Verify sales data and improve the quality of the data
You’re a data steward at a retail company. Your company's sales data is stored in a data warehouse and cataloged in Data Governance and Catalog. You want to ensure that the quality of the sales data is accurate, reliable, and meets the data quality standards. You identify the data sets that don't have data quality scores associated with them, analyze the data set, get recommendations for data quality rules, and apply the recommended rules to set standards for accuracy, completeness, and consistency for your organization data.
To achieve this goal, you can take the following approach:
•First, you discover tables in your catalog that have no data quality scores. To discover assets, CLAIRE GPT utilizes the data discovery skill.
•After you discover tables with no data quality scores, you assess the profiling statistics of a table of your choice. To show the assessment and profiling results of a data set, CLAIRE GPT utilizes the data quality assessment skill of the data quality agent.
•Finally, you get data quality rule recommendations and apply the rules on the table to establish the data quality criteria and create data quality rule occurrences for the data elements in your catalog. To recommend rules, CLAIRE GPT utilizes the data quality rule recommendation skill of the data quality agent.
By following this approach, you can analyze the quality of the sales data and define the data quality criteria for the data.
2Start a conversation to identify the tables that do not contain data quality scores.
Enter the following prompt:
Show the tables without DQ scores
The following image shows the response displaying the key insights on the tables that don't have data quality scores:
To view the list of all tables without data quality scores, click the card named Tables without DQ scores.
You can expand the reasoning to view the analysis process of the discovery agent.
The following image shows the canvas with a tabular view of the tables without data quality scores:
You can click a table name to open it in Data Governance and Catalog and see further details.
3To understand more about any table, you can assess the profiling details of the table.
Enter the following prompt:
show data profile for SLS_TXN
The following image shows the response displaying the profile of the SLS_TXN table:
You can see that since the table wasn't profiled earlier, CLAIRE GPT performed on-demand data profiling of the table.
To view the detailed profiling information, click the card named Data Profile for SLS_TXN Asset.
The following image shows the canvas with a detailed profile of the SLS_TXN table:
4Based on the profiling results and assessment of the table, get data quality rule recommendations for the SLS_TXN table.
Enter the following prompt:
Recommend Data Quality rules for SLS_TXN
The following image shows the response displaying recommended data quality rules for the SLS_TXN table:
To preview all the rules, click the Recommended Data Quality Rules card.
The following image shows the canvas with a tabular view of recommended data quality rules that are retrieved from Data Quality:
You can copy or download the list and use it for further analysis. To copy the rules to a CSV file, click the Copy icon. To export the rules to a CSV file, click the Download icon.
5If you are happy with the recommended rules, click Accept all recommended rules for SLS_TXN.
After you accept the recommended rules, Data Quality runs the rules on the SLS_TXN table and creates rule occurrences in Data Governance and Catalog.
You were able to quickly identify assets with no data quality scores, perform a data quality assessment, and get data quality rule recommendation using simple natural language prompts.
