Agentic AI Data Quality Platform

A Unified Platform for Data Quality, Observability & Reconciliation

DataOps Data Quality catches bad data early, reconciles and validates it through to your reports and AI, and keeps every failing record until it is fixed and proven.

Bad data does its damage before anyone notices.

Problems We Solve at Scale

Bad data reaches AI models and agents first

Models train and agents answer on data nobody checked. Wrong answers ship before anyone notices.

AI writes the rules. People still do the rest.

AI drafts hundreds of rules fast, but people still monitor them, triage alerts and find root causes. Teams drown in AI output.

Quality looks fine, but data is missing

Every check passes while only 90% of records arrived. Reports and models run on partial data that no longer matches the source.

Hundreds of alerts, no root cause

Nobody owns the fix. Rules that block a load get switched off “for now” and stay off, and the same failure returns.

Unstructured data goes unchecked

Documents and text feed AI, but checks only cover tables. Missing or duplicate content flows straight into answers.

The context AI relies on is wrong

Knowledge graphs that miss real data relationships, wrong or duplicate metadata, unchecked document metadata. AI uses them anyway.

How Do We Solve Them Differently

Continuous checks catch it first — Drift checks run from the first load, on a schedule or when a pipeline finishes. Machine learning catches what rules miss.
AI drafts the rules, self-healing keeps them current — AI drafts rules from your metadata and profiles, your team approves them, and they adapt as your data changes. 500+ rules in under two hours.
Validation reaches every consumer — Row-by-row reconciliation and completeness checks, validated through to the reports, models and agents that use the data.
Root cause first, then an owner and proof of closure — Lineage traces each failure to where it started. Failing records are kept, ranked and assigned, alerts reach Email, Teams, Slack, Jira or a webhook, and every step is logged.
Documents and text are checked too — Unstructured content is validated alongside tables before it feeds AI.
Context is validated, not assumed — Semantic validation confirms metrics, terms and metadata mean the same thing everywhere AI looks.

60%

No data quality, no AI

AI is only as good as the data and context it reads. Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.

Source: Gartner press release, 26 February 2025.

Most Data Quality tools compete at detection and Monitoring.
DataOps Data Quality goes further.

DataOps Data Quality covers that base, then builds up to reconciliation, BI validation and a trust score for AI. Hover any capability to see our value addition compared to other alternatives

Enterprise data quality suites

Strong at

Governance breadth, master data, matching and packaged content.

Usually stops at

Typically higher cost and slower to stand up. Cover reconciliation and BI reports only in part.

Observability pure-plays

Strong at

Machine learning anomaly, freshness and volume monitoring, started quickly.

Usually stops at

Typically light on remediation, matching and packaged content, and limited on reconciliation and BI reports.

Platform-native data quality

Strong at

Low cost and a fast start inside one data platform.

Usually stops at

Typically stay within that platform, with little reconciliation, BI coverage or remediation.

Datagaps DataOps Suite

Covers the whole path

Mappings, reconciliation, remediation, quality, lineage and BI validation together in one platform, from source to report.

Works with what you already run

Built-in catalog capability, with scores synced to Collibra, Purview and Unity Catalog.

Datagaps DataOps Suite Where other data quality tools fall

Hover any capability to compare

← Swipe to see all capabilities

Business Outcomes we delivered

500B+

Records Validated

10M+

Automated Test Cases

75%

Faster Issue Detection

65%

Fewer Incidents

How does dataops data quality work, from source to Output

DataOps Data Quality checks your data at every stage, from applications to the AI models and BI reports your users rely on.

Understand the data

Check it from source to AI and BI

Resolve it and prove it

reads via JDBC / ODBC

profiles + metadata

approved rules

data to reconcile

pass / fail per rule

failing rows kept

Connect

Link any JDBC or ODBC source, plus files and APIs. 200+ connectors.

Snowflake

Databricks

Azure Synapse

Oracle · DB2

200+ connectors

Profile

Column profiles show ranges, patterns, nulls and distributions.

min

max

Nulls 2.1%

Pattern found

Define rules

AI drafts rules, your team approves. Self-healing rules adapt as your data changes.

