Platform · Quality

Quality.trustData Quality & Observability

Quality here runs where your data lives and answers the only question that matters: can this number be trusted right now? When the answer is no, an incident opens with the impact already mapped.

The product, at a glance

What Quality is made of.

6 capabilities, and the part of the mechanism this product carries. Every box opens right here, with its full story one tap deeper.

tap any box, or any circle in the mechanism, and it answers right herecontrol what changes

What changes

What changes when this is running.

Not promises of speed, but mechanisms: each of these happens because of how the product works, not because we say so.

A failure arrives with its blast radius mapped, because every check result ties to downstream reports and consumers.

Silent misses disappear, because a check that failed to run counts as a failure, never a clean status.

Unknown breaks get caught upstream, before a report consumes them, because anomaly detection watches for what nobody thought to write a rule for.

who lives hereData stewardsDataset ownersData engineersSecond line riskanswers toBCBS 239

The product

Measure. Validate. Improve. Trust.

Enterprise-grade data quality at scale, running where your data already lives. So you can trust the numbers, meet the regulatory obligation, reduce risk and move faster.

Trust and Scale

Business outcomes

Higher data trust

Fewer regulatory findings

Improved decision accuracy and confidence

Lower cost of poor data quality

Faster issue resolution and remediation

Stronger risk and control effectiveness

At a glance

Data Quality continuously monitors, validates and improves data across the enterprise, using rules, profiling, reconciliation, observability and AI. It runs against your systems rather than copying them.

Profile

Understand your data with deep profiling, patterns and statistics.

Rule

Define quality rules with no code or with code, in business or technical terms.

Validate

Execute and schedule rules across batch, streaming and real time.

Monitor

Continuously monitor pipelines, quality trends and health.

Investigate

Triage issues, perform root cause analysis and track to resolution.

Remediate

Manage issues and workflows, and correct the data at its source.

Prove

Generate evidence, attestations and audit-ready reports.

Trigger events

When an institution reaches for this.

A regulatory return rejected for a completeness failure nobody saw comingTwo systems reporting a different total for the same balanceA quality rule that exists in a spreadsheet and runs nowhereAn issue found by the business rather than by a controlA migration where nobody can say whether the data arrived intact

What it does

Seven stages from profiling data nobody has looked at closely to certifying it for a filing.

01 · Discover and profileDiscover assets and profile structure, quality patterns, anomalies and relationships.

Column profiling
Data distributions
Pattern and anomaly detection
Relationship discovery

Hover, click or use the arrow keys to walk the lifecycle.

The quality observability dashboard

What the product produces: one score, the dimensions that make it, how it has moved, the rules failing most today, and the assets an issue lands on hardest. Every figure is computed from rule executions, which is why it can be argued with.

of 100 · Strong

Trust by business process

Hover a dimension to see what it measures, or a cell to read the process behind it.

Trust over the last 6 periods

Overall
Accuracy
Timeliness

Hover the trend to read all series at one period. A headline score can hold steady while what is under it falls, which is why three lines are drawn and not one.

Rules failing most today

Customer name not null12,431
Account currency valid8,213
Balance is not negative6,982
Effective date not in the future4,521
Reference code valid3,908

Top impacted assets, by impact score

Available deposit balance72
Customer master58
Loan exposure balance44
Counterparty master31
Securities position19

Strong · 80 to 100Moderate · 60 to 79Weak · 0 to 59

Open issues by severity

Critical 12%High 28%Medium 38%Low 22%

An illustrative dashboard for ABC Bank, the site's standing fictional institution. The shape is real; the numbers in your estate are your own.

What a rule actually looks like

Rules are declarative and graded, not buried in a pipeline somebody has to read. The logic is the rule, the threshold is the bar, and the severity decides what happens when it is missed.

RuleTypeLogicThresholdSeverity

Balance non-negative

type Validation · logic BALANCE >= 0 · threshold 100% · severity Critical

Five rules from the library, written the way a steward writes them. The library ships with more than four hundred.

One reconciliation, four systems

The same balance, totalled in four places. Three agree. The teaching is the one that does not, and the whole point of running this continuously is that you find it here rather than in a filing.

ReconciliationSource totalTarget totalDifferenceVarianceStatus

Treasury systemfail

source total 7,832,110,558 · target total 7,832,105,231 · difference -5,327 · variance -0.07%

An illustrative reconciliation for ABC Bank. A variance of seven hundredths of a per cent is small enough to survive a spreadsheet review and large enough to matter in a regulatory return, which is exactly the band automated reconciliation exists to catch.

