Production-Ready ML Platform for Data Quality Management
The Proof of Concept phase successfully demonstrated that machine learning can detect data quality issues, identify stable patterns, and suggest reliable corrective values.
ML-based decision logic works.
The Proof of Concept successfully validated the core intelligence. It demonstrated that machine learning can detect data quality issues, identify stable patterns, and suggest reliable corrective values that improve over time through user feedback.
However, proving that ML logic works is not the same as operating a production system.
The PoC was intentionally focused on validating decision quality, not on building a fully operational platform. As a result, it did not address the requirements needed to run this intelligence reliably at scale.
At this stage, key production capabilities were still missing:
These limitations are not weaknesses of the PoC. They define the boundary between experimentation and industrialization.
The PoC proved that data can be corrected with the support of ML, rules, and human validation. However, true self-healing requires more than fixing individual records. It requires a system that can safely automate decisions, learn deterministically from human input, and retain clear ownership and accountability over time.
This is the role of the platform's operating layer — not to add intelligence, but to make learning, automation, and governance part of a single, continuous system.
The intelligence is proven — but it is not yet industrialized.
The platform industrializes the proven ML decision logic by adding the operational infrastructure required for production use.
Standardized workflows connect ML output with user interfaces, enabling consistent decision processes across all data domains.
Controlled processes for model training, versioning, testing, and deployment ensure quality and traceability.
Standardized patterns for issue detection, decision-making, and correction allow the same logic to apply across different datasets.
Complete decision logs and evidence trails ensure compliance, enable learning, and build trust in automated corrections.
High-confidence cases (>95%) auto-correct without human review; medium confidence suggests; low confidence escalates to experts.
Enterprise-grade security controls protect sensitive data and ensure regulatory compliance through least-privilege access and comprehensive audit trails.
Automated CI/CD pipelines and Infrastructure as Code ensure consistent, repeatable deployments with built-in quality gates and rollback capabilities.
The platform does not add new intelligence. It makes proven intelligence reliable and scalable.
The platform is not a static data quality tool. It is a decision system that combines machine learning, rules, and human expertise.
The journeys below illustrate how different types of data quality issues are handled in daily operations — from fast, automated corrections to expert-driven decisions that help the system learn and improve over time.
The most common scenario — quick validation of ML suggestions
User provides better value than ML suggested
Complex cases require specialist review
Patterns trigger systematic improvements
Proven corrections applied without human review
Requesting model improvements for specific domains
A record is flagged with a high-confidence ML suggestion. The user has context to validate whether the suggestion makes sense.
Reviews the suggested correction in context of the full record, verifies it aligns with their domain knowledge, and accepts the suggestion with one click.
This is the scalable path. Most quality issues follow known patterns, so validated suggestions let teams handle 10x more records without manual research. Acceptance feedback strengthens the model over time.
The system suggests a correction, but the user knows the correct value is different — perhaps due to recent domain knowledge or context the model hasn't learned yet.
Overrides the suggestion by entering the correct value manually. Adds a short justification explaining why the model's suggestion was incorrect.
The platform learns from expert corrections. Every override is a learning opportunity. Over time, patterns that required manual correction become automated as the model improves.
The system flags a record with low confidence, or the case involves business-critical context that requires expert judgment beyond standard validation rules.
Escalates the case to a domain expert or data steward. Provides context about why expert review is needed and flags any relevant business constraints.
Complex cases go to the right people. The platform doesn't try to automate everything — it routes decisions to experts when needed and learns from their judgment.
Recurring feedback patterns signal a systemic gap — perhaps a new product category isn't in the dictionary, or a business rule changed and the system isn't aware yet.
A data steward identifies the pattern from operational reports and requests an enhancement — update a validation rule, enrich a reference dictionary, or retrain the model with new examples.
The platform gets smarter from operational feedback. It's not static — it evolves as the business changes. Rules, dictionaries, and models all improve continuously based on real-world usage.
A pattern has been validated over time with consistent user acceptance. The system has proven it can handle this type of correction reliably, with high confidence and no overrides.
Nothing — the correction happens automatically. Users see a summary of auto-corrections in daily reports for awareness and spot-check any anomalies.
Automation scales proven intelligence safely. The platform doesn't automate blindly — it only auto-corrects patterns that have been validated in practice, and continuously monitors for any signs of drift or degradation.
A business domain shows low suggestion quality or coverage — the model isn't performing well for a specific data segment, product category, or region.
Submits a request for model improvement through the platform interface. Provides business context: which domain, what quality issues are occurring, and how important this segment is.
ML lifecycle is managed through operational feedback. Retraining decisions are driven by business impact, not guesswork. The platform connects operational users with ML teams to ensure continuous improvement.
Once the platform foundation is established, rollout to business domains becomes a repeatable process of configuration and adaptation, not system rebuilding.
Each new domain is onboarded using the same decision framework, governance model, and learning mechanisms proven during the PoC and platform foundation phase.
Business experts identify domain-specific data quality patterns within a predefined decision framework.
Workshops focus on capturing domain rules, validation priorities, and correction intent — not redesigning system logic.
Platform capabilities are configured, not rebuilt.
Existing workflows, role models, and ML logic are adapted to the new domain, including domain-specific training data — all within the same platform foundation.
Role-based training prepares Validators and Data Owners to operate within the platform’s decision model.
A focused 2-week hypercare period ensures adoption, monitors decision quality, and captures early feedback before transition to business-as-usual operations.
New business domains can be enabled quickly because the core decision system, roles, and governance are already in place.
Most work focuses on domain-specific configuration and training, not rebuilding pipelines, rules, or models.
The same ML decision logic, confidence framework, and workflows are reused consistently across domains.
The platform supports expansion to multiple domains while preserving decision quality, accountability, and learning over time.
Each new business domain benefits from proven capabilities rather than starting from scratch.