Use Data Quality Boost in AI Studio - Precisely Data Integrity Suite

Use Data Quality Boost to assess sample data, review recommended fixes and enrichment, build a pipeline, and compare the quality score before and after changes.

1. Open Data Quality Boost in AI Studio.
   The Data page opens so you can choose the data to assess.
2. On the Data page, choose how to start the assessment.
   • Select Watch it work to start the guided demo.
   • Select an industry sample, such as retail, financial, insurance, or healthcare.
   • Upload a CSV file.
   Uploaded files must be 10 megabytes (MB) or smaller and contain no more than 10,000 rows and 250 columns. Files that exceed these limits show an inline message naming the limit.
   The assessment starts from the demo, selected sample, or uploaded CSV file.
3. On the Quality Analysis page, review the overall score.
   The overall score is the starting quality score for the selected data.
4. Review the prioritized issues.
   The prioritized issues show the data quality problems that Data Quality Boost recommends addressing first. If the score is 100 or no issues are found, you cannot select Remediate Quality Issues.
5. Review the component and column breakdown.
   The breakdown shows where quality issues appear across the assessed data so you can see which components and columns contribute to the score.
6. Review the row quality table.
   The row quality table shows row-level quality details for the selected data.
7. Select Remediate Quality Issues.
   The Fix issues in your data page opens with recommended fixes.
8. On the Fix issues in your data page, review the recommended fixes.
   Data Quality Boost organizes the recommended fixes into operator groups so you can see which actions cleanse, enrich, or standardize the data.
9. Review the operator groups for the recommended fixes.

   Operator group	Description
   Cleanse	Includes fixes that repair data quality issues detected during analysis.
   Enrich	Adds enrichment columns when you include standardization in the plan.
   Standardize	Standardizes values such as addresses, dates, or other detected fields.

10. Review which recommended operators you want to include in the pipeline.
    The selected operators determine which cleanse, enrich, and standardize actions Data Quality Boost applies when the pipeline runs.
11. Select Build pipeline.
    The pipeline build shows step-by-step progress for the selected fixes and enrichment.
12. Review the build progress.
    The progress shows each selected fix and enrichment step as the pipeline builds.
13. On the Results page, compare the before and after scores.
    The score comparison shows how the selected changes affect the overall data quality score.
14. Review the result details.
    The result details show the data quality information available after the selected changes.
15. Optional: Download the results as a CSV file, Excel file, or HTML report.