12 essential data quality testing methods for

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12 essential data quality testing methods with code examples

data quality is paramount for any organization relying on data-driven decisions. poor data quality can lead to inaccurate insights, flawed analyses, and ultimately, bad business outcomes. thorough data quality testing is crucial to ensure the reliability and validity of your data. this tutorial outlines 12 essential data quality testing methods, providing explanations and illustrative code examples using python and common libraries like pandas and great expectations.

**note:** the code examples assume you have a pandas dataframe named `df` containing your data. install necessary libraries using `pip install pandas great-expectations`.

**1. completeness:** checks if all required fields have values.

* **explanation:** identifies missing values in critical columns. high levels of missing data can significantly impact analysis.
* **code (pandas):**
**2. uniqueness:** verifies that unique identifiers (e.g., primary keys) are truly unique.

* **explanation:** ensures that each record is distinct and avoids duplicate entries.
* **code (pandas):**
**3. validity:** confirms that data conforms to defined constraints (e.g., data types, ranges, allowed values).

* **explanation:** checks if data values are within acceptable limits or follow specified formats.
* **code (pandas):**
**4. accuracy:** verifies that data is correct and free from errors.

* **explanation:** often requires comparing data against external sources or using domain knowledge. this is often more difficult to automate fully.
* **code (illustrative - requires external data comparison):**
**5. consistency:** ensures that data is consistent across different sources or within the same dataset.

* **explanation:** checks for discrepancies between related data points.
* **code (pandas):**
**6. timeliness:** checks that data is current and up-to-date.

* **explanation:** crucial for real-time applications and decision-making. ...

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data quality
testing methods
data accuracy
data completeness
data consistency
data validation
data profiling
data cleansing
data integrity
automated testing
manual testing
data governance
test case design
error detection
data reliability

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