Data Quality Software That Fixes Issues, Not Just Reports Them
Most data quality tools profile your data and hand back a report. deKorvai profiles, scores, and then cleanses — generating rules with AI, detecting anomalies in real time, and merging duplicates into golden records, across any database or application.
every rule versioned · every change logged & auditable
Data quality measures whether data is complete, accurate, consistent, and valid — and improving it means fixing issues at the source, not just reporting them. deKorvai profiles data automatically, applies reusable rules (or generates them from natural language), scores quality in real time, and cleanses issues as they're found.
Most tools stop at measuring and produce a dashboard. deKorvai closes the loop: it detects duplicates, drift, and anomalies, merges records into governed golden records, and keeps a full audit trail — across any relational database, SaaS application, or ERP it connects to.
Measuring bad data is not the same as fixing it
Profiling tools got very good at surfacing issues. The value only lands when someone acts — and remediation is the work that keeps getting deferred until a report breaks or a project stalls.
The snapshot goes stale immediately
Most tools profile at a point in time. Data drifts every day after, so the quality you signed off last quarter rarely matches what's flowing through today.
Tools report, humans remediate
A dashboard flags 40,000 issues. Cleansing them becomes a manual backlog that competes with every other priority — and loses.
Duplicates hide behind small differences
"Hex Bolt M10x50" and "Hexagonal Bolt M10 x 50mm" are the same part. Exact-match checks miss them; the business pays with inflated stock and duplicate orders.
Rules don't scale across systems
Quality rules written by hand for one database rarely carry to the next, so every system reinvents its own definition of "good data."
Everything data quality needs, on one layer
Every capability below measures continuously and acts under governance — not just reports.
Automated data profiling
Discover metadata, structure, and content patterns automatically — column, cross-table, and content-level analysis that surfaces issues before they spread.
Rule-based validation
Reusable business and technical rules test completeness, accuracy, consistency, and validity — applied identically across every connected system.
AI rule generation
Describe a check in plain language; agentic AI writes the rule, flags anomalies, and proposes remediation, with human approval.
Real-time quality scorecards
Live scores across every quality dimension, with trend insight — so data health is visible at a glance and provable to auditors.
Anomaly & drift detection
Continuous monitoring flags outliers, unexpected distributions, and schema drift the moment they appear, not at the next quarterly review.
Fuzzy duplicate detection
Levenshtein, Jaro-Winkler, and phonetic matching cluster near-duplicates that exact matching misses — the true source of most master-data mess.
Golden records & survivorship
Merge duplicates into a single trusted record with survivorship rules; linked transactions are re-pointed to the surviving record automatically.
Embedded cleansing & remediation
Standardization and cleansing run inside the workflow — invalid records are routed for correction, not just counted on a report.
Continuous monitoring & audit
Ongoing quality monitoring with a full, tamper-evident audit trail behind every rule, score, and change — ready for governance reviews.
The six data quality dimensions deKorvai measures
A shared, measurable definition of "good data" — applied consistently across every system you connect.
| Dimension | The question it answers |
|---|---|
| Completeness | Are all required values present? |
| Accuracy | Does the value reflect reality? |
| Consistency | Does it agree across systems? |
| Validity | Does it conform to the rules? |
| Uniqueness | Are there hidden duplicates? |
| Timeliness | Is it current and fresh? |
Quality issues corrected, not just counted
Four patterns — each measured continuously, decided under governance, and executed as a workflow.
Discover
Auto-profile a source: metadata, structure, distributions.
Score
Rate each dimension and produce a live quality scorecard.
Prioritize
Rank issues by impact so teams fix what matters first.
Evidence
Publish the score with the rules and data behind it.
What trusted data is worth
Lower cost of bad data
Catch errors at the source and cut the rework, wrong decisions, and failed processes that poor data quality quietly funds.
Decisions you can trust
Reporting, analytics, and AI models are only as good as their inputs. Clean, scored data makes every downstream decision defensible.
Faster projects & onboarding
Migrations, integrations, and new-system rollouts stop stalling on data surprises — issues are known and fixed before they block delivery.
Audit & compliance readiness
A live quality position with a full audit trail means governance reviews and regulators meet evidence, not a scramble.
One platform, less sprawl
Profiling, validation, monitoring, and cleansing on one layer removes the licensing and integration cost of four separate tools.
AI-ready foundation
Agentic AI and analytics need trustworthy data. deKorvai keeps the foundation clean so the intelligence built on it is reliable.
Where teams put data quality to work
Continuous data quality governance
Ongoing quality monitoring with real-time scorecards and comprehensive reports for governance and audits.
Master data & deduplication
Fuzzy duplicate detection and golden-record creation for master data across systems.
System consolidation
Profile and cleanse data when merging or consolidating systems, so quality issues are resolved before the merge.
Test data management readiness
Validate and profile data destined for non-production environments as part of the broader platform's test-data workflows.
Migration data readiness
Profile and cleanse before a migration — including programs like an SAP S/4HANA move — so bad data doesn't derail cutover.
Audit-ready data integrity
Continuous monitoring with full audit trails produces a defensible, evidenced data-quality position on demand.
From measure-and-report to measure-and-fix
Traditional data quality tools
- →Profile data at a point in time
- →Flag issues on a dashboard for someone to action
- →Cleansing is a manual backlog that gets deferred
- →Duplicates keep multiplying across systems
- →Rules hand-written per system, inconsistently
- →Quality position reconstructed under audit pressure
With deKorvai
- ✓Profile continuously, with a live score
- ✓Issues corrected through governed cleansing workflows
- ✓Remediation runs as an approved action, not a chore
- ✓Duplicates merged into golden records automatically
- ✓AI generates reusable rules that apply everywhere
- ✓A current, evidenced position ready on demand
The data quality engine inside deKorvai
The DQ Engine is one component of the deKorvai platform, working alongside the ETL Engine, Scrambler, and agentic AI (Agent Master) — connecting to your systems through standard protocols.
Documented integrations
SAP · SAP HANA · Oracle · Microsoft · Snowflake · AWS · Google Cloud · Salesforce · PostgreSQL. Explore ETL, Data Masking, and Agentic AI.
120,000+ records cleansed with AI fuzzy matching
Near-duplicate master data across plants and locations — clustered, reviewed, and merged into golden records, with linked transactions re-pointed automatically. (Delivered on SAP master data, using the same engine that runs on any system.)
Data quality, answered
Part of one unified platform
Data quality is one of four capabilities in deKorvai — explore the rest.
Stop finding data issues too late
See how deKorvai profiles, scores, and cleanses data — and what that changes for your migrations, reporting, and governance.
Book a DemoA working session on your data quality challenges