Data Quality Agent > Data quality diagnosis > Sample conversation
  

Sample conversation

Let's take a look at a sample conversation to help you get started on your Data Governance and Catalog data quality diagnosis journey in CLAIRE GPT.
As a data steward in a retail organization, you want to identify the reasons why the vendor data you discovered has poor data quality scores. You want to get insights on the data elements and dimensions that led to poor scores, or any records that violated data quality rules.
To achieve the goal, perform the following steps:
  1. 1Log in to CLAIRE GPT.
  2. 2Start a conversation to get a data quality diagnosis.
    3. Enter the following prompt:
    What could be the reason for low DQ score in DIM_VENDOR?
    The following image shows the response that provides reasons for poor data quality scores in the vendor information table and recommendations on data quality score improvements: The response displays reasons why the data quality score of the vendor table is low.
The data diagnosis skill of the data quality agent identifies the reasons for poor quality scores in DIM_VENDOR data. The agent also shows key insights to low performing dimensions, rules, category distribution, trend analysis, overall summary and recommendations for remediating poor data quality scores in DIM_VENDOR data.