> For the complete documentation index, see [llms.txt](https://help.delpha.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://help.delpha.io/delpha-for-salesforce/how-to-faq/delpha-data-quality/what-are-the-data-quality-dimensions-for-contact-name.md).

# What are the Data Quality dimensions for Contact Name

{% hint style="info" %}
Name analysis relies on the standard `Contact.Name` field
{% endhint %}

## Understanding Delpha’s Data Quality Dimensions for Name

Delpha evaluates each field using **key data quality dimensions**. Below is how each dimension applies to **Name field**, helping users interpret scores and drive cleanup efforts.

## Data Quality Dimensions Explained

### **Completeness**

> **Question:** Are both first and last names present?
>
> **Computation:** 1 if both are present, 0 if either is missing or empty.

### **Validity**

> **Question:** Do both names conform to valid character and pattern rules?
>
> **Computation:** 1 if both are valid, 0 if either is invalid.      
>
> *Example:*
>
> * Input: `Jean` → Valid
> * Input: `J3an!` → Invalid                                                                                                                                                                                                                                                                                                                                                  

### **Uniqueness - NA**

### **Consistency**

> **Question:** Do the original and [normalized names match](/delpha-for-salesforce/how-to-faq/delpha-data-quality/how-contact-name-normalization-works.md)?
>
> **Computation:** 1 if both first and last names are unchanged after normalization, 0 otherwise.        
>
> *Example:*
>
> * Input: `Jean` → Normalized: `Jean` → Consistency: 1
> * Input: `Jéan` → Normalized: `Jean` (if accents removed) → Consistency: 0

### **Accuracy**

> **Question:** How likely is the name to be correct (not reversed or misspelled)?
>
> * **Reversed Names Detection**
>   * **Purpose:** Detects if the first and last names are likely swapped.
>   * **Method:**
>     * Uses Bayesian/probabilistic scoring based on the frequency of each name as a first or last name.
>     * If the reversed score exceeds a threshold (e.g., 0.7), the names are considered reversed.
>   * **Edge Cases:**
>     * Handles ambiguous names (common as both first and last names) with probabilistic logic.
> * **Misspelled Names Detection**
>   * **Purpose:** Identifies likely misspellings in either name.
>   * **Method:**
>     * Uses phonetic algorithms (e.g., Match Rating Codex, NYSIIS, Beider-Morse) and string comparison against a database of common names.
>     * If either name is likely misspelled, the record is flagged.
>   * **Edge Cases:**
>
>     * Handles accented, Unicode, and strongly normalized forms for robust detection<br>
>
>     **3. Consistency**

### **Timeliness**&#x20;

Related to the last assessment date. &#x20;


---

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