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As more and more organizations mature with their data management systems and increasingly taking advantage of their enhanced capabilities with respect to their Information systems, the ability to enhance the quality, availability and integrity of data and its effective governance is vital for mitigating risk and increasing value of data.

Trinus, with its wide ranging experience in the data management domain, has a practice model that rivals the best in industry by providing a systematic approach to data quality and governance implementations that protects and leverages your critical data assets. Trinus’ team of experts combine their proven business and technology knowledge with collaborative methods and best practices to establish and jumpstart data governance projects and ensure that these projects are properly integrated with business needs.

Our data governance services can support your initiatives in:

  • Protecting intellectual and informational assets
  • Establishing and meeting compliance requirements
  • Enhancing potential innovation by utilizing quality data and established process of governance of data
  • Risk management and mitigation
  • Revenue generation and cost optimization by aligning with business imperatives

 Our data governance practice and implementation approach stresses on:

  • Promoting global and strategic thinking while simultaneously executing locally and tactically
  • Building just enough governance to solve specific business problems
  • Focusing on business-driven use cases and integrating success metrics to deliver measurable results
  • Leveraging automation and established tools to deliver results
  • Enabling cultural change by viewing data governance as an organizational change across business functions

Once a data governance program is established, our methodology focuses on maintaining the trust, transparency and discipline to sustain the initial implementation.

Data governance implementation mainly focuses on:

  • Establishing data governance information model
  • Creating and monitoring data quality and profiling processes
  • Linking technology-oriented data dictionary with business-oriented business glossary
  • Establishing custom analytics on the metadata, model usage, glossary status, workflow performance, business rules usage
  • Implementing and managing workflow and exception management processes
  • Creating and defining business rules and policy management
  • Establishing audit trail and tracking

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