AI-drafted

Approved

customer_id is unique

email matches format

Self-healing

Rules adapt automatically as your data changes.

Monitor

Rules and drift checks run on a schedule or when a pipeline finishes, from the first load.

Daily, or on pipeline finish

Checks

Freshness

Volume

Schema drift

Row-count drift

ML anomalies

Reconcile & validate

Compare source to target row by row, then validate through to the BI layer.

Source

Row counts match

Warehouse

Values match

BI report

Report totals match

Score & alert

Lineage traces each failure to its root cause. Scores roll up by column, table, model and domain; alerts reach your tools.

94.6

Domain score

Column

Table

Domain

Adaptive alerts to

Jira

Teams

Slack

Resolve & prove

Root cause found, failing records kept and owned. Fix at source, re-run, close.

Duplicate IDs

Critical

1,204 rows kept

Owner: data steward

Open since 2 days

Open

Resolved

Re-run passed

Audit trail logged

Every resolved issue can become a new rule

First load: rules run with no history

Evidence: failing records are kept

Audit: every rule, score and exception is logged

Illustrative values. Sources shown are examples. Data Quality Monitor, part of the Datagaps DataOps Suite.

From source to AI and BI

Checked at every step

Applications & sources

Profiled and monitored

Pipelines

Checked as each load finishes

Warehouse

Reconciled to the source

AI models & BI reports

Validated end-to-end

Three steps keep every dataset trusted, from first load to final report. Every step is logged.

STEP 1

Understand the data

Connect your sources, profile every column and let AI draft the rules. Your team approves them.

200+ connectorsAI-drafted rules

STEP 2

Check it, source to AI and BI

Rules and drift checks run on a schedule or after each pipeline. Then reconcile row by row and validate through to BI and AI.

FreshnessReconciliationBI validation

STEP 3

Resolve it and prove it

Root cause analysis shows where each failure started. Owners get the alert; failing rows are kept, fixed at source and re-run.

Root causeOwner assignedRe-run passed

How does DataOps Data Quality fit your data architecture?

Your data flows from source systems through pipelines, your warehouse and semantic layer to AI and BI. DataOps Data Quality checks it at every step, without getting in the way.

Deployment

Cloud, on-premise or hybrid, on AWS, Azure, GCP or Databricks.

Security and compliance

SOC 2 Type II and ISO 27001 certified, with role-based access.

Built to scale

50B+ records monitored with 5M+ automated checks.

Sources and tools shown are examples. Any JDBC or ODBC source can be connected.

What does DataOps Data Quality do?

Monitor and detect

AI rule wizard

Drafts rules from metadata, profiles, sample data or plain English.

Self-healing rules

Adapt automatically as your data changes.

Freshness, volume, schema and row-count drift

Built in for data quality and observability.

Anomaly detection

Machine learning flags unusual patterns, ranges and trends.

Checks data in motion

Scores data during ingestion and movement, not only after it lands.

Critical and personal data thresholds

Critical data and personal info are scored separately, with an alert when quality drops below your limit.

Only at Datagaps

Reconcile and validate

Source-to-target reconciliation

Row-level comparison after every load or migration.

Validation through the BI layer

Check the reports and dashboards users read.

Reconciliation, scored

Reconciliation and validation results are scored with your quality rules, rolling up by column, table, model and domain, each against its target and the previous run, with a trend.

Resolve and alert

Adaptive alerting

Email, Teams, Slack, Jira or webhook.

Root cause analysis

Lineage traces each failure to its source and impact, so teams fix the cause, not the symptom.

Failing records kept

Rows that fail are retained for review.

Issues ranked and owned

Criticality, first-detected date and age, with tasks and threaded comments.

Open, Ignored, Resolved

Ignored issues stay visible and need a documented exception.

Audit trail

Every rule, score and exception is logged.

Make data AI-ready

Lineage with scores

Column-level lineage built from your source-to-target mappings.

Semantic validation

Check that metrics, business terms and metadata mean the same thing across systems.

Unstructured data checks

Quality checks on documents, text and their metadata before they feed AI.

Versioned data contracts

Validate datasets against the latest approved schema and rules.

Catalog sync and Git

Share scores with Collibra, Purview and Unity Catalog. Version rules in Git.