One quality issue, from failure to evidence

What happens after a rule fails. Each link names who acts, and the colour says whether it is the platform or a person: most of this is machine work, and the judgement calls are exactly where a person is named.

01 · Rule failureSystemPlatform

A rule fails where the data lives, during a run nobody had to start.

A chain is a conjunction. Take one link out and see what is left.

Platform
AI-assisted
A person decides

The chain ends at evidence, not at closure. An issue that was fixed but cannot be shown to have been fixed is an issue that will be raised again at the next exam.

Key capabilities

Data profiling

Column profiling
Data distributions
Pattern detection
Anomaly detection
Outlier identification
Correlation analysis
Data completeness
Profiling at scale

Rule studio

Rule library, 400 and more
No-code rule builder
SQL, Python and regex
Parameterised rules
Rule reusability
Custom functions
Thresholds and ranges
AI rule suggestions

Validation engine

Batch validation
Real-time validation
Streaming validation
Cross-column rules
Multi-table rules
Referential integrity
Conditional rules
Rule versioning

Quality monitoring

Data quality scores
Dimension scorecards
Trend analysis
Freshness monitoring
Volume monitoring
Threshold alerts
SLA and SLO monitoring
Data health dashboard

Issue management

Issue triage and routing
Severity and impact
Root cause analysis
Business impact tagging
Comments and collaboration
Attachments and evidence
Escalation workflows
Issue tracking

Remediation workflows

Workflow orchestration
Approvals and tasks
Auto-remediation
Data correction
Rule adjustment
Source system fix
Re-validation
Closure and verification

Evidence and reporting

Data quality reports
Scorecards and KPIs
Rule execution logs
Issue reports
Audit evidence pack
Attestations and sign-offs
Regulatory libraries
Export: PDF, Excel, XBRL

Where the platform proposes

AI-powered, and every one of them a recommendation rather than a decision.

Suggest rules from what profiling actually found

Detect anomalies and outliers without a rule being written first

Rank issues by predicted business impact

Narrow a failure to a root cause using lineage

Recommend a threshold from the observed distribution

Draft the evidence pack for a filing

Manual and AI-assisted

The same work, split. Everything on the left is something a person does; everything on the right is something the platform proposes, with evidence, for a person to accept. Nothing crosses the line on its own.

Manual

A person does this.

Decide which data is critical enough to rule
Set the threshold and the severity
Triage an issue and decide whether it matters
Fix the data at its source
Close the issue, and be recorded closing it
Certify quality for a filing

AI-assisted

The platform proposes. A person accepts.

Profile structure, patterns and distributions
Suggest rules and thresholds from what was found
Detect anomalies and outliers continuously
Narrow a failure to its root cause
Rank issues by predicted impact
Assemble the evidence

Common use cases

Where institutions start

Data completeness and accuracy monitoring
Data reconciliation across ledger, balances and positions
Reference data and code validation
Regulatory data quality: BCBS 239, FR Y-14, LCR
Data migration quality assurance
Master data quality
AI and ML data quality and feature monitoring
Pipeline and data observability

What it connects to

Warehouses and lakes

Snowflake, Databricks, BigQuery
Teradata, Redshift, SQL Server, Oracle
S3, ADLS, GCS, HDFS

Movement

Kafka, Kinesis, Event Hub
dbt, Informatica, DataStage
Spark, Talend

Operational systems

Core banking and ERP
Treasury and risk
CRM

Consumption

Power BI, Tableau, Looker
Excel
REST APIs, JDBC and ODBC
Files and logs

End-to-end workflow

01Profile
02Define rules
03Validate
04Score
05Monitor
06Triage
07Root cause
08Remediate
09Re-validate
10Certify

What gets measured

Against your own baseline.

Overall data quality scoreCritical data assets monitoredRules passedQuality rules executedMean time to detect an issueMean time to resolve an issueCost of poor data quality avoided

Who it is for

Data stewards and ownersData engineers and architectsBusiness analystsDataOps and platform teamsRisk, compliance and auditRegulatory reporting teamsAI, ML and analytics teams

Integrations

OntaVera CatalogOntaVera GovernanceOntaVera LineageOntaVera PrivacyOntaVera Reference DataOntaVera RegulatoryOntaVera Data ProductsOntaVera Trust IntelligenceSnowflake, Databricks, BigQueryKafka, Kinesis, Event Hubdbt, Informatica, DataStageCore banking, ERP, treasuryPower BI, Tableau, LookerREST APIs, JDBC, ODBC

Built on the OntaVera platform

Shared by every product in the suite.

