, punctuation, and lowercasing).\
If any required field differs after strong normalization, consistency is 0; otherwise, 1.

*Examples:*

* Original: `paris fRance`, Normalized: `Paris France` → Consistency = 1
* Original: `Paris`, Normalized: `Lyon` → Consistency = 0

### Address Normalization

The normalization cleans and standardizes the input fields.

Each address field is normalized using specialized logic:

* **Street:**
  * Cleaned and standardized for casing and accents, with particles compressed to their preferred short form (e.g., 'avenue' → 'Av', 'boulevard' → 'Bd', 'de', 'du', 'la', etc. replaced with a standard, often shorter, form for consistency).
  * *Examples:*
    * Input: `rue du Louvre` → Normalized: `Rue du Louvre`
    * Input: `Avenue de l'Opéra` → Normalized: `Av de l'Opera`
    * Input: `43-45 boulevard Saint-Germain` → Normalized: `43-45 Bd Saint-Germain`
* **City/State:**
  * Normalized for casing, accents, and removes unwanted punctuation (commas, parentheses).
  * *Examples:*
    * Input: `paris` → Normalized: `Paris`
    * Input: `saint-denis (île-de-france)` → Normalized: `Saint-Denis Ile-De-France`
* **Postal Code:**
  * Uppercased and cleaned, keeps special characters.
  * *Example:*
    * Input: `75001` → Normalized: `75001`
    * Input: `sw1a 1aa` → Normalized: `SW1A 1AA`
* **Country:**
  * Normalized for casing, accents, and known aliases using a geo database. Title-cased for presentation.
  * *Examples:*
    * Input: `france` → Normalized: `France`
    * Input: `deutschland` → Normalized: `Germany`
* **Country Code:**
  * Uppercased, removes special characters, converts non-ASCII.
  * *Example:*
    * Input: `fr` → Normalized: `FR`
    * Input: `de` → Normalized: `DE`

## Accuracy & Recommended address <a href="#recommendation-and-accuracy-computation" id="recommendation-and-accuracy-computation"></a>

**How recommendations are generated:**

* If there is **no normalized match**, no recommendations are returned and accuracy is set to -`1`.
* If a match exists:
  1. The geocoded match is used as the recommendation.
  2. If the normalized input street (after strong normalization) contains the geocoded match's street, the match's street is replaced with the input's street (to preserve original formatting).
  3. The **accuracy** is computed as:
     * `accuracy = geocoding_score * completeness_of_recommendation`
  4. f the computed accuracy is **greater than 0.5**, the recommendation is returned; otherwise, no recommendation is returned and accuracy is set to `0`.

### Accuracy status <a href="#label-assignment" id="label-assignment"></a>

**How the status is assigned:**

* If accuracy is -`1` : label is `"Unknown"`.
* If accuracy is **below the threshold** (`0.75` by default): label is `"No"`.
* If accuracy is **greater than or equal to the threshold**: label is `"Ok"`.

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