Data Quality Tool - Quick Intro for Absolute Beginners

Explore the data quality tool with a 20-day free trial, choose an installation method (Google account or business email), and follow the official step-by-step guide to preview all features.

Explore the Calibra data quality interface by connecting, testing, and managing data, then navigate the main menu with scorecards, rules, catalog, reports, alerts, jobs, and admin settings.

Connect to your data sources by selecting connections from the menu or admin panel, explore options like remote files and Kafka streaming, then add and validate new connections.

Onboard the training database and create a data quality job in Explorer, then run profiling on sales data to monitor job status from queue to finished.

Explore the data quality jobs section to monitor status trends (running, finished, failed, unknown) and use sorting, filtering, and csv or pdf exports for analysis.

Learn how a 0-100 data quality score uses nine dimensions—behavior rows, outliers, pattern source records, schema, dupes, and shapes—to assess and track quality over time.

Explore the data profile page to drill down into column statistics, including baseline and run profiles, completeness, top values, and automated Calibra checks for quick data quality insights.

Shapes highlight inconsistencies in data formats as observations on the view results page, like seven-digit phone numbers, and show how to validate, invalidate, assign, or resolve them.

Detect duplicates with fuzzy matching in data quality tool Culebra, flagging near-identical records like identical transaction IDs and customers and generating observations to validate.

Monitor column structure with the schema feature, tracking changes like new, added, or deleted columns and data type shifts. This helps data stewards understand data set evolution through schema observations.

Discover the records feature, which monitors common rows, flags disappeared records, shows row count trends, and helps detect data issues in crucial datasets with an opt-in.

Learn how the source feature of the data quality tool verifies that source data matches target data in a data warehouse, checking schema differences, value discrepancies, and precision losses.

Explore pattern feature in the data quality tool Calibra to connect data patterns, flag anomalies like unusual dosage or mismatched insurance numbers, and support fraud detection.

Turn on data quality features, such as pattern, outlier, duplication, and source, in a job by using the Explorer, selecting the training database and patient data, and running the job.

Learn how data quality detects numerical and categorical outliers by flagging values outside preset bounds; tailor detection with dosage examples like 100–600 mg and 8000 mg.

Learn how to create user defined rules in Calibra using the rule builder after profiling your data with automatic features, with Gulliver's data rule checks available for guidance.

Explore the behavior tab to see how Calibra profiles data automatically and learns typical patterns across runs. Adjust lookback, learning phase, and adaptive rules as needed.

Explore the catalog in collaborate data quality to tag sensitive fields, label PII, assign datasets to business units, and organize data categories for clearer collaboration.

Explore CareerBuilder's data quality reports, focusing on the completeness report and its dimensions such as business unit and data set, with look back ranges and column view.

Explore the data summary report and the data quality check summary, showing weekly average scores, trends, row counts, and detailed automated checks across datasets.

Explore how the coverage report shows data quality across datasets, using the explorer to generate a coverage view that indicates 70% schema coverage with new and existing jobs.

Navigate caliber data quality reporting, review the reports menu, generate a profile report from data set findings, export it to Excel, and connect a BI tool for integrated data quality.

Set up data quality alerts in Collibra by defining alert conditions, selecting a dataset, and configuring email recipients to automatically notify data stewards when scores fall below thresholds.

Learn to automate recurring data quality checks with the Collibra DQ scheduler, setting daily, weekly, or quarterly runs and applying time restrictions in the admin console.

Discover how to build data quality rules with the rule builder, choose simple, free form, or native SQL rules, and use the SQL builder for complex checks after data profiling.

Learn to create generic rule templates that check all columns for empty values, saving hundreds of redundant rules. Access, edit, or delete template rules in the rule library.

Explore rules definitions in the data quality tool, detailing rule name, applicable data set, row values or SQL statements, and a summary view of all rules.

Explore the rule summary to view statistics on the most used rule types, the data set with the most rules, and top rules run, with a configurable date range.

Review data quality score cards that visually display a dataset’s score and dimensions such as shapes, and see how duplicates and outliers shape the stacked bar interpretation.

Explore the list view in data quality scorecards to identify quality issues across datasets. Filter by issue type and range, and sort by type, subtype, or count to narrow scope.

Explore how the assignments view in the data quality scorecard lets you assign validated observations to team members, filter by user, and manage workflow via the assignment queue.

Explore the post view in data quality scorecards to see a heatmap of jobs over the past 30 days with hover details, filters by business unit, and lookback options.