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DataOps Suite
The complete platform
Platform Overview
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DataOps Suite

Data Quality and Agentic AI platform that validates, reconciles, scores, monitors and remediates data from ingestion to BI and AI.

Why one platform

Agentic AI-powered test and rule creation
Agents generate data quality rules, validation tests and SQL from prompts, with your approval.
Data quality score per dataset
Shows the current score, what changed since the last run and the failing records behind it.
Validation and observability inside the pipeline
Every load is compared with its source, transformations are tested against business logic, and freshness, volume and schema changes are monitored.
Reports that match the source
Dashboard figures are compared with the source data behind them before users see them.
Test data without real records
Synthetic data that mirrors production replaces sensitive records in test environments.
Quality gates for releases
Pipeline and report changes are tested before they go to production.
Explore DataOps Suite Platform

Data Quality Monitor

Continuously profiles, validates and scores enterprise data, with Agentic AI that recommends rules and flags anomalies, so analytics and AI run on trusted data.

Capabilities

AI-recommended DQ rules and scoring
Agents suggest completeness, validity and uniqueness rules, with scores down to the column.
Data profiling
Analyzes distributions, patterns and frequencies to surface outliers.
AI-based observability
Agents monitor pipelines end to end, detect anomalies and suggest fixes.
Data catalog
Organizes models and tables in one catalog, with AI-drafted descriptions.
Data lineage
Traces data from origin to destination through every transformation.
Data quality issue tracking
Assigns every failing record to an owner and tracks it to resolution.
Explore Data Quality Monitor

ETL Validator

Automates ETL/ELT testing, with Agentic AI that builds tests from mapping docs, confirming every record arrives complete, accurate and correctly transformed.

Capabilities

AI-generated ETL tests
Turns mapping docs into checks for joins, transforms and edge cases.
Source-to-target validation
Matches loaded data to the source, down to record counts and required fields.
Transformation validation
Verifies business logic with SQL, Python or Scala.
Migration validation
Checks schema, row counts and integrity for cloud migrations.
Metadata change audit
Catches schema and metadata discrepancies before they break pipelines.
Root cause and adaptive tests
Pinpoints where data broke, and agents update tests when mappings change.
Explore ETL Validator

BI Validator

Automates testing and governance for Power BI, Tableau and Oracle Analytics, with Agentic AI that builds tests from plain English and keeps reports accurate and reliable.

Capabilities

AI-created BI tests
Describe a test scenario in plain English, and agents build it.
Visual-to-source comparison
Compares the data behind every visual to the source using auto-generated SQL.
Regression and upgrade checks
Pixel-level comparisons across environments, with AI summaries of what changed.
Measure logic checks
Converts BI measures and calculations to SQL to confirm KPI logic.
Performance and load testing
Simulates concurrent users and measures load and refresh times.
BI governance
Finds unused measures, duplicate reports and orphaned tables.
Explore BI Validator

Test Data Manager

Uses Agentic AI to detect and mask sensitive data and generate synthetic data that mirrors production, so teams can test without exposing real records.

Capabilities

AI synthetic data generation
Learns patterns from a small sample to create production-like data.
PII discovery and masking
Detects PII and PHI and replaces it with realistic fake values.
Relationship-aware data
Keeps linked tables connected with referential integrity.
Millions of records in minutes
Scales for load, stress and performance testing.
Multi-format output
CSV, Parquet or JSON, or loaded straight into test databases.
Compliant by design
Cloud or on-prem, supporting GDPR and HIPAA requirements.
Explore Test Data Manager
Data Quality Use Case
The problem to solve
ROI calculator
Estimate the time and cost you’ll save.
Financial services
SOX, BCBS 239 and NAIC MAR

Banking, Financial Services, and Insurance (BFSI)

Stop reporting errors before examiners find them, with every number traced from core system to board dashboard.

How Datagaps helps

Core platform migrations
Cut over banking and insurance platforms, including mainframe to cloud, without losing a record.
Regulatory reporting
Hold submissions until the data meets quality thresholds.
Financial reconciliation
Match GL to subledger automatically, with every break flagged.
Risk and credit data
Catch drift before risk, lending and pricing models degrade.
Dashboard attestation
Prove capital and board dashboards match the warehouse.
See How it Works

Healthcare

Keep claims and clinical data accurate through every migration, without exposing patient records in testing.

