Head Data Quality Management Control & Operationalization

At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.

Job Description

The Data Quality Manager is key in proactively ensuring that data quality is maintained across the enterprise, and as a result enables the Analytics Team to deliver relevant and accurate models and insights. He/She will also drive action within the enterprise to ensure that data issues are resolved.

Duties And Responsibilities

• Establish data quality objectives aligned with Data Governance Policies
• Devises and executes proper data collection procedures, policies, guidelines including data cleaning, standardization, and analysis
• Define, Track and Manage Data Quality KPIs across the enterprise
• Implement controls and compliance metrics to reduce data issues and improve data quality
• Develops evaluation methods and conduct constant testing and experimentation to provide the Data Governance Council with feedback and recommendations for improvements in data management and governance process, standards and guidelines
• Design processes and relevant trainings as well as lead initiatives to ensure alignment of different data owners to data quality objectives and plans
• Partner with the Data Governance Council to define and align data management and governance strategies while providing key inputs from the data quality and cleansing initiatives
• Collaborates with reporting, analytics, and operational segments to ensure data quality standards are met, errors and root causes identified
• Identify, prioritize and lead Enterprise-wide data cleansing initiatives to deliver substantial improvements in data quality across the enterprise
• Plan, manage and deliver communication about the data cleanse activity to the key stakeholders throughout the process.
• Manage and drive teams to run and deliver initiatives designed to resolve data quality issues
• Manage project risks and issues and manage stakeholder’s expectations on initiatives started
• Mentor team members and provide training as needed
  
  
  

KPIs

• Data Quality Index (DQI): Assess the overall quality of data across the enterprise, considering factors such as accuracy, completeness, consistency, and timeliness.
• Data Issue Resolution Time: Monitor the time taken to identify and resolve data quality issues reported by various stakeholders.
• Compliance with Data Governance Policies: Ensure adherence to established data governance policies and procedures, including alignment of data quality objectives with governance policies.
• Data Cleansing Effectiveness: Evaluate the effectiveness of data cleansing initiatives by measuring the reduction in data errors and inconsistencies.
• Stakeholder Satisfaction:Measure stakeholder satisfaction with data quality improvements and initiatives through regular surveys or feedback sessions.
  
  
  

Top 3-5 Deliverables

• Data Quality Objectives:Establish and communicate data quality objectives aligned with Data Governance Policies, ensuring that they are clearly defined, measurable, and achievable.
• Data Collection Procedures and Policies:Develop and execute proper data collection procedures, policies, and guidelines, encompassing data cleaning, standardization, and analysis to ensure high-quality data inputs for analytics and reporting purposes.
• Data Quality KPI Management: Define, track, and manage Data Quality Key Performance Indicators (KPIs) across the enterprise, monitoring data quality metrics and performance against established benchmarks.
• Controls and Compliance Metrics Implementation:Implement controls and compliance metrics to identify, mitigate, and reduce data issues, ensuring continuous improvement in data quality standards and compliance with regulatory requirements.
• Enterprise-wide Data Cleansing Initiatives: Identify, prioritize, and lead enterprise-wide data cleansing initiatives aimed at delivering substantial improvements in data quality across systems and processes, addressing data errors, inconsistencies, and redundancies.
  
  
  

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