Authored By Gaurang M.
Ensuring data accuracy and reliability is paramount for businesses that rely on analytics, machine learning, and automation. Poor data quality can lead to misguided decisions, compliance risks, and operational inefficiencies. At InfoGlobalTech, we specialize in automating data quality checks to maintain data integrity, leveraging the Medallion Architecture as a structured approach to managing data pipelines with efficiency and governance.
Why do organizations need Automated Data Quality Checks?
Manual data quality assessments are time-consuming, error-prone, and often fail to scale with increasing data volumes. Organizations require automated solutions that can
Detect anomalies in real-time
Enforce consistency and validation rules
Ensure compliance with regulatory requirements
Streamline data transformation and enrichment processes
Enhance trust in analytics and AI models (AI Data Curation)
Medallion Architecture: A Scalable Approach to Data Quality
The Medallion Architecture (also known as the bronze, silver, and gold data model) provides a structured approach to incrementally improving data quality at different stages. This layered approach ensures raw data undergoes necessary transformations and validations before it is consumed for critical decision-making.
Bronze Layer: Raw Data Ingestion and Initial Validation
This layer is the foundation, where data is ingested as-is from various source databases, APIs, IoT devices, and streaming platforms.
Key Automated Quality Checks:
Schema validation – Ensuring incoming data adheres to expected formats.
Duplicate detection – Identifying and flagging duplicate records.
Basic anomaly detection – Identifying null values, outliers, and missing fields.
Metadata tagging – Categorizing data sources for traceability.
Silver Layer: Data Cleansing, Standardization, and Transformation
At this stage, data undergoes cleaning, deduplication, and standardization to improve quality and usability.
Key Automated Quality Checks:
Deduplication algorithms – Removing redundant records while maintaining integrity.
Data type enforcement – Ensuring consistency in date, string, and numeric formats.
Business rule validation – Checking compliance with predefined rules (e.g., valid customer IDs, transaction thresholds).
Reference data validation – Comparing against master data sources for consistency.
Gold Layer: Enriched, Trusted, and Business-Ready Data
The gold layer contains fully refined, trusted data used for analytics, reporting, and AI/ML models.
Key Automated Quality Checks:
Data reconciliation – Verifying transformations against original data sources.
Anomaly detection with AI – Using machine learning to identify unexpected trends.
Audit logs and lineage tracking – Providing visibility into data transformations.
Automated SLA monitoring – Ensuring data freshness and availability to meet business requirements.
Tools and Technologies for Automated Data Quality Checks
We implement industry-leading data governance and quality automation tools, seamlessly integrated into your data ecosystem:
Databricks & Delta Lake – Enforcing ACID compliance, schema evolution, and quality constraints.
Apache Spark & PySpark – Scalable data processing for quality checks.
Great Expectations & dbt (data build tool) – Defining and automating data validation tests.
AWS Glue, Azure Purview, Google Data Catalog – Metadata management and governance.
ML-driven anomaly detection – Leveraging AI for proactive data quality monitoring.
Why Partner with us?
End-to-End Data Governance and Automation
We don’t just provide tools; we architect and implement comprehensive data governance frameworks to ensure long-term data reliability and compliance.
Expert-Led Implementation and Support
Our team of data engineers, AI specialists, and governance experts ensures that your organization gets a tailored, scalable solution that evolves with your business needs.
Optimized for Performance and Compliance
We ensure that automated data quality checks align with industry best practices and regulatory standards (GDPR, HIPAA, CCPA, etc.), reducing operational risk and enhancing trust in your data.
Future-Proof Your Data with Automated Quality Checks
Automating data quality checks is no longer optional and is critical for organizations aiming to stay competitive in an increasingly data-driven landscape. Using Medallion Architecture as our guiding framework, we ensure that your data progresses from raw ingestion to gold-standard analytics, delivering unmatched accuracy, efficiency, and compliance.
Let’s build a future where data is big, clean, reliable, and actionable.
Contact us today to explore how we can elevate your data strategy with automation and governance.
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