Data Governance (DG)

• Assess AS-IS Data Governance Framework and identify any critical gaps

• Assess present process for Metadata and Reference Data Management

• Define Success Criterion for implementation

• Define & discuss the overall implementation architecture roadmap with Project Architecture team

• Help decide the integration mechanism, deployment configuration changes

• Define To-Be process for MetaData and Reference Data Management

• Provide thought leadership in terms of DG best practices

• Ensure all the best practices are followed during project implementation

• Define any DG policies and rules, which will be used for implementation

• Define Critical Data Elements along with domain consultant

• Anchor all the project implementation related discussions with the stakeholders

Data Quality(DQ)

• Conduct workshops for requirement elaboration and check for technical feasibility
• Define the data quality plan for target solution, working with source and target system SMEs
• Design and create the mechanism to extract, cleanse, transform, enrich, load and validate all required data for the successful implementation and operation of the new solutions
• Define the Data Quality approach, DQ metrics and KPIs to be measured
• Presentation of plans and progress to project and programme boards.
• Sets technical direction for the overall Data quality framework & architecture using the tool of choice, defines component architecture, reviews detailed designs for accuracy and overall compliance to defined architecture across the complete lifecycle
• Ensures that strategies translate to business requirements and models into feasible and acceptable database designs to ensure that business needs are met
• Supports the identification of critical data elements to be managed, captures the business DQ rules with Data SMEs, translates them in measurable DQ metrics and KPIs, identifies data anomalies and defines the path to remediation and validation.
• Responsible for the high level / detailed design document preparation, reviews and seeking approvals.
• Ensure all the best practices are followed during project implementation
• Ensure project Functional and Non Functional requirements are met for the respective track.
• Be aware of the programme timelines and flag potentials risks and issues for timely action by the programme management team
• Review data mapping specification and DQ scorecards
• Review Data profiling report of source datasets in scope and reported data anomalies
• Define the cleanse rules based on the DQ report and Business rules captured
• Design custom DQ scorecards based on captured DQ rules and create the DQ Scorecard

About Cognizant