Using CLAIRE GPT > Data Quality
  

Data Quality

CLAIRE GPT integrates with Data Quality to analyze assets, perform on-demand data profiling, detect outliers, and suggest rules to identify data discrepancies. To enhance data accuracy and governance, you can also cleanse data and generate rule specifications from natural language rule descriptions in files or prompts.
The following table lists the Data Quality tasks that you can perform using CLAIRE GPT:
Task
Description
Data quality assessment
Discover and analyze assets based on data quality scores, status, or specific criteria, and view summaries that highlight data types, and key statistics, and outliers. If a data profile is missing, CLAIRE GPT performs on-demand profiling to generate the data profile. Start your conversations to explore and evaluate data quality from a business perspective.
For more information about data quality assessment, see Data quality assessment.
Data quality diagnosis
Identify the reasons for poor data quality scores in your asset. Get insights on the data elements and dimensions that led to the poor scores, the data quality rules that contributed to the poor scores, or any records that violated data quality rules.
For more information about data quality diagnosis, see Data quality diagnosis.
Data quality rule recommendation
Get data quality recommendations for your data assets to define and improve the data quality standards for your data assets. After you accept a rule that CLAIRE GPT recommends, a rule occurrence is created in Data Governance and Catalog.
For more information about data quality rule recommendation, see Data quality rule recommendation.
Data quality cleanse
For the data sets in your catalog or a file that you upload, get recommendations for cleanse rules for your data. After you accept and apply the cleanse rules, the agent identifies and corrects errors or inconsistencies in data sets and generates cleansed data to enhance accuracy, completeness, and reliability. Additionally, generate mapplets to create a reusable data cleansing process for similar data sets in the future.
For more information about data quality cleanse, see Data quality cleanse.
Data quality rule generation
Extract business rules from an uploaded file or prompt that contains data quality rule descriptions in natural language, sample data, or a specified catalog source, and convert them into data quality rule specifications in Data Quality.
For more information about data quality rule generation, see Data quality rule generation.