> AI agents: this is one page from Mammoth Analytics documentation. The index of all pages as Markdown is https://mammoth.io/docs/llms.txt. Append `.md` to any docs URL, or send `Accept: text/markdown`, to get Markdown.

# How the Mammoth data quality score is calculated

> How Mammoth scores data quality: the six DAMA dimensions and their weights, how each is scored, why a dimension shows a dash, and what Excellent to Poor means.
>
> Applies to: Where: View › View Data Quality › View Details › Overview

The Data Quality Report gives a View an overall score out of 100 from six DAMA (Data Management Association) dimensions, each scored 0–100%. You see it in the **Overview** of the report: click **View Data Quality** in the footer below the data grid, then **View Details**. How to run and read the report is on [Data quality report](https://mammoth.io/docs/learn/explore/data-quality/).

## The six dimensions and their weights

Dimensions count columns with a problem, not rows, so one bad column lowers a dimension by its share of the columns checked. The overall score is the weighted average, rounded down to a whole number.

| Dimension | Weight | What it measures | How it's scored |
| --- | --- | --- | --- |
| **Validity** | 25% | Values unexpected for what the column holds | Share of columns without unexpected negatives (prices, quantities), unexpected zeros, or future dates. Text formats such as email or phone patterns are not checked |
| **Uniqueness** | 25% | Duplicates in columns that should be unique | Share of identifier columns (IDs and keys) without duplicates. A column is flagged when it is less than 95% unique. No identifier columns scores 0% (a dash) |
| **Completeness** | 20% | Nulls, empty fields, empty columns | Share of columns with no missing values |
| **Consistency** | 15% | Case variants in text columns, such as "Active", "ACTIVE" and "active" | Share of text columns without case variants. No text columns scores 100%. Relationships between columns are not checked |
| **Accuracy** | 10% | Numeric outliers, found with the interquartile range (IQR) method | Share of number columns without outliers. Never below 60%, because outliers need domain knowledge to judge. No number columns scores 100% |
| **Timeliness** | 5% | Age of the newest date against the refresh frequency the AI expects (for example, daily for transactions) | 100% within the expected age, 70% up to twice it, 50% up to four times, 25% beyond. With no expected cadence: 100% up to 30 days old, 70% up to 90 days, 50% beyond. No date columns scores 100% |

The report states the measured age and, when there is one, the expected cadence, for example "Data is 1678 days old (very stale vs expected 1 day)". Common causes of stale data are ETL jobs failing silently and sources that stopped sending.

## Why a dimension shows a dash

A dimension that scores 0% shows a dash ("-") instead of a number. For Uniqueness, this happens when every identifier column has duplicates, or when the View has no identifier columns. The note under the dash says which.

Because dimensions count columns, a View with few columns swings sharply, and the weights may not match your priorities. Read the dimension tiles and the issue cards, not only the total.

## Score levels: Excellent, Good, Fair and Poor

| Score | Level | What it means | Typical next step |
| --- | --- | --- | --- |
| 95–100% | **Excellent** | Production-ready; minor formatting issues at most | Publish dashboards, share, automate reporting; record the score for audit or compliance; set up an automation to refresh the data on a schedule |
| 80–94% | **Good** | Acceptable with awareness: some missing values, formatting or minor duplicates | Note the limits; consider minor cleanup |
| 60–79% | **Fair** | Several dimensions affected, some critical issues | Fix critical issues before production; development use only |
| Below 60% | **Poor** | Major problems; results may be unreliable. Quality assessment only, not suitable for analysis | Fix critical issues first; don't proceed to analysis until quality improves; consider fixing at the source |

## Related

- [Data quality report](https://mammoth.io/docs/learn/explore/data-quality/): generate the report and read its sections.
- [Fix common data quality problems](https://mammoth.io/docs/learn/explore/fix-data-quality/): the pipeline steps that raise each dimension.
- [Schedules and triggers](https://mammoth.io/docs/learn/automate/schedules-and-triggers/): refresh data on a schedule so Timeliness stays high.

---
Source: https://mammoth.io/docs/learn/explore/data-quality-score.md · Updated: 2026-11-08