[DOCS] Expectation selection, group by data quality issue

docs/docusaurus/docs/reference/learn/data_quality_use_cases/distribution.md

@@ -202,7 +202,7 @@ expect_column_median_to_be_between: True
 
 - **Assuming static distributions**: Data distributions often evolve over time due to seasonality, trends, or changes in data collection. It is crucial to regularly update reference distributions and Expectations to reflect the current state of the data.
 
-- **Overlooking data quality issues**: Data entry errors, missing values, or outliers can significantly distort the distribution. Comprehensive data quality checks, including handling missing data and outliers, is an essential complement to distribution analysis and validation.
+- **Overlooking data quality problems**: Data entry errors, missing values, or outliers can significantly distort the distribution. Comprehensive data quality checks, including handling missing data and outliers, is an essential complement to distribution analysis and validation.
 
 - **Not accounting for multimodal distributions**: Some datasets may have multiple peaks, requiring appropriate methods and Expectations that can handle multimodal distributions. Ignoring multimodality can lead to inaccurate interpretations of the data.
 

docs/docusaurus/docs/reference/learn/data_quality_use_cases/dq_use_cases_lp.md

@@ -13,7 +13,7 @@ import LinkCard from '@site/src/components/LinkCard';
 import OverviewCard from '@site/src/components/OverviewCard';
 
 <OverviewCard title={frontMatter.title}>
-  Learn how to use GX to address key data quality issues.
+  Learn how to use GX to address key data quality scenarios.
 </OverviewCard>
 
 A comprehensive data quality strategy relies on a multidimensional approach to achieving and maintaining high-quality data. GX enables you to define and validate data quality checks across a variety of data quality and observability dimensions.

docs/docusaurus/docs/reference/learn/data_quality_use_cases/integrity.md

@@ -227,7 +227,7 @@ Run the following GX Core workflow.
 
 ## Scenarios
 
-The following scenarios provide insight into how Great Expectations can help enforce data integrity across various domains. By combining built-in Expectations with custom SQL checks, organizations can create comprehensive validation frameworks that catch inconsistencies, prevent data quality issues, and ensure compliance with business rules and regulations.
+The following scenarios provide insight into how Great Expectations can help enforce data integrity across various domains. By combining built-in Expectations with custom SQL checks, organizations can create comprehensive validation frameworks that catch inconsistencies, prevent data quality problems, and ensure compliance with business rules and regulations.
 
 ### Cross-system financial reconciliation
 

docs/docusaurus/docs/reference/learn/data_quality_use_cases/uniqueness.md

@@ -8,7 +8,7 @@ import TabItem from '@theme/TabItem';
 
 Data uniqueness is a fundamental aspect of data quality that ensures distinct values are present only once where expected in a dataset. Uniqueness constraints are often applied to columns that serve as primary keys, composite keys, or other unique identifiers. Validating uniqueness is critical for maintaining data integrity, preventing duplication, and enabling accurate analysis.
 
-Failing to validate uniqueness can lead to various data quality issues:
+Failing to validate uniqueness can lead to various data quality problems:
 
 * Duplicates can skew analytics, leading to incorrect conclusions and flawed decision-making. For example, duplicate transactions could overstate revenue.
 * Non-unique identifiers can cause data corruption when merging or joining datasets, resulting in lost data or mismatched records.