Configure profiles - Precisely Data Integrity Suite

Data Integrity Suite
Product
Spatial_Analytics
Data_Integration
Data_Enrichment
Data_Governance
Precisely_Data_Integrity_Suite
geo_addressing_1
Data_Observability
Data_Quality
dis_core_foundation
Services
Spatial Analytics
Data Integration
Data Enrichment
Data Governance
Geo Addressing
Data Observability
Data Quality
Core Foundation
ft:title
Data Integrity Suite
ft:locale
en-US
PublicationType
pt_product_guide
copyrightfirst
2000
copyrightlast
2026

Profilers analyze datasets to assess completeness, consistency, distribution, and detect missing values, duplicates, and outliers.

Profilers scan your data to find quality issues, patterns, and changes over time. They measure completeness (missing values), uniqueness (duplicates), and data patterns to establish a baseline and detect drift.
  1. Set up a data connection before you create a profile. For more information, see Add datasource and connection.
  2. Go to Observability > Profilers.
  3. Click Create Profile.
  4. On the Source page, select the datasets and fields you want to profile.
    The default analysis rule applies automatically and measures completeness, uniqueness, data patterns, and detected data types. Click Next.
  5. On the Profile page, review the default analysis rule and click Next.
  6. On the Finalize page, enter a Name and Description for the profile.
  7. Enable the scheduler and select how often the profile runs.
  8. Click Save to create the profile, or Save & Run to create and run it immediately.
    Your profile is created. You can select multiple assets from different data sources. Search for your profile by name on the Profilers page.
    Tip: Only assets cataloged in the Data Integrity Suite appear in the dataset list.
  9. Manage your profile from the Profilers page.
    • Run: Click the ellipsis next to the profile and select Run Profile. An error icon appears if the run fails. Hover over it to see details.
    • Stop: Click the ellipsis and select Stop Profile.
      Warning: Stopping removes previous run statistics.
    • Edit: Click the ellipsis and select Edit. Update datasets, fields, or schedule, then save.
    • Duplicate: Click the ellipsis and select Duplicate to create a copy with different settings.
    • Delete: Click the ellipsis and select Delete, then confirm.
      Warning: Deletion is permanent and removes the associated schedule.
    • Sort: Click column headers to sort by profile name, completeness percentage, or last run date.
  10. Check the scheduler status in the profile row.
    • Enabled: Profile runs on schedule. Click to view history or toggle off.
    • Disabled: Profile doesn't run on schedule. Click to toggle on.
    • Suspended: Scheduler stopped after ten consecutive failures. Click to investigate.
    • Not Configured: No schedule exists. Click to create one.
    • Unavailable: Scheduler service is down.

Explore profiling results

Data profiling results analyze your data across multiple parameters, including completeness and uniqueness. The analysis identifies potential data integrity issues, systematic errors, and quality shifts that could impact accuracy. Profiling results display at three levels: profile, table, and column. Column-level results follow your configured profiling rules.

The profiling results page shows your data quality across three levels: profile (overall), table (by table), and column (by field). The page has three sections: header with run details, left pane with tables and columns, and report pane with analysis.

  1. From the Profilers page, click a profile name to view its results.
    The results page opens showing the header, left pane, and report pane.
  2. Review the header for profile details: name, description, run date, creator, duration, and rows processed.
  3. In the left pane, click a table name to view table-level metrics in the report pane.
  4. In the left pane, click a column name to view column-level metrics in the report pane.

Profiling metrics and visualizations

Reference guide for metrics and visualizations in profiling results.

Table-level metrics

Metric What it means
Completeness (%) Percentage of rows with all fields filled. High completeness means fewer missing values.
Asset Details Source information: schema, connection name, host, database.
Table popularity Number of times the table was queried in the last 24 hours. Available for Snowflake, Redshift, and BigQuery.
Note: Snowflake: Only users with accountadmin role or database access can see popularity. For more information, see Enabling Snowflake database usage for other roles.

Column-level metrics

Metric What it means
Column Column name. Click to view detailed results.
Data Type Column data type (text, number, date, etc.).
Completeness (%) Percentage of filled records. Low completeness indicates missing values. In detailed view: breakdown of filled, empty, and blank records. Click to view specific records in each category.
Uniqueness Percentage of distinct values. High uniqueness means fewer duplicates. Detailed breakdown:
  • Unique: Values appearing only once
  • Non-unique: Values appearing more than once (duplicates)
  • Distinct: Total number of different values

Example: In a column with: Roger, Gigi, Gigi, Gigi, Garey, Elena, Brad, Brad

  • Unique: Roger, Garey, Elena (appear once)
  • Non-unique: Gigi, Brad (appear multiple times)
  • Distinct: 5 different values total
Null Count Number of empty values in the column.
Detected Type Identified data pattern (email, phone, city, name, address, etc.).
Analysis metrics Additional metrics by data type:
  • Text Metrics: Minimum and maximum text length
  • Value Distribution: How often each value appears. Helps spot patterns and outliers
  • Text Length Distribution: How text lengths vary across the column
  • Number Metrics: For numeric columns: lowest, highest, and average values
  • Stats: For numbers: min, max, average. For text: length range

Popularity counts by data warehouse

Data warehouse What's included
Redshift Current run queries plus last 24 hours
BigQuery Last 24 hours only (current run may not appear immediately)
Snowflake Last 24 hours only (excludes current run)

Visualizations

Chart type Axes What it shows
Percentile X: values; Y: percentiles (0–100) Where values fall relative to others. Example: If 2008 is at the 75th percentile, 75% of values are lower and 25% are higher. Values may change between runs because they're approximated.
Histogram X: value ranges; Y: frequency (count) How values are distributed across ranges. Example: A bar showing "115,654" for "1970-1975" means 115,654 values fall in that range. Doesn't display when all values are identical.
Detected Type N/A Identified data patterns and confidence levels. Select the Detected Type tab to view. Confidence shows percentage certainty (for example, 98% means the system is 98% sure the data matches that pattern).

Monitor profiling trends and run history

Trends show how metrics change across multiple profile runs. Run history shows when each run occurred, how long it took, and how many rows were processed. Together, they give you visibility into your data quality over time.

You can view trends for: total records, completeness, uniqueness, number ranges, text length, and detected type confidence.

  1. From the Profilers page, click a profile name to open the results page.
  2. To view trends, click the Trend icon next to the metric you want to analyze.
    A side panel opens showing the trend graph and historical data table.
  3. Review the trend panel: it shows the metric name, a graph of changes over time, and a table of historical values.
  4. Use the Period dropdown to filter the trend by week, month, year, or all runs.
    The graph and table update to show data for your selected period.
  5. To view run history, locate the run information in the top-right corner of the results page.
  6. Click the < or > buttons to browse between runs, or click Latest to return to the most recent run.
  7. Review the run details: date, time, creator, duration, and rows processed.