When data isn’t reliable, the costs are high. Gartner estimates that poor data quality costs organizations an average of $12.9 million per year, excluding lost opportunities and stalled AI ambitions. As technology evolves, trusting data to support increasingly complex systems becomes essential.
To that end, we need to know when and where our data breaks, and what must be done to repair it. And we need to be able to prove our data quality, with clear evidence, to satisfy our most rigorous governance checks and regulatory audits. That’s why we built Validata.
This post explores what Validata is, the four areas where it delivers the greatest impact, and why it sets a new standard for enterprise-scale data confidence.
Validata: Continuous, Real-Time Source-to-Target Validation
Validata is Striim’s data validation and reconciliation engine, a new product built for enterprise modernization, CDC replication, AI/ML data sets, and regulated workloads.
Most enterprises lack a systematic approach to measuring and repairing data quality. Often they rely on data quality spot checks, sprawling SQL scripts, ad hoc reports, or flimsy home-built tooling that are difficult to maintain. These solutions fail to scale and often miss data drift, or catch it too late when the damage is already done.
Where these solutions fail to scale, Validata meets the challenge by turning complex processes into intuitive, user-friendly workflows. Validata makes it easy to run table-level validation across heterogeneous sources. It includes built-in scheduling, alerting, historical tracking, and reconciliation: all without overloading production systems.
Validata supports enterprise data validation in any context or environment. But it is particularly impactful in four strategic areas:
- Operational Reliability
- Data Modernization
- Regulatory Compliance & Audit Readiness
- AI/ML Data Quality Assurance
Let’s look at each of these pillars and explore how teams can restore data trust with Validata.
Operational Reliability
In large enterprises, the quality and integrity of data replicated from source databases is paramount to daily operations. Inaccuracies, silent data drift, or omissions from replicated data can all have devastating consequences for downstream systems. Maintaining trust and confidence in operational data is a must.
The Challenges of Safeguarding Reliability at Scale
- The Scale of Enterprise Data Movement: Modern data platforms run thousands of CDC and batch jobs every minute. Manual spot checks can’t keep up with the sheer volume of data that needs to be verified.
- Silent Data Drift: Validation failures are often silent and fly under the radar. Teams only discover inaccuracies when the damage is already done: when dashboards break or the customer experience is impacted.
- Infrequent Validation: Since full-table comparison for every run is slow and expensive, teams can only afford to validate occasionally, leading to gaps in observability and lower overall confidence.
- Replication False Positives: In-flight records in continuous replication are often mis-classified as mismatches, generating false positives that waste triage time from governance teams.
How Validata Enables Always-On Operational Control
Validata’s continuous validation loop lets teams move from ad hoc checks to a system for always-on control.
With recurring schedules (hourly, daily, weekly), interval-based validations on recent changes, in-flight revalidation, and real-time notifications that immediately alert engineers to any data discrepancies, Validata turns validation workflows into a governed, automated control loop embedded in day-to-day data operations.
With Continuous Reliability from Validata, Enterprises can:
- Limit outages, broken dashboards, and customer-facing issues caused by silent data problems.
- Decrease incident and firefighting costs as teams spend less time in war rooms and post-mortems.
- Ensure adherence to internal and external SLAs for data freshness and correctness.
- Gain clearer ownership of data reliability across data engineering, platform, and business teams.
- Get peace of mind for all downstream business applications and teams that they are working with trusted data.
Data Modernization
For many enterprises, realizing their ambitions with data and AI means moving to the cloud. Large scale migrations, whether like-for-like (e.g., Oracle → Oracle) or cross-engine (e.g., Oracle → PostgreSQL) are fraught with complexity and risks. Certifying data quality across a migration or modernization project requires more than a SQL script or spreadsheet. It calls for a systematic, repeatable approach that proves, not just promises, source–target parity.
The Challenges of Data Quality In Modernization
- Data Discrepancies During Cutover: Large, multi-wave migrations from on-prem databases to cloud databases carry high risk of missing, duplicated, or transformed records.
- Data Lost in Translation: Complex transformation logic (joins, aggregates, filters) can subtly change meaning, and teams often only discover issues after go-live.
- Cost Spikes from Parallel Systems: Dual-run periods are expensive. Every extra week of parallel systems, reconciliations, and rollbacks drains budget, distracts teams, and pushes back cutover-dependent migration changes.
- Unscalable, Ad Hoc Solutions: Most organizations stitch together SQL scripts, spreadsheets, and one-off checks to “certify” migrations, which doesn’t scale across domains and programs.
How Validata Upholds Data Trust through Modernization
Replacing unstandardized validation frameworks that are complex to manage and impossible to scale, Validata offers a productized way to certify source-target equivalence before cutover. Through vector validation for high-speed checks, full-and fast-record validation to confirm row-level parity, and key validation to highlight whether every critical ID in the source is present in the target, Validata provides comprehensive coverage.
