Data Quality Engineering Services

Forte Group brings 25 years of quality engineering discipline to every layer of the data stack — from architecture decisions to pipeline delivery to AI model inputs. When the integrity of your data determines the integrity of your decisions, quality cannot be an afterthought.
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Data Quality Engineering

Quality is Forte Group's founding discipline — now applied to data.

Most organizations treat data quality as a cleanup task. We treat it as an architectural commitment. As one of the few technology services firms with a dedicated Chief Quality Officer, Forte Group embeds quality controls at every phase of data platform design, engineering, and delivery. The same rigor that has defined our software quality practice for over two decades now governs how we build, migrate, and operate modern data infrastructure.
The result:
A data platform your business can act on with confidence.

The cost of data quality failures

Bad data does not announce itself. It compounds silently. Faulty data architectures surface months after design decisions are locked. Migration errors reach production disguised as schema drift. AI models trained on unchecked data produce confident, incorrect outputs. By the time the problem is visible, the cost is significant — in reporting errors, failed migrations, and AI initiatives that cannot be trusted. Quality controls built into the engineering process prevent failures that audits after the fact cannot recover.

Three statistics that should concern every data leader

$12.9M

Average annual cost of poor data quality per enterprise.

83%

Of data migrations fail or exceed budget and schedule.

60%

Of AI projects will be abandoned due to data that isn't AI-ready.

Our data quality engineering services

Four domains. One quality standard.

Data Architecture Quality

A sound architecture is the first quality control. Before a single pipeline runs, architectural decisions determine whether your data platform will scale, interoperate, and produce trustworthy outputs across its lifecycle.
Forte Group's data architecture quality practices cover:
Fitness-for-purpose review
validating that proposed architectures align with actual analytical workloads, not theoretical ones.
Schema design validation
assessing data models for normalization, extensibility, and downstream compatibility before implementation.
Integration point audits
evaluating source system connectivity, API contracts, and data contract enforcement across the technology ecosystem.
Scalability and reliability assessments
stress-testing architectural assumptions against projected data volumes and SLA requirements.
Governance readiness evaluation
confirming that lineage tracking, access control, and audit trail capabilities are designed in, not retrofitted.
Architecture quality reviews are conducted independently of implementation — providing an objective baseline before commitments are made.

Data Migration Quality

Migration failures are rarely caused by the move itself. They are caused by insufficient validation of what was moved, how it was transformed, and whether it arrived intact.
The most dangerous failure mode is silent data corruption — when a migration completes "successfully," row counts match, and green lights appear across the board, but the data itself is broken. Character encoding changes, numeric precision loss, datetime conversion errors, and referential integrity failures can all pass a basic count check while corrupting the underlying data. By the time the problem surfaces in a report or a business decision, fixing it costs orders of magnitude more than catching it before cutover.
Forte Group's migration quality controls include:
Pre-migration data profiling
characterizing source data for completeness, consistency, format anomalies, and business rule compliance before extraction begins.
Transformation logic validation
verifying that every mapping, cleansing rule, and business logic translation produces the intended output, documented and testable.
Reconciliation frameworks
automated row counts, hash comparisons, and statistical sampling to confirm source-to-target parity at field, record, and aggregate levels.
Referential integrity verification
enforcing foreign key relationships and cross-entity consistency throughout staging and load phases.
Cutover readiness checklists
structured go/no-go criteria that prevent premature production cutover when data integrity thresholds are not met.
Post-migration acceptance testing
user-facing validation that business reports, dashboards, and downstream applications produce expected results against the migrated environment
Our commitment: No migration is considered complete until reconciliation confirms it.

Data Engineering & Pipeline Quality

A pipeline that delivers data on schedule while delivering incorrect data is not a functioning pipeline. Forte Group applies software quality engineering principles to data engineering work — treating pipelines as production systems that require the same testing discipline as application code.
Quality controls embedded in our pipeline engineering practice:
Data contract enforcement
defining and monitoring schema expectations, type constraints, and null tolerance between producers and consumers.
Automated data quality testing
unit tests for transformation logic, integration tests for pipeline behavior, and regression suites that run on every deployment.
Anomaly detection and alerting
statistical monitoring of data distributions, volume trends, and freshness metrics to surface pipeline degradation before it reaches consumers, with AI-powered detection that understands seasonal patterns, marketing events, and business cycles.
Idempotency validation
confirming that pipelines produce consistent results under replay conditions, a requirement for reliable incident recovery.
SLA tracking and observability
end-to-end lineage visibility with latency monitoring, failure alerting, and audit trails that satisfy both operational and compliance requirements.
Infrastructure-as-code quality reviews
applying code review standards to pipeline configuration, Terraform, and orchestration definitions to prevent environment-specific drift.

AI Data Quality

AI systems inherit the quality of the data they consume. A model cannot compensate for training data that is incomplete, biased, inconsistently labeled, or temporally stale. Forte Group applies structured quality controls at the boundary between data engineering and AI — the layer that most organizations leave unexamined.
Our AI data quality practice addresses:
Training data audits
evaluating datasets for coverage gaps, label inconsistencies, class imbalance, and representation issues that compromise model behavior.
Feature engineering validation
reviewing feature pipelines for data leakage, look-ahead bias, and distribution shift between training and production environments.
Data lineage for model governance
establishing traceable connections between model inputs and their source systems to support explainability and regulatory inquiry.
Inference pipeline monitoring
detecting drift in the statistical properties of production data relative to training baselines, triggering revalidation before model degradation affects business outputs.
Retrieval-Augmented Generation (RAG) quality controls
validating the accuracy, freshness, and relevance of documents ingested into knowledge bases that power AI assistants and decision-support tools.
Synthetic data validation
when synthetic data is used for training or testing, confirming that it preserves the statistical properties of production data without introducing artifacts.
AI that operates on trusted data is an asset. AI that operates on unvalidated data is a liability.

