Data Quality & Master Data Management (MDM)
Data Quality and Master Data Management (MDM) are critical components of a structured data management strategy that ensure data is accurate, consistent, and reliable across an organization. Data Quality focuses on the cleansing, validation, and monitoring of data to remove errors, inconsistencies, and redundancies. MDM, on the other hand, ensures that all critical business data—such as customer, product, and supplier information—is consolidated and governed under a single, unified framework. These processes enable businesses to create a single source of truth (Golden Record) for enterprise-wide data usage.
Why is Data Quality & Master Data Management (MDM) Important?
Poor data quality can result in inaccurate reporting, poor decision-making, regulatory non-compliance, and operational inefficiencies. Without MDM, businesses struggle with data fragmentation, duplication, and inconsistent records across departments. A strong Data Quality & MDM strategy ensures data integrity, improves business efficiency, enhances customer experience, and provides reliable insights for analytics and AI-driven initiatives. It also helps organizations meet compliance requirements and reduces the risks associated with bad data.
Our Data Quality & Master Data Management (MDM) Services:
Duplicate Data Identification & Removal
Use AI-driven or rule-based algorithms to identify and remove duplicate records.
Compare records across different databases and applications to detect duplicates.
Apply fuzzy matching and exact match logic for higher accuracy.
Merge duplicate records while retaining critical attributes.
Track data merges and ensure records are updated without data loss.
Allow manual intervention for critical data before removing duplicates.
Standardized Data Formats
Standardize data labels, metadata, and attribute names across systems.
Align date formats, financial units, and measurement values.
Convert disparate formats into a unified schema for easy integration.
Implement ETL/ELT-based formatting for structured datasets.
Follow ISO, HL7, IFRS, and other industry-wide data compliance Standards.
Ensure uniformity enforcement across the enterprise.
Golden Record Creation
Aggregate disparate records into a single, trusted data set.
Define relationships between customer, supplier, and transaction data.
Establish rules for who owns and manages master data.
Resolve discrepancies when multiple sources provide conflicting data.
Maintain historical records of updates and modifications.
Ensure real-time access to master data across all applications.
Data Validation & Cleansing
Use AI-based anomaly detection for identifying inconsistencies.
Implement real-time validation rules to prevent incorrect data input.
Auto-correct missing or incomplete values using reference data sets.
Identify and manage extreme values that distort analysis.
Assign data reliability scores to assess usability.
Cleanse and transform data before it is used for reporting.
Hierarchical Data Management
Maintain hierarchical relationships (e.g., Customer > Orders > Payments).
Establish linkages between customers, products, locations, and transactions.
Identify how different systems define and store similar data points.
Ensure all relational data remains accurate and intact.
Enable intuitive access to structured datasets for analytics.
Align hierarchical relationships with organizational processes.
Continuous Data Quality Monitoring
Implement scheduled checks to monitor data health.
Use BI tools to track quality KPIs and monitor trends.
Notify teams of sudden inconsistencies or missing data.
Enable AI-driven automated correction mechanisms for minor issues.
Use user-reported corrections to improve validation rules over time.
Ensure quality checks apply to massive, fast-moving datasets.