Certainly! Below is an example of how to draft **5 data quality rules** for the **”Transaction_Amount”** attribute in a dataset.

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**Data Quality Rules for Transaction_Amount Attribute**

**Attribute Overview:**
The **Transaction_Amount** attribute represents the monetary value of a transaction in the dataset. Ensuring that this field is accurate, consistent, and valid is essential for reliable business analysis, financial reporting, and decision-making.

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### **1. Rule: Positive Transaction Amounts**

– **Description:**
Ensure that **Transaction_Amount** is always a **positive number**. Negative values indicate errors in data entry or processing and should not be accepted.

– **Validation:**
– If the **Transaction_Amount** is less than or equal to 0, the record should be flagged as invalid.
– Action: Flag and review these records for correction.

– **Example:**
A **Transaction_Amount** of **-100.50** should be flagged as invalid.

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### **2. Rule: Currency Consistency**

– **Description:**
Ensure that the **Transaction_Amount** is consistently represented in the same currency across the dataset. If multiple currencies are used, a separate currency field should be provided to identify the currency type.

– **Validation:**
– **Transaction_Amount** values must be cross-checked against the currency code provided (e.g., USD, EUR).
– If the currency is not specified or is inconsistent, the record should be flagged for review.

– **Example:**
A **Transaction_Amount** of **100.00** must be accompanied by a consistent **Currency_Code** such as **USD** or **EUR**.

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### **3. Rule: Range Validation**

– **Description:**
Ensure that **Transaction_Amount** falls within an expected range based on business rules, historical data, or predefined thresholds.

– **Validation:**
– Transaction amounts should be within reasonable bounds, such as between **$0.01** and **$1,000,000**.
– Any value outside this range should be flagged as an anomaly for further investigation.

– **Example:**
A **Transaction_Amount** of **1,500,000** may be flagged as out of range if the upper threshold is set at **$1,000,000**.

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### **4. Rule: No Null or Missing Values**

– **Description:**
Ensure that the **Transaction_Amount** field is never null or missing, as it is a critical attribute for financial analysis.

– **Validation:**
– Any record with a missing or null **Transaction_Amount** should be flagged for review.
– Action: The missing values should either be imputed based on business logic or corrected by the data entry team.

– **Example:**
A record with a null **Transaction_Amount** value should be flagged as incomplete and investigated.

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### **5. Rule: Consistent Decimal Precision**

– **Description:**
Ensure that **Transaction_Amount** has consistent decimal precision across all records. This is crucial for accurate financial reporting and analysis.

– **Validation:**
– **Transaction_Amount** should have a consistent number of decimal places, typically two decimal places for monetary values (e.g., **100.50**).
– If the precision is inconsistent, it should be flagged for review and corrected to ensure uniformity.

– **Example:**
A **Transaction_Amount** of **100.5** should be corrected to **100.50** to match the expected precision.

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### **Conclusion:**

The **Transaction_Amount** attribute is crucial for maintaining the integrity of financial datasets. By enforcing these five data quality rules—ensuring positive values, currency consistency, range validation, non-null entries, and consistent decimal precision—we can improve the accuracy, completeness, and reliability of the dataset. Regular monitoring and validation of these rules will ensure that the data remains of high quality, facilitating accurate analyses and decision-making.

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This technical explanation provides a clear set of **data quality rules** designed to maintain the integrity of the **Transaction_Amount** attribute. The rules are structured for clarity and focus on specific validation steps to ensure data accuracy and consistency.