Analytical and model-driven decisions are only as defensible as the data behind them. Orlovs Consulting provides data quality assurance to support data analytics platforms, machine-learning workflows, and AI systems operating in regulated and high-trust environments.
Where relevant, this includes systems built on Model Context Protocol (MCP).
This is a consultancy-led service delivered through tailored processes and workflows, not a packaged software product.
This service is intended for organisations where data provenance, representativeness, and auditability matter more than silent correction.
What this is and is not
This is not a traditional data quality platform, ETL tool, or master data management solution.
Our Data Quality Assurance service is designed to:
- Support analytics, ML, and AI use cases in regulated environments
- Work with read-only or controlled data access models, including MCP
- Assess suitability of internal or external data sources for analysis and modelling
- Surface data quality risks, limitations, and assumptions explicitly
- Provide evidence and context for analysts, data scientists, and decision-makers
All outputs are advisory, inspectable, and reviewable, not automatically applied.
What this service provides
Data quality assessment
We assess data sources used in analytics platforms, ML pipelines, or AI-assisted systems to identify characteristics that affect reliability and decision-making, including:
- Completeness and coverage gaps
- Consistency across records, sources, and time
- Timeliness and update behaviour
- Structural and semantic anomalies
- Known limitations, biases, or ambiguities
Assessments are designed to inform whether and how data should be relied upon, not to silently correct it.
Data provenance and traceability
We design processes that explicitly document:
- Source systems and publication points
- Access paths and refresh behaviour
- Known transformations applied upstream
- Relationships and dependencies between datasets
This supports audit, assurance, reproducibility, and internal review of analytical, ML, and AI workflows.
Quality signals for analytics, ML, and AI workflows
Where appropriate, data quality indicators can be surfaced to users or downstream systems as contextual signals, such as:
- Confidence or coverage indicators
- Known exclusions, edge cases, or data gaps
- Freshness, staleness, or volatility warnings
These signals are designed to inform human analysis and model development, not to automate decisions.
Data lake and pipeline design
Where required, we also help organisations design and deploy data lakes that support reliable analytics, ML, and AI use, often on cloud platforms such as AWS and Azure.
This typically follows a Medallion Architecture approach:
- Bronze layer — raw, ingested data with full traceability
- Silver layer — validated, cleaned, and structured datasets
- Gold layer — curated, analysis-ready datasets aligned with business use cases
Each layer is treated as a governed stage in the data lifecycle, with explicit quality checks, documentation, and review points.
Data quality checks run across these layers, so that refinement steps are explicit, reviewable, and auditable.
Designed for regulated environments
That means:
- Read-only or controlled access to source data
- No automated correction or mutation of records
- Clear separation between assessment and decision-making
- Outputs suitable for audit and peer review
- Alignment with internal governance and risk models
This approach supports analytics, ML, and AI adoption without introducing hidden data changes.
Typical use cases
Organisations typically use this service to:
- Assess suitability of regulatory, public, or internal datasets for analytics or ML
- Improve confidence in analytical reports and dashboards
- Support internal compliance, risk, and audit tooling
- Support model development and validation workflows
- Prepare data quality documentation for audit or assurance
- Reduce uncertainty around AI- or model-assisted outputs
It is commonly used alongside MCP services and applied solutions such as the Regulatory Compliance Assistant.
Relationship to MCP services
Data Quality Assurance works alongside MCP services.
- MCP provides controlled, read-only access to data
- Data Quality Assurance assesses and documents data characteristics
- Applied solutions use both to support defensible analysis, model development, and decision-making.
Each layer remains independently reviewable and governed.
Limitations and safeguards
Explicit limitations
This service:
- Does not modify, cleanse, or correct source data
- Does not override upstream data governance
- Does not automate data quality decisions
- Does not guarantee data accuracy or completeness
It provides assessment and context, not enforcement.
Safeguards by design
- Read-only or controlled data access
- Transparent documentation of findings
- Evidence-backed outputs
- Human review by default
Procurement and audit summary
Scope and intent
- Supports analytics, ML, and AI workflows
- Provides data quality assessment and documentation
- Does not perform automated data correction
Auditability
- Data quality findings are inspectable and reviewable
- Sources, assumptions, and limitations are explicitly documented
- Suitable for internal audit and assurance activities
Risk posture
- Reduces risk from implicit data assumptions
- Improves defensibility of analytical and ML outputs
- Works within internal governance and risk models