How Datagaps helps

EHR migrations
Confirm every record survives the move to Epic, Cerner or Oracle Health.
Claims data quality
Catch errors in claims and remittance feeds as they arrive.
APCD submissions
Check every file against state templates before you submit.
Clinical dashboards
Catch KPI and report changes after every refresh.
Provider directories
Keep provider listings accurate, current and audit-ready.
See How it Works

Life Sciences

Catch issues in clinical and real-world data before submission, and keep trial subject data out of test environments.

How Datagaps helps

Clinical data migrations
Move EDC, CTMS and LIMS data to the cloud without losses.
CRM migrations
Keep rebate calculations right through Veeva and Salesforce moves.
Pharmacovigilance data
Catch adverse event data issues before regulatory submission.
Commercial and clinical dashboards
Trace every KPI back to source clinical and lab records.
De-identified trial data
Keep subject data out of test, analytics and AI environments.
See How it Works

Manufacturing

Avoid broken ERP cutovers and supplier overpayments, and catch sensor drift before it hits production.

How Datagaps helps

ERP migrations
Move BOM, inventory and financial data across Oracle, SAP and cloud without breaks.
Supplier reconciliation
Catch overpayments and duplicate invoices with three-way PO, ASN and invoice matching.
Sensor data quality
Catch drift before it skews predictive maintenance and yield models.
Operations dashboards
Catch report breaks after every release or upgrade.
Safe ERP test data
Keep supplier pricing and production volumes out of lower environments.
See How it Works

Retail and Utilities

Catch settlement, inventory and pricing errors before your customers or regulators do.

How Datagaps helps

Settlement reconciliation
Match marketplace payout files to the general ledger, record by record.
Inventory accuracy
Keep POS, ERP and warehouse numbers in sync.
Pricing and promotions
Catch price and promotion mismatches across channels.
Reliability dashboards
Keep SAIDI and SAIFI reliability reporting accurate.
BI under load
Make sure reports hold up for thousands of concurrent users.
See How it Works

Higher Education & Research

Get enrollment, financial-aid and transcript data right before SIS cutover, before students feel it.

How Datagaps helps

SIS migrations
Validate moves from Banner or PeopleSoft to Workday Student or Ellucian.
Financial-aid reconciliation
Match aid and Bursar data to the general ledger.
Enrollment reporting
Keep census and admissions dashboards consistent from cycle to cycle.
Federal reporting
Monitor data quality ahead of IPEDS submissions.
Safe student data
Test and train AI without exposing real student records.
See How it Works

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A practitioner summit focused on the validation, observability, governance, and reconciliation practices that determine whether enterprise data is ready for AI.

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Nov 19th, 2026
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Technology Partners

Datagaps works natively with the cloud, data and BI platforms your teams already run.

Our technology partners

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SI Partners

Consulting and integration firms that deliver Datagaps validation for enterprise clients worldwide.

Our SI partners

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+6 more partners
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Solutions

Use cases

Data migration testing
Proves data moved intact from legacy and on-prem systems to new platforms through cutover.
Data reconciliation
Finds missing, mismatched, orphan and duplicate rows between source and target.
Data observability
Tracks freshness, volume and schema changes across pipelines, with AI agents tracing root causes.
Data remediation
Routes failing records to owners, suggests fixes and tracks each one to resolution.
Data Compliance
Audit-ready evidence for SOX, HIPAA, GDPR, CCPA, FERPA, NAIC and MAR.
AI Ready Data
Validates the data feeding ML models and catches drift before retraining.

Cloud platforms

Snowflake
Validates and scores data inside Snowflake with SQL pushdown, catching schema drift and migration gaps.
Databricks
Validates Delta Lake tables from Bronze to Gold inside notebooks, with results in Unity Catalog.
Microsoft Fabric
Validates Fabric data from OneLake ingestion through Lakehouse and Warehouse to Power BI semantic models.
Azure Synapse
Validates Synapse migrations and Medallion-layer transformations, with tests running in Azure DevOps.
Salesforce
Checks Salesforce objects, uploads and updates for completeness and quality on every migration and ingest.

