Data AnalystData CleaningAdvancedChain

Data Quality Score Chain AI Prompt

Data Quality Score Chain is a advanced chain for data cleaning. This prompt focuses on identifying and resolving data quality problems that can distort analysis or break downstream workflows. It guides the AI to inspect the dataset systematically, explain the issues clearly, and recommend or apply practical fixes. It is useful when the data is messy, inconsistent, or not yet ready for reliable reporting or modeling. It is structured as a multi-step chain so the AI can reason through the problem in a deliberate order and produce a more complete result. The requested output should be comprehensive, methodical, and suitable for expert review or production-style work.

Prompt text
Step 1: Assess completeness — calculate the percentage of non-null values across all columns and rows. Score: (non-null cells / total cells) × 100.
Step 2: Assess consistency — count type mismatches, formatting inconsistencies, and constraint violations. Score: 100 minus one point per violation type found.
Step 3: Assess uniqueness — count exact duplicate rows and near-duplicate rows. Score: (unique rows / total rows) × 100.
Step 4: Assess validity — count values that fail domain rules (impossible numbers, invalid dates, unexpected categoricals). Score: (valid rows / total rows) × 100.
Step 5: Compute an overall Data Quality Score as a weighted average: completeness 30%, consistency 25%, uniqueness 20%, validity 25%.
Step 6: Return a one-page Data Quality Report: score per dimension, overall score, top 5 issues to fix, and estimated effort to resolve each.

When to use this prompt

Use case 01

When the dataset contains missing values, inconsistent formatting, or suspicious records.

Use case 02

When analysis results look unreliable and you need to validate the raw data first.

Use case 03

When you are preparing data for dashboards, machine learning, or SQL pipelines.

Use case 04

When you need a documented cleaning plan rather than ad hoc fixes.

What the AI should return

The AI should return a practical cleaning assessment with issue-by-issue recommendations or actions, ideally in tables and clearly labeled sections. It should explain what was found, why it matters, and what fix is recommended or applied for each column or record type. When code is requested, the code should be runnable and aligned with the decisions described in the narrative. The final output should make the cleaning process auditable and easy to implement.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Data Cleaning.

Frequently asked questions

What does the Data Quality Score Chain prompt do?+

It gives you a structured data cleaning starting point for data analyst work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for data analyst workflows and marked as advanced, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Data Quality Score Chain is a chain. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Automated Cleaning Code Generator, Column Renaming Plan, Data Type Fixer.