Together with downloadable reports and repair scripts, Validata makes data validation part of the migration runbook; not just a side project.
With Certified Modernization, Enterprises can:
- Ensure fewer failed or rolled-back cutovers, avoiding downtime, revenue impact, and brand damage.
- Decrease run-rate spend on legacy infrastructure and licenses by safely decommissioning systems sooner.
- Reduce remediation and rework after go-live because issues are found and fixed earlier.
- Streamline stakeholder sign-off on migration phases, supported by clear evidence instead of anecdotal checks.
Regulatory Compliance & Audit Readiness
Regulatory authorities, particularly in Financial Services, Healthcare, and Insurance, require organizations to protect the integrity of critical data, and prove they have done so. Maintaining data quality at scale is hard enough. Collecting sufficient evidence to demonstrate data integrity, especially with painful, manual processes is harder still.
Failure to satisfy regulatory requirements can lead to audit findings, significant fines, or expanded scrutiny. Enterprises need a way to generate clear, long-term evidence, so they can provide definitive proof of compliance without fear of increased regulatory oversight or punitive action.
The Challenges of Meeting Compliance Standards
- Proving Clean, Complete Data: Regulators and auditors expect organizations to show how they ensure data completeness and integrity, especially for trades, claims, payments, and patient records.
- Record Keeping at Scale: Many teams simply cannot produce multi-year validation history, proof of completeness (e.g., key absence), or clear records of corrective actions.
- Manual, Unscalable Evidence Collection: Some enterprises rely on manual evidence collection during audits, which is slow, error-prone, and expensive.
How Validata Empowers Enterprises towards Audit-Readiness
Crucial information about validation runs within Validata isn’t lost; they’re stored in Historian or an external PostgreSQL database. Teams working with Validata maintain clear, timestamped evidence of record-level completeness (e.g., ensuring that every Customer_ID or Order_ID in the source has a corresponding record in the target), with downloadable JSON reports for audit files.
Validata leverages fast-record and interval validations to enable frequent, lightweight integrity checks on regulated datasets. Combined with reconciliation script outputs that can be attached to audit records, this approach enables teams to continuously collect evidence of repaired data quality issues, supporting their efforts towards compliance and audit readiness.
With Comprehensive Evidence of Compliance, Enterprises can:
- Demonstrate that controls around critical data are operating effectively, supporting broader risk and compliance narratives.
- More accurately predict audit cycles, with fewer surprises and remediation projects triggered by data issues.
- Free up time and people from audit preparation, so teams can focus on strategic work.
- Use reports to correct any data discrepancies to ensure adherence to regulatory and other compliances.
AI / ML Data Quality Assurance
Discrepancies in AI training and inference data are like poison in a water supply: even small flaws can cause havoc downstream. Maintaining data quality for AI/ML performance is imperative. However, modern data quality tools were mainly designed to fix errors in warehousing, reporting, and dashboards, not to support real-time AI pipelines or agentic systems.
When enterprises plan to deploy AI in production, they need assurance their data can keep up. They need a solution to match the speed, scale, and versatility of enterprise AI projects, as they evolve.
The Challenges of Delivering Trusted AI
- Model Pollution: ML models are highly sensitive to subtle data drift, missing features, and environment mismatches between training, validation, and inference datasets.
- Outdated Tooling: Standard data quality tools focus on warehouses and reporting, not on ML feature stores and model inputs.
- Lack of Observability: Diagnosing model performance issues without data quality telemetry is slow and often inconclusive.
How Validata Restores Confidence in AI Workflows
Validata is not just a verification tool for source-target parity. Teams can work with Validata to validate data across AI and other data pipelines or datasets, regardless of how the data moved between them.
Better yet, teams can transform a previously complex process into a conversational workflow. With Validata AI, users ask natural-language questions—such as “show me drift trends for my target data” or “which models had the most validation failures last quarter”—and receive guided insights and recommendations.
Ensure Data Accuracy and Trust in Your AI, with Validata
As enterprise AI moves into production, trust in data has become non-negotiable. Systems that make decisions, trigger actions, and operate at scale depend on data that is accurate, complete, and reliable, as well as the ability to prove it.
Validata sets a new standard for data trust by continuously validating data across operational, modernization, regulatory, and AI workflows. By surfacing issues early, supporting targeted repair, and preserving clear evidence over time, Validata gives enterprises confidence in the data that powers their most critical systems.
In the “buildout” era of AI, confidence starts with trusted data. Validata helps enterprises ensure data clarity, and move forward with certainty.
Start your journey toward enterprise data trust with Validata.