Which data quality problems does it solve in your industry?

Banking & financial services

Prove regulated reporting data is complete and consistent

Score each domain and keep audit-ready evidence for reviewers.

See the industry page for banking & financial services

Insurance

Catch bad policy and claims data before filing deadlines

Failing records go to the owning team, not to an inbox.

See the industry page for insurance

Healthcare & life sciences

Keep patient, claims and trial records clean

The embedded AI and your data stay in your environment.

See the industry page for healthcare & life sciences

Consumer goods & manufacturing

Stop bad demand and plant data reaching planning

Reconcile and monitor syndicated data and ERP feeds.

See the industry page for consumer goods & manufacturing

Retail

Keep many source systems feeding one commerce view

One bad mapping should not corrupt a week of sales data.

See the industry page for retail

Higher education & research

Keep student and aid records audit-ready

One missing record in an audit is a compliance event.

See the industry page for higher education & research

Results customers have Experienced

Ad optimization and marketing analytics company

99.9%

Data model quality, with automated validation across 10+ sources and 50 TB a day.

Leading research university

95% DQ score

Collibra-integrated validation of student information system data.

National P&C insurer

90–120 to 5–10 min

Reconciliation per segment across 15 lines of business, with audit-ready evidence (ETL Validator).

Results are from Datagaps customer engagements and vary by environment.

How does DataOps Data Quality compare with other alternatives?

Use these 15 factors as your evaluation checklist. We scored enterprise suites, observability tools and platform-native checks alongside Datagaps.

Factor

Enterprise data quality suites

Observability pure-plays

Platform-native data quality

DataOps Data Quality

Price advantage

Time to value

AI-drafted rules running in hours, not weeks.

Governance suite breadth

Built-in catalog capability, synced to Collibra, Purview and Unity Catalog.

Probabilistic matching and MDM

Record matching finds duplicates and builds a single view.

Packaged content and reference data

Prebuilt rules and industry rule packs, ready out of the box.

ML anomaly, freshness and volume checks

Machine learning flags unusual freshness, volume and trends.

Rule discovery and AI automation

Unstructured data quality

Remediation inside the platform

Lineage-driven impact and root cause

AI-agent access (MCP)

Cross-source reconciliation

Coverage through the BI layer

Synthetic and test data

Trust Score, pipeline to report

Want to test these scores against your own stack?

See DataOps Data Quality on your data

A 30-minute demo built around your sources and rules, or a free 14-day trial on your own data.

SOC 2 Type II certified | ISO 27001 certified | No credit card required for trial | Your data never leaves your environment

FAQ’s about AI powered data quality and observability

Common questions from Enterprise Buyers

What is AI powered data quality monitoring?

It uses machine learning and an embedded language model to draft rules, catch drift and anomalies, trace root causes and score data continuously, so the data feeding your AI and reports can be trusted. DataOps Data Quality also reconciles source to target and validates through to BI reports. Start with the data quality and observability guide.

How does DataOps Data Quality automate data quality checks?

It connects to your sources, profiles the data, drafts rules with AI, and runs them on a schedule or after each pipeline. Scores roll up by column, table and domain, and issues go to owners. Not sure where to begin? Try the Data Quality Maturity Assessment.

How is DataOps Data Quality different from other data quality and observability tools?

Many tools stop at the warehouse and leave teams with alerts but no root cause. Datagaps adds root cause analysis, reconciliation and BI-layer validation alongside quality, observability and asset completeness, so data is proven before it reaches AI, in one DataOps Suite. Read The Case of End-To-End Data Validation.

How is data quality different from data observability?

Data observability watches table health: freshness, volume, schema and row-count drift. Data quality tests business rules and keeps failing rows. DataOps Data Quality includes both, built in from the first load, so one scorecard covers detection and proof. Read the complete guide.

Does it validate data in BI reports and dashboards?

Yes. Validation continues through the BI layer, so reports and dashboards are checked as well as tables. BI Validator covers Power BI, Tableau and Oracle Analytics. See the pharma Tableau validation case study.

What are self-healing rules?

Self-healing rules adapt automatically as your data changes, so checks need no constant rewriting. The AI rule wizard drafts rules from metadata and profiles for your team to approve. Read about AI-powered data quality assessment.

