Merge pull request #262376 from rcdun/1129569_aoi_batch_query_interface

1129569 Correct batch query interface from Operator Insights Data Pro…

Author: v-regandowner

articles/operator-insights/concept-data-quality-monitoring.md

@@ -13,9 +13,9 @@ ms.date: 10/24/2023
 
 Every Data Product working on Azure Operator Insights platform has built-in support for data quality monitoring. Data quality is crucial because it ensures accurate, reliable, and trustworthy information for decision-making. It prevents costly mistakes, builds credibility with customers and regulators, and enables personalized experiences.
 
-Azure Operator Insights platform monitors data quality when data is ingested into Data Product input storage (first AOI Data Product Storage block in following image) and after data is processed and made available to customers (AOI Data Product Compute in following image).
+Azure Operator Insights platform monitors data quality when data is ingested into Data Product input storage (first AOI Data Product Storage block in the following image) and after data is processed and made available to customers (AOI Data Product Compute in following image).
 
-:::image type="content" source="media/concept-data-quality-monitoring/operator-insights-architecture.jpg" alt-text="Diagram of Azure Operator Insights Data Product architecture." lightbox="media/concept-data-quality-monitoring/operator-insights-architecture.jpg":::
+:::image type="content" source="media/concept-data-quality-monitoring/operator-insights-architecture.svg" alt-text="Diagram of Azure Operator Insights Data Product architecture. It shows ingestion from on-premises data sources, processing in a Data Product, and analysis and use in Logic Apps and Power BI." lightbox="media/concept-data-quality-monitoring/operator-insights-architecture.svg":::
 
 ## Quality dimensions
 
@@ -24,7 +24,7 @@ Data quality dimensions are the various aspects or characteristics that define t
 - Accuracy - Refers to how well the data reflects reality, for example, correct names, addresses and up-to-date data. High data accuracy allows you to produce analytics that can be trusted and leads to correct reporting and confident decision-making.
 - Completeness - Refers to whether all the data required for a particular use is present and available to be used. Completeness applies not only at the data item level but also at the record level. Completeness helps to understand if missing data will affect the reliability of insights from the data.
 - Uniqueness - Refers to the absences of duplicates in a dataset.
-- Consistency - Refers to whether the same data element does not conflict across different sources or over time. Consistency ensures that data is uniform and can be compared across different sources.
+- Consistency - Refers to whether the same data element doesn't conflict across different sources or over time. Consistency ensures that data is uniform and can be compared across different sources.
 - Timeliness - Refers to whether the data is up-to-date and available when needed. Timeliness ensures that data is relevant and useful for decision-making.
 - Validity - Refers to whether the data conforms to a defined set of rules or constraints.
 
@@ -35,7 +35,7 @@ All data quality dimensions are covered by quality metrics produced by Azure Ope
 - Basic - Standard set of checks across all data products.
 - Custom - Custom set of checks, allowing all data products to implement checks that are specific to their product.
 
-The basic quality metrics produced by the platform are available in a table below.
+The basic quality metrics produced by the platform are available in the following table.
 
 | **Metric**                                                                       | **Dimension** | **Data Source** |
 |----------------------------------------------------------------------------------|---------------|-----------------|