Tech mahindra
Tech mahindra

Data Governance

The Tech Mahindra Data Governance framework offers a SOA-based, comprehensive approach towards data governance. It is a wholesome approach that looks at Data from all dimensions, right from the management of Master Data, Meta Data, Data Quality, Data Integration, Data Archival and Test Data for your critical data projects. It is based on pre-built, readily available standards and guidelines and looks at bringing reliable control across your organization's data points.

The framework enables value to be derived from your data by helping you with the:

  • Management, development and protection of data as an enterprise asset
  • Effective identification of Data in various industry/domain contexts
  • Facilitation of controlled, appropriate and consistent access, distribution and sharing of enterprise data objects
  • Bringing out true benefits of the SOA program including process flexibility, reusability and standards
  • Built on best of breed tools and technologies

The following services are offered under the umbrella of Data Governance services:

  • Master Data Management
  • Meta Data Management
  • Data Quality Management
  • Data Integration and Acquisition
  • Test Data Management
  • Data Archival
  • Data Anonymization
  • Robust methodology to help in building analytical, operational and collaborative MDM solutions
  • Master Data Hub implementation serves as a back bone for future Data Management and Business Intelligence services
  • Source system master data analysis, integration, design and implementation of data models in MDM hub
  • An integrated 360 degree view of business entities with strong data governance and stewardship interface
  • Building application layers on top of central master data hubs to provide master information to various OLTP & OLAP applications built on Service Oriented Architecture
  • Master Data Management hub on multi-domains, like customer, supplier, product, vendors, etc.
  • Providing standardized and reliable data for effective business transactions
  • Synchronization between the MDM hub and source systems through real/near-real-time batch processes
  • Ongoing training for skill up-gradation
  • Consolidating Metadata from disparate systems
  • Maintenance of Metadata
  • Data Lineage report
  • Configuration, Versioning, Scalability, Administration
  • Lower TCO (Total Cost of Ownership)
  • Enterprise-wide data quality assessment and strategic directions for enterprise DQM
  • Design and implementation of DQM solutions
  • Domain specific Metric and KPI based Data Quality solutions for multiple industries
    • End to end solutions for all your data points right from customer data, service data, billing data, inventory/product data, etc.
    • Solutions to decrease Cycle Time & Cost per Order, and increase Variance (Ex: Right First Time) & Throughput (Ex: Volume of Correct Orders)
    • Prevention of Revenue Leakage through Quality improvements and cost control
  • Multiple levels of Quality Management as the way towards ultimate information governance
    • Operational DQM – Tool based Data Quality Improvements/Failure Cause Identification
    • Tactical DQM – Root Cause Analysis for systems and stacks
    • Strategic DQM – Process Improvements and Business Value Generation
  • Aided by in-house built Frameworks and Tools
  • Keep it Clean (KIC) proposition as a solution towards continuous quality improvement
  • Data Integrity Management across your processes, systems and Data
  • Periodic review, health check and improvement of the existing data quality management applications
  • A combined Tool-Service-Domain expertise to ensure higher Return on investment (ROI) compared to traditional one way approaches to Data quality

Data Acquisition

  • Seamless Interface with multiple source systems which include File-based sources, RDBMS, log scanners, web services, sensors, etc.
  • Standardized approach for Change-data-capture mechanisms for daily and near real-time feeds
  • Data Acquisition framework to ensure File-level and Field-level Data quality checks in line with the enterprise Data Governance strategy
  • Capabilities across a large breadth of Data Acquisition technologies

Data integration

  • Integration of data from multiple, heterogeneous source system applications
  • Providing a 360-degree view and Single-version-of-truth across the enterprise
  • Addressing data redundancy and duplication through effective Data Standardization
  • Enhancement of data consistency and standardization of business entity, business data definitions
  • Enabling easier and effective use of the data to facilitate decision-making
  • Alignment of Data Integration solutions with the enterprise Data Governance strategy
  • Provisioning of required test data ensuring compliance to regulatory and organizational standards
  • Extraction of data for non-production environments
  • Creation of targeted, appropriately sized test environments
  • Option of synthetic or production data
  • Maintaining test data quality
  • Masking of sensitive data to prevent unauthorized usage
  • Expertise on multiple tools and products
  • Framework based approach towards Archival - Reusable components leading to minimal time spent on development, reduced cost and quicker time to market
  • Multiple technology options including archiving on Hadoop, Flat File and other low cost options
  • Helps lower IT costs by archiving inactive data and data from legacy / end of life applications
  • Seamless integration with multiple reporting technologies and platforms
  • Easy and appropriate levels of access to archived data, through standard and ad-hoc reporting
  • On demand management and retrieval of information
For further information please write to

For further information please write to