BI and analytics

Power BI
Matches visuals to source data and tests DAX, slicers and row-level security from Dev to Prod.
Tableau
Matches dashboards to source tables, checks calculated fields and row-level security, and catches drift across versions.
Oracle Analytics
Matches analyses to SQL results, tests subject areas and permissions, and validates OBIEE to OAC migrations.

ROI calculator →

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Home Industries Retail & Utilities

Catch the Errors Before Your
Customers or Your Regulators Do.

From marketplace settlement and price accuracy across every channel to BI reports that hold up under thousands of concurrent users,
Datagaps validates the systems retail and utility teams can’t afford to get wrong in production.

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The Challenges

Five Business Risks Every Retail & Utilities Team Faces

What weak data validation costs retail and utilities — in settlement errors, compliance exposure, and customer trust

Marketplace Settlement & Payment Reconciliation for Retail & Utilities

Marketplace Settlement & Payment Reconciliation

What's the cost of settlement discrepancies that hide until quarter-end?

PCI DSS requires full transaction lineage from POS to settlement — and failed reconciliation triggers mandatory audits. When marketplace payouts are matched against the GL by hand, discrepancies hide until quarter-end.

$100K+

per incident in PCI DSS breach penalties, plus card network fines and potential loss of processing ability

Omnichannel Inventory & Pricing Accuracy For Retail & Utilities

Omnichannel Inventory & Pricing Accuracy

What's the cost when POS, ERP, and warehouse each hold a different version of truth?

POS, ERP, and warehouse systems each hold their own version of inventory and pricing truth. When they drift apart, it shows up as stockouts customers see and pricing mismatches that leak revenue across channels.

23.1% CAGR

Retail is the fastest-growing Data Quality Tools vertical (Mordor Intelligence)

Consumer Privacy & Consent Compliance Retail & Utilities

Consumer Privacy & Consent Compliance

What's the cost of a deletion request you can't fully honor — or consent data that's stale across systems?

CCPA/CPRA, GDPR, and state privacy laws require auditable consent management and timely deletion across every system holding consumer data. When consent state doesn't sync across platforms, every gap is a compliance exposure.

€20M

maximum EU GVP penalty per violation; US FDA can issue Warning Letters and import bans for PV failures

Utility BI Report Performance & Concurrency Risk at Scale For Retail & Utilities

Utility BI Report Performance & Concurrency Risk at Scale

What's the cost of a billing or outage dashboard that falls over under peak concurrent load — discovered after go-live?

A report that runs fine for one analyst can collapse under a thousand concurrent customer-service, field, or executive users. One utility account spent months hand-validating 220 reports to find out. Most teams only discover the problem after go-live, when the rollout is already slow.

10-15

testing resources over several months to manually validate 220 reports (real utility account discovery data)

NERC CIP Audit Evidence & Asset-Categorization Gaps Retail & Utilities

NERC CIP Audit Evidence & Asset-Categorization Gaps

What's the cost of an asset-categorization gap you don't catch until NERC does?

NERC CIP compliance rests on an accurate Bulk Electric System asset inventory — every downstream control traces back to it. When categorization and change evidence are reconstructed by hand across IT and OT, the gaps show up as enforcement findings, not internal audit catches.

$10M

NERC CIP penalty for 127 violations, of which only 13 were caught during an audit — the remaining 114 were self-reported (per NERC's 2019 enforcement notice, via Forescout)

The Platform

How DataOps Suite Solves It

Each product maps directly to a Retail & Utilities data problem.

ETL Validator for Industry icon

ETL Validator

Solves: Marketplace settlement reconciliation, omnichannel inventory reconciliation & fragmented multi-pipeline validation

Reconcile marketplace payout files and POS/ERP/warehouse inventory counts against the general ledger and each other — plus native source-to-target validation across batch, streaming, and microservices pipelines, so testing scales with your pipeline count instead of falling behind it

Marketplace Payout Reconciliation
Omnichannel Inventory
Batch + Streaming + Microservices
AMI/CIS Reconciliation
BI Validator for Industry Icon

BI Validator

Solves: Cross-channel pricing accuracy & BI report trust at scale

Pricing mismatches across channels show up as direct revenue leakage, and reports that fall over during peak events erode stakeholder trust overnight. Validates both accuracy and performance before your customers find the breaking point.