Why Forte

Quality embedded from the start. Not tested at the end.

Most quality problems in data engineering are architectural in origin and operational in consequence. Forte Group's approach embeds quality controls at the point of design — not as a review layer applied after the fact.
Most large systems integrators run data engineering and quality engineering as separate practices — separate teams, separate methodologies, separate go-to-market motions. When you engage them for a data project, quality is something the QE team adds at the end, if it's scoped at all. We built this differently.
What makes our approach different:
25 years of quality engineering
a founding discipline, not a recent addition to our portfolio.
Dedicated Chief Quality Officer
organizational commitment to quality standards across every service line.
QA-native engineering teams
data engineers who work alongside quality engineers as a standard delivery model, not an optional add-on.
Platform-agnostic quality frameworks
quality controls designed for the tools you use, including Databricks, Snowflake, dbt, Azure, AWS, and Google Cloud.
Independent validation capabilit
Forte Group can perform quality assessments on data platforms built by other vendors, providing objective assurance without conflict of interest.

What our clients achieve

4x Faster Critical User Flows for a Global VDR Provider

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Optimizing Healthcare Mobility App Through Scalable Test Automation

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10× Increase in Test Coverage with Scalable Test Automation

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Start with a conversation

Discover

We explore your current data landscape, the quality challenges you're facing, and where the highest-value opportunities are. No commitment required. You walk away with a clear picture of your data quality risk and what it's costing your organization.

Build

We embed quality gates, observability tooling, data contracts, and validation frameworks into your infrastructure — working alongside your existing team, inside your existing stack. No rip-and-replace. No new platform mandates.

Scale

We extend quality engineering across your full data estate — including AI/ML pipelines — with continuous monitoring, managed quality SLAs, and governance programs that give your domain teams ownership of their data quality.

FAQs

What is Data Quality Engineering?

Data Quality Engineering is the systematic application of software quality engineering principles — shift-left testing, automated validation, CI/CD integration, and observability — to data pipelines, migrations, and data-driven systems. Rather than treating data quality as a manual audit process or a final checklist, Data Quality Engineering embeds automated quality controls throughout the data lifecycle, catching issues at the source before they compound downstream.

What does the four-domain service structure cover?

Our Data Quality Engineering practice covers four domains: Data Architecture Quality (pre-implementation reviews and governance readiness), Data Migration Quality (profiling, reconciliation, and acceptance testing), Data Engineering and Pipeline Quality (contracts, automated testing, observability, and SLA tracking), and AI Data Quality (training data audits, feature validation, inference monitoring, and RAG quality controls). Organizations typically engage one domain first and expand across others as the engagement matures.

Can you assess a data platform that was built by another vendor?

Yes. Forte Group offers independent data quality validation — quality assessments conducted separately from implementation, providing objective assurance without conflict of interest. This is particularly relevant for organizations that want an independent review of a platform in flight, recently delivered, or under consideration for production cutover.

Can you help specifically with data quality for AI and machine learning pipelines?

Yes. AI and ML pipelines have requirements beyond standard analytics: training data completeness, feature distribution consistency, label quality, leakage prevention, and drift detection between training and inference data. We address these as a dedicated domain within our Data Quality Engineering practice — ensuring the data feeding your models meets the standards required for reliable, auditable AI performance.

How is Data Quality Engineering different from traditional data governance?

Traditional data governance focuses on policies, ownership structures, and compliance frameworks. Data Quality Engineering is the engineering practice that makes those policies technically enforceable — the automated tests, quality contracts, observability monitoring, and validation tooling that ensure data actually meets the standards governance has defined. They are complementary: governance defines the rules; data quality engineering enforces them.

Do you work with our existing data stack, or do we need to adopt new tools?

We work with your existing stack. Our approach is platform-agnostic: we implement quality frameworks that integrate with your current data platforms — whether that's Databricks, Snowflake, BigQuery, Azure Synapse, or a custom architecture. We recommend tooling based on what is right for your environment, not what we are partnered to sell.

How does this relate to your Quality Engineering services for software?

It is the same discipline applied to a different domain. The shift-left testing philosophy, automated validation frameworks, and observability-first mindset are core to how Forte has delivered software quality engineering for 25 years. Data Quality Engineering applies those principles to data infrastructure — treating each pipeline stage the way a software engineer treats a code module: with tests, contracts, and continuous monitoring. The engineering philosophy is identical. The tooling and implementation patterns are specific to data.

Let’s build data foundations you can actually trust.

What our experts say

The uncomfortable truth of the AI era is that your strategy is only as strong as your data engineering foundation. Without rigorous quality controls, even the most sophisticated models are built on quicksand. We help organizations transform their data from a liability into a high-fidelity strategic asset.
Lee Barnes
CQO at Forte Group

What our experts say

Data quality is the ultimate silent killer of velocity. When teams spend 60% of their time 'firefighting' bad data, innovation stops. By shifting quality checks 'left' into the engineering pipeline, we eliminate the friction that holds back enterprise-scale digital transformations.
Pavel Chechat
VP Delivery at Forte Group

What our experts say

We’ve moved beyond the era of 'black box' data pipelines. Modern DQE is about treating data as a product—complete with clear ownership, automated observability, and guaranteed uptime. When stakeholders can finally trust the dashboard, they start making faster, bolder business decisions.
Egor Goryachkin
CDO at Forte Group

What our experts say

In highly regulated industries, data integrity isn't just a technical requirement—it's a competitive moat. Implementing automated validation and clear governance frameworks doesn't just reduce risk; it provides the transparency needed to scale globally with absolute confidence.
Lucas Hendrich
CTO at Forte Group