Which data sources does it connect to?

200+ connectors cover applications such as SAP and Salesforce, databases, mainframes, files and APIs, including Snowflake, Databricks and Azure Synapse. Any JDBC or ODBC source connects. Scores sync to catalogs, including Collibra.

Does my data leave my environment?

No. DataOps Data Quality runs in your cloud, on-premise or hybrid environment, and its embedded language model is hosted there, so no data reaches external AI services. The DataOps Suite is SOC 2 Type II and ISO 27001 certified. See compliance solutions and Compliance Is a Data Problem.

What happens when a data quality check fails?

Failing records are kept, and an issue opens with its criticality and age. It goes to an owner as a task, moves through Open, Ignored and Resolved, and every exception is logged. See the data governance and data quality collaboration case study.

Does it make data AI-ready?

Yes. AI is only as good as the data and context it reads. DataOps Data Quality scores, reconciles and validates the data behind models, agents and dashboards, including documents and metadata, with an audit trail. Read Data Quality for AI Readiness and the common challenges of data quality for AI.

Can it check unstructured data and the context AI uses?

Yes. Documents, text and their metadata are checked alongside tables, and semantic validation confirms metrics and terms mean the same thing across systems.

Which industries is it built for?

It fits regulated, data-heavy teams in financial services, healthcare, life sciences, retail and higher education that must prove data before audits and reports. See the NAIC MAR and APCD compliance case studies.

How do I get started, and what does it cost?

Start a free 14-day trial on your own data, or request a demo built around your sources. Estimate savings with the ROI calculator, see pricing and plans, or read the datasheet.

Learn More About Datagaps Solutions

See how enterprises are solving complex data challenges and achieving measurable ROI

Blog

A Unified Platform for Data Quality, Observability & Reconciliation

Bad data does its damage before anyone notices.

Problems We Solve at Scale

Bad data reaches AI models and agents first

AI writes the rules. People still do the rest.

Quality looks fine, but data is missing

Hundreds of alerts, no root cause

Unstructured data goes unchecked

The context AI relies on is wrong

How Do We Solve Them Differently

Most Data Quality tools compete at detection and Monitoring. DataOps Data Quality goes further.

Enterprise data quality suites

Observability pure-plays

Platform-native data quality

Datagaps DataOps Suite

Business Outcomes we delivered

500B+

10M+

75%

65%

How does dataops data quality work, from source to Output

Connect

Profile

Define rules

Monitor

Reconcile & validate

Score & alert

Resolve & prove

Applications & sources

Pipelines

Warehouse

AI models & BI reports

Understand the data

Check it, source to AI and BI

Resolve it and prove it

How does DataOps Data Quality fit your data architecture?

Deployment

Security and compliance

Built to scale

What does DataOps Data Quality do?

Monitor and detect

AI rule wizard

Self-healing rules

Freshness, volume, schema and row-count drift

Anomaly detection

Checks data in motion

Critical and personal data thresholds

Reconcile and validate

Source-to-target reconciliation

Validation through the BI layer

Reconciliation, scored

Resolve and alert

Adaptive alerting

Root cause analysis

Failing records kept

Issues ranked and owned

Open, Ignored, Resolved

Audit trail

Make data AI-ready

Lineage with scores

Semantic validation

Unstructured data checks

Versioned data contracts

Catalog sync and Git

Which data quality problems does it solve in your industry?

Prove regulated reporting data is complete and consistent

Catch bad policy and claims data before filing deadlines

Keep patient, claims and trial records clean

Stop bad demand and plant data reaching planning

Keep many source systems feeding one commerce view

Keep student and aid records audit-ready

Results customers have Experienced

How does DataOps Data Quality compare with other alternatives?

See DataOps Data Quality on your data

FAQ’s about AI powered data quality and observability

What is AI powered data quality monitoring?

How does DataOps Data Quality automate data quality checks?

How is DataOps Data Quality different from other data quality and observability tools?

How is data quality different from data observability?

Does it validate data in BI reports and dashboards?

Most Data Quality tools compete at detection and Monitoring.
DataOps Data Quality goes further.