Financial ontology and knowledge graph

Metadata fabric and connectors

Policy and rules engine

Workflow and approvals

AI and ML services for rule recommendation and anomaly detection

Audit and evidence, immutable

APIs and events: REST, GraphQL

Why OntaVera

AI-powered rule authoring, profiling and anomaly detection
Real-time, continuous quality and observability at scale
Built-in lineage, impact analysis and root cause identification
End-to-end workflows with evidence and an audit trail
Out-of-the-box rules for financial and regulatory use cases
Enterprise-grade performance, security and governance

Trusted data.Measurable quality.Stronger controls.Better decisions.

Try it yourself

See Quality do real work.

setting the stage…

Inside Quality6 sub-modules

Quality rules belong in the hands of the people who know the data. In rule studio your stewards write rules in plain language or SQL, profile the data first so the rule targets how it actually behaves, and simulate against history before anything runs live. Deployment is maker-checker, enforced by the platform, and the approval is sealed on the ledger.

What you get

Plain-language authoring compiles to SQL you can inspect, so stewards write the rule and engineers can still read exactly what will run.

Profiling comes before writing. You see null rates, ranges, and value distributions on the actual columns, so rules aim at real behavior instead of assumptions.

Simulation against recent history shows which rows would have failed and how often, so you tune thresholds before the first alert instead of after the hundredth.

Maker-checker approval runs through the one decision door. Nobody deploys their own rule, and the approval is sealed on the hash-chained ledger.

Rules are bound to datasets and CDEs on the knowledge graph, so a rule and its results appear in the lineage of every report the field feeds.

Checks run where your data lives through the platform's connectors. Only metadata and results come back, and nothing is copied out.

How it works

Profile

The studio profiles the dataset first, so you see nulls, ranges, and distributions before you write a line.

Write

You express the rule in plain language or SQL. Either way, the compiled check is readable and reviewable.

Simulate

You run the rule against history, see exactly what it would have flagged, and adjust before it goes live.

Approve

A second person reviews and approves through the decision door, and the deployment is sealed on the ledger.

Questions buyers ask

Asked about Quality

5 questions

Your existing checks tell you a job passed; this module ties every result to the downstream reports and consumers, so a failure arrives with its blast radius already mapped. Every result is sealed on a hash-chained ledger, and a check that failed to run counts as a failure rather than a silent green. That is the difference between running checks and being able to prove quality.

Read access through the platform's connectors is the main requirement, because checks run where your data lives and only metadata and results come back. Nothing is copied out, and monitoring baselines are learned from each dataset's own history, so you are not writing rules on day one.

How checks run in place →

Stewards carry most of the daily work: they author rules in plain language, work the incident queue, and confirm or dismiss anomaly flags. Engineers are not cut out, because the compiled SQL behind every rule is there for them to inspect. Incidents route to named dataset owners, and second line watches the same queue instead of chasing status by email.

The examiner gets a sealed record rather than a reconstruction. Check results and incident timelines are sealed on the hash-chained ledger, SLO attainment is computed from those sealed results, and scorecard exports keep pointers back to them, so a board number can be walked back to the check behind it. Report-level views show BCBS 239 posture for each regulatory report in one place, current as of the last check.

Dashboards & scorecards →

It proposes and people decide. Anomaly detection flags outliers as proposed findings with evidence attached, and plain-language authoring compiles to SQL a person can read, but a flag becomes an incident only after steward confirmation and no rule deploys without maker-checker approval. Nothing here lets a model close an incident or edit a quality number.

Anomaly detection →

Stronger together

One shared record, so products compound.

Every product you add makes this one smarter, because they read and write the same governed record. Start where it hurts most; nothing you adopt later starts from zero.

Quality + Lineage

Add lineage and a failed check names every report and consumer in its blast radius.

Quality + Regulatory

Add evidence and sealed check results flow straight into the packs your examiners and auditors read.

Getting started

Start small, and prove it on your own systems.

Most banks start with one priority report and five source systems, live in 90 days, then bring in more of the platform behind exit gates they approve. Quality is part of that first conversation.

Keep exploring

← Lineageavailable now Privacy →available now The engines underneatharchitecture How decisions workframework

The next step

Start with proof, not a promise.

There are three ways in, and each one earns the next. You approve the exit criteria before we spend any more of your time or budget.

A 60-minute working session

Bring one report that is expensive to prove. We walk the mechanism against it. Nothing is required from your side.

A two-week guided sandbox

Your team works hands-on against a synthetic sample bank. It requires zero access to your systems and zero procurement.

A six-week proof on your systems

We use read-only metadata across five systems and one priority report, scored against criteria both sides sign first. The decision it produces is yours.

Book a working session See the adoption path