Peak-Event Readiness
Pricing & Promotion Accuracy
Power BI, Tableau, Looker, OAS
SAIDI/SAIFI Dashboard Validation
Data Quality Monitor for Industry Icon

Data Quality Monitor

Solves: Consumer data quality, consent compliance & unified DQ monitoring across the estate

A six-dimension rule engine with AI-assisted rule inference from your data dictionaries — plus one SLA/SLO view across every source, replacing the fragmented, per-pipeline checks most teams stitch together manually.

Six-Dimension Rule Enginee
AI-Assisted Rule Inference
Unified SLA/SLO View
GIS/OMS Asset Monitoring
Test Data Manager for Industry icon

Test Data Manager

Solves: Realistic checkout/payment test data without exposing real cardholder data

Generate realistic, referentially-intact synthetic checkout and payment data for QA — deterministic, reversible masking that satisfies PCI-DSS's documented retention and disposal requirements instead of working around them.

PCI-DSS-Aligned Masking
Referential Integrity
Utility Usage Data Masking
Multi-Format Output
DataOps Suite for Industry Icon

DataOps Suite

Solves: No CI/CD gate stopping a bad data change before it reaches production

AI proposes reconciliation rules and quality checks from mapping docs and SQL — your team approves before anything runs. Trigger validation directly from your CI/CD pipeline and block or revert a merge on reconciliation or quality failure, with one lineage graph and audit trail across every channel and system.

Cutover Assurance
Full Lineage 
Human-in-the-Loop AI
NERC CIP Audit Trail

Measurable Impact

Impact Across Retail & Utilities Teams

50–70%

Faster Issue Resolution

Data quality issues caught and resolved in hours, not days — across vendor feeds, settlement files, and pipeline handoffs.

99.9%

Data Model Accuracy

Automated validation across every source, transformation, and reporting layer — replacing manual spot-checks with continuous, rule-based assurance.

75%

Reduction in Data Assurance Team Size

One resource operating what previously required a full team — reusable templates and automated monitoring replace manual, per-pipeline scripts.

2,000+

Concurrent Users Stress-Tested

BI reports validated under peak concurrent-user load before rollout — so the breaking point is found in testing, not in production.

Use Cases

Retail & Utilities Use Cases by Product

Marketplace Settlement Reconciliation

Reconcile marketplace and payment-platform payout files — including fee and deduction schedules — against the general ledger with full PCI DSS transaction lineage.

Omnichannel Inventory & Pricing Reconciliation

Source-to-target validation across POS, ERP, and warehouse systems. Identifies inventory drift and pricing mismatches across channels before they reach customers.

Cross-Channel Price & Promotion Validation

Visual regression confirms pricing and promotions display and calculate correctly across web, app, and in-store. Catches mismatches that show up as direct revenue leakage.

Concurrent-User BI Stress Testing

Simulate thousands of concurrent users against Power BI, Tableau, or Looker reports before rollout. Find the breaking point before your business users do.

SAIDI/SAIFI Reliability-Metrics Dashboard Validation

Cell-level regression testing across reliability-metric dashboards and PUC-facing reports — catching broken formulas, drift, and stale refreshes before a misstated SAIDI/SAIFI number ships.

IoT/Sensor & GIS Data Quality Monitoring

Zero-code AI-generated rules catch GIS connectivity errors, orphaned assets, and phase-assignment mistakes before they degrade outage prediction — one utility cut a 16-hour manual GIS audit to 15 minutes.

Vendor Feed & Multi-Source Data Quality

Six-dimension quality monitoring across 10+ vendor feeds and source systems. Automated anomaly detection with AI-assisted rule inference from data dictionaries.

PCI-DSS-Safe Synthetic Checkout Data

Generate realistic, referentially-intact synthetic checkout and payment data for QA. Deterministic, reversible masking satisfies PCI DSS retention and disposal requirements.

