Data Quality Assessment
Description
Analyzes data profiles, schema patterns, and existing data quality issues to automatically recommend technical data quality rules that improve accuracy, consistency, completeness, compliance, and reliability.
Details
This recipe automatically scans every table added to your workspace and grades it across eight data quality dimensions — completeness, uniqueness, validity, accuracy, consistency, timeliness, integrity, and conformance — while flagging potential PII. It turns those findings into a single prioritized remediation list and an interactive dashboard, so teams know exactly which tables need attention, why, and what to fix first.
Business value
- Creates a reusable, weighted data quality & AI-readiness scorecard for every table in scope, with a plain-English reason behind each score.
- Detects nulls, duplicate rows, non-unique or missing keys, broken table relationships, and possible PII patterns (email, phone).
- Converts raw findings into one ranked remediation backlog, ordered by severity and issue volume so nothing gets lost.
- Visualizes completeness, PII exposure, freshness, and advanced quality checks side by side so effort goes where it matters most.
Who benefits
- Data quality officers who need a single dashboard to check table health at a glance.
- Data stewards responsible for monitoring quality and enforcing standards.
- Data engineers validating source readiness for downstream pipelines and analytics.
- Governance and compliance stakeholders tracking PII exposure and remediation priorities.
What you receive
How the recipe works (guided flow)
Step 1 — Profile, score & recommend
The recipe scans every table added to the workspace, profiles each column's schema, type, and business role, and checks for nulls, duplicates, non-unique or missing keys, and PII patterns (email, phone). It then runs deeper cross-table checks — comparing shared identifiers across tables for consistency, and testing parent/child relationships for orphaned or missing records — before rolling everything into a weighted Completeness, Uniqueness, Validity, Accuracy, Consistency, Timeliness, Integrity, Conformance, and Security & PII scorecard per table, plus one AI-readiness composite score and a single severity-ranked list of recommended fixes.
Step 2 — Visualize & prioritize
The recipe turns those results into eight linked dashboard views: completeness ranked worst-first, duplicate rows alongside validity issues, PII exposure by table, recommendations broken down by severity per table (so you see exactly which table needs urgent work), recommendations broken down by issue category, the AI-readiness composite score, data freshness (how stale the latest record is), and the advanced accuracy/consistency/integrity/conformance checks. Each panel renders independently, so a table missing one metric never blocks the rest of the dashboard.
Sample insights (illustrative)
| Insight Category | What the recipe discovered | Business Impact |
|---|---|---|
| Headline health check | Ranking tables by completeness and AI-readiness score immediately surfaces the two or three tables dragging down overall workspace quality, instead of an even spread of minor issues. | Leadership can see, in one glance, exactly which datasets are safe to build on and which need work before any AI or reporting use case. |
| Broken relationships between tables | Cross-table checks can catch identifier columns with nulls where a key is expected, and foreign-key columns pointing to records that no longer exist in the parent table. | Fixing these before they reach a join or a dashboard prevents undercounted or duplicated figures in downstream reports. |
| Compliance-ready remediation queue | Columns carrying potential PII (email, phone) are flagged separately from general quality issues, and every recommendation — PII or not — is ranked by severity into one ordered backlog. | Governance teams get a ready-made, defensible worklist instead of having to triage scattered findings themselves. |
Make sure the following ingredient is available:
- The table(s) you want assessed are added to this workspace — this can be data you've uploaded directly, or tables already cataloged in OvalEdge that you've brought into the workspace