AI/ML-Driven Data Cleansing for a Leading Telecommunications Enterprise

Our Objective

A leading telecommunications provider in India approached Computyne with a critical operational challenge: managing rapidly growing volumes of customer, contact, and location data across multiple disparate enterprise systems. Faced with deep-seated inconsistencies, high duplication rates, and incomplete records, the client found that poor data quality was severely affecting analytics accuracy and stalling new AI initiatives. They required a secure, scalable partner to transform this fragmented information into a clean, standardized, and intelligence-ready foundation, all while maintaining strict alignment with ISO/IEC 27001:2022 security standards.

Challenges We Faced

To support their digital transformation, the client needed a structured data cleansing partner with robust AI/ML capabilities to manage telecom-scale datasets securely and efficiently.

• Fragmented Data Ecosystem: Customer and account data was siloed across a mix of legacy mainframes and modern cloud platforms.
• High Data Duplication: Redundant records were skewing reporting metrics and reducing the accuracy of model training.
• Incomplete Attributes: Essential contact and location details often lacked critical fields required for service delivery.
• Manual Bottlenecks: Existing manual validation processes were slow, error-prone, and operationally expensive.
• Low Data Trust: Limited confidence in the underlying data prevented the successful deployment of automation and AI-driven workflows.

How We Solved It

Computyne delivered an end-to-end Data Cleansing and AI/ML Solutions Framework specifically engineered to handle the complexity and scale of the telecommunications sector. All processing activities were executed under strict security controls aligned with ISO/IEC 27001:2022, ensuring confidentiality, integrity, and controlled access to sensitive telecom data.

• AI/ML-Driven Data Profiling: Advanced machine learning models analyzed incoming datasets to detect patterns, anomalies, and structural inconsistencies across millions of records. This automated profiling enabled faster identification of data health issues and prioritized immediate cleansing actions.
• Contact Data Processing For datasets containing names, email addresses, phone numbers, and corporate details, we applied a multi-stage refinement process:
• Contact Data Processing : AI-Assisted Cleaning and Reformatting: ML-based normalization standardized data formats, corrected structural inconsistencies, and automatically flagged low-confidence entries for review.
• Contact Data Processing : Intelligent Filtering and Deduplication: AI-driven matching algorithms identified duplicates and obsolete records with significantly higher precision than traditional rule-based methods.
• Contact Data Processing : ML-Based Enrichment and Verification: Predictive models cross-referenced data against verified sources to improve the completeness and accuracy of contact attributes.
• Contact Data Processing For datasets containing names, email addresses, phone numbers, and corporate details, we applied a multi-stage refinement process:
• Location Data Processing : AI-Enabled Updating and Completion: Machine learning models inferred missing address elements and corrected partial entries to ensure deliverability.
• Location Data Processing : Smart Cleaning and Grouping: AI clustering techniques consolidated duplicate and conflicting location records into unified, consistent structures.
• Location Data Processing : Verification Through ML Confidence Scoring: Each record was validated using confidence scores to ensure reliability for downstream analytics and automation.
• Continuous AI-Driven Quality Monitoring: Post-cleansing, AI models were deployed to continuously monitor incoming data streams, enabling real-time detection of quality degradation, new duplication patterns, and anomalies.

Results Achieved

• 13.7 Million+ total data points processed and standardized.
• 7.5 Million+ customer and account records validated.
• 6.2 Million+ service location records verified.
• 850,000+ duplicate and obsolete records removed.

Why Choose Computyne?

By partnering with Computyne, the client successfully transformed their raw information into a trusted, future-ready asset.

• Higher Data Trust: Clean, standardized data immediately improved predictive model accuracy and reporting reliability.
• Operational Efficiency: AI-assisted workflows significantly reduced the manual effort required for data validation.
• Security & Compliance: All operations were executed under strict ISO/IEC 27001:2022 certified controls.
• Automation Readiness: High-quality, reliable data enabled smoother integration with advanced AI systems and automation tools.