Synthetic Customer & Loyalty Data

Realistic synthetic datasets for loyalty, CRM, and personalization testing — without exposing real customer PII in development environments. CCPA/GDPR-aligned.

Privacy-Safe Customer Usage & Billing Test Data

Generate synthetic, referentially-intact customer, meter, and billing test data for CIS/MDM/billing testing — without exposing real usage data across the fragmented 50-state privacy patchwork.

CI/CD-Gated Data Validation

Trigger validation directly from your CI/CD pipeline. Block or revert a merge on reconciliation or quality failure — with one lineage graph and audit trail across every channel and system.

Multi-Pipeline Validation (Batch + Streaming + Microservices)

One platform covering batch ETL, streaming/CDC, and microservices pipelines — so testing scales with your pipeline count instead of falling behind it.

NERC CIP Compliance Evidence & Audit Trail

A persistent, queryable evidence layer spanning ERP, CIS, EAM, and OT sources feeding NERC CIP audits — built for the self-reporting-driven compliance culture NERC's enforcement record favors.

Proof Points

Customer Evidence in Retail & Utilities Services

Retail Analytics Company

Driving Retail Success Through Analytics Consolidation

Consolidated 5-6 fragmented BI tools (Power BI, Tableau, Looker, Sisense, Qlik) down to 2-3 core platforms. Refactored 80-90 reports to eliminate duplication. 50% more productive development time, 50%+ cost savings, and executives who finally trust the numbers.

Read Case Study
Ad Optimization & Marketing Analytics Company

Transforming Data Quality for Marketing Analytics

Automated validation across 50TB daily from 10+ diverse sources. Data model quality improved to 99.9%, data assurance team reduced by 75%, and time to resolve issues cut by 50-70%.

Read Case Study
Leading French Personal Care Company

Automated Testing and 60% Migration Time Reduction

Automated ETL validation for data migration to Cassandra and Hive. 45-60% reduction in migration testing time, 30% decrease in TCO, and 100% automated testing coverage for all migrated data.

Read Case Study
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Our team will run a live demo against your specific use case your stack, your data problem, your pipeline.

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FAQs: Retail & Utilities

 Common questions from Enterprise Buyers

Can Datagaps reconcile marketplace payouts against the general ledger?

Yes — ETL Validator runs record-level, non-sampled reconciliation of marketplace and payment-platform payout files — including fee and deduction schedules — against your GL, with full PCI DSS transaction lineage so every discrepancy traces back to its source. See Data Reconciliation for the underlying matching logic.

Does Datagaps validate pricing and promotions across web, app, and in-store channels?

Yes — BI Validator runs visual regression and pixel-to-pixel comparison across every channel to confirm pricing and promotions display and calculate correctly, catching cross-channel mismatches before they leak revenue. Learn more about cross-platform validation in our BI Testing guide.

Can Datagaps load-test Power BI reports for concurrent-user performance before a rollout?

Yes — BI Validator simulates thousands of concurrent users against Power BI, Tableau, or Looker reports before go-live, so the breaking point is found in testing, not after rollout. Our Power BI Testing Automation page covers filter, slicer, and RLS regression too.

Does Datagaps generate PCI-DSS-safe synthetic payment data for testing?

Yes. Test Data Manager generates realistic, referentially-intact synthetic checkout and payment data using deterministic, reversible masking that satisfies PCI DSS’s retention and disposal requirements — so QA teams never need real cardholder data in test environments.

Can Datagaps validate data across batch, streaming, and microservices pipelines from one platform?

Yes — ETL Validator and DataOps Suite provide native source-to-target validation across batch ETL, streaming/CDC, and microservices pipelines from a single platform, so testing scales with your pipeline count instead of falling behind it. See Cloud Data Test Automation for source coverage.

Does Datagaps have experience with retail or utilities companies specifically?

Yes — from consolidating 5-6 fragmented BI tools into 2-3 platforms for a retail analytics company (50%+ cost savings) to validating 50TB of daily vendor-feed data at 99.9% accuracy for a marketing analytics company. See these and more on our Case Studies page.

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How Agentic AI Is Transforming BI Validation

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