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

          HomeCase StudiesAI/ML-Driven Data Cleansi...

          Executive Summary

          A leading telecom provider partnered with Computyne to resolve critical data quality issues stemming from rapid growth and legacy system fragmentation. By implementing an AI/ML-driven Data Cleansing Framework, the client processed over 13.7 million records, eliminated 850,000+ duplicates, and established a secure data foundation aligned with ISO/IEC 27001:2022 standards.

          The Objective

          To establish a "Golden Record" for customers and service locations by resolving data fragmentation and inconsistency. The goal was to build a scalable, secure foundation that supports advanced analytics, AI adoption, and operational automation.

          Challenges We Faced

          To support the client’s digital transformation, several telecom-scale data challenges had to be addressed:

          • Fragmented Ecosystem:Customer and network data resided in silos across disconnected legacy mainframes and modern cloud platforms.
          • High Data Duplication:Redundant records distorted subscriber reporting and reduced the accuracy of churn prediction models.
          • Incomplete Attributes:Critical contact fields and service location details were missing, directly affecting service delivery.
          • Low Data Confidence:Limited trust in the underlying datasets restricted the adoption of automated marketing and support tools.

          Challenges We Faced

          To support the client’s digital transformation, several telecom-scale data challenges had to be addressed:

          • Fragmented Ecosystem:Customer and network data resided in silos across disconnected legacy mainframes and modern cloud platforms.
          • High Data Duplication:Redundant records distorted subscriber reporting and reduced the accuracy of churn prediction models.
          • Incomplete Attributes:Critical contact fields and service location details were missing, directly affecting service delivery.
          • Low Data Confidence:Limited trust in the underlying datasets restricted the adoption of automated marketing and support tools.

          Key Results & Impact

          The initiative transformed the client’s raw data into a strategic asset, delivering massive scale improvements.

          MetricVolumeDescription
          Total Processed13.7 Million+Total data points standardized and cleansed across the enterprise.
          Customer Records7.5 Million+Customer and account records fully validated and enriched.
          Location Data6.2 Million+Service location records verified for accurate network mapping.
          Duplicates Removed850,000+Obsolete and redundant records eliminated, reducing storage costs and improving reporting.

          Why Choose Computyne for Telecom Data?

          • Higher Data Trust: Improved accuracy directly enhances analytics reliability and AI model performance.
          • Operational Efficiency: AI-assisted workflows replace slow, error-prone manual validation.
          • Security First: Full alignment with ISO/IEC 27001:2022 security standards.
          • Automation Readiness: Clean, reliable data enables seamless integration with advanced automation platforms.

          "We were struggling to make sense of millions of messy records until Computyne stepped in. They handled the scale and complexity perfectly. Now, we finally have data we can trust, which has significantly accelerated our decision-making and analytics."

          Chief Data Officer, Leading Telecommunications Enterprise

          Project Info

          Company Type
          Leading Telecommunications Enterprise
          Industry
          Telecommunications
          Headquarters
          India
          Operational Scope
          National Network Provider

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          Quality Before You Commit.Start a Free Pilot Project

          Turn Your Results Into Our Next Milestone !

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