Summary The Insights and Decision Science (IDS) team is dedicated to enabling improved decision-making at Novartis by leveraging data and advanced analytics capabilities to generate actionable insights that drive business growth. We collaborate closely with the US business, bringing insights and challenging ideas to empower smarter, data-driven decision-making. The Lead, Quality Assurance and Governance is responsible to ensure operational processes adopt data governance frameworks, policies, and standards that support the organization's strategic objectives.
By collaborating with Data Strategy and Governance pillar of IDS, this role will ensure that all relevant frameworks are implemented, governance is put in place along with escalation mechanism. The Lead will also work with cross functional IDS pillars to understand and triage data quality issues ultimately ensuring enhancement of product or business process quality. The ultimate goal of this team will be to deliver high trust in data.
Major Accountabilities
Data Quality Management: Overseeing the implementation of data quality initiatives to ensure that data is accurate, complete, and reliable. This includes monitoring data quality metrics, identifying data quality issues, and implementing corrective actions.
Data Governance Framework: Developing and maintaining a robust data governance framework that defines roles, responsibilities, and processes for data management. This framework ensures that data is managed as a valuable asset and is used in compliance with regulatory requirements. Establish and enforce data governance policies, standards and procedures.
EDM Support: Supporting US EDO with quality processes and monitoring. Embed DCAM and DAMA principles into data management processes, including metadata management, data lineage, data privacy, and master/reference data management. Conduct regular DCAM-based capability assessments to identify gaps and drive continuous improvement.
Stakeholder Collaboration: Collaborating with various stakeholders, including IT, business units, and external partners, to ensure that data governance practices are aligned with business needs and objectives.
Risk Management: Identifying and mitigating risks associated with data management, including data breaches, data loss, and non-compliance with regulations.
Training and Awareness: Promoting data governance awareness and providing training to employees on data management best practices and policies.
Data Stewardship Oversight: Establishing and managing a network of Data Stewards across business units. Defining stewardship roles and responsibilities, facilitating regular engagement, and ensuring Data Stewards are empowered to monitor, report, and resolve data quality and governance issues in alignment with Novartis Governance policies and standards
Continuous Improvement: Continuously evaluating and improving data governance practices to adapt to changing business needs and technological advancements
Key Performance Indicators
Data Accuracy: Measure the percentage of data entries that are free from errors and discrepancies. High data accuracy ensures reliable decision-making.
Data Completeness: Track the proportion of data fields that are fully populated. Complete data sets are essential for comprehensive analysis.
Data Consistency: Evaluate the consistency of data across different systems and databases. Consistent data helps maintain integrity and reduces redundancy.
Data Timeliness: Assess the speed at which data is updated and made available for use. Timely data is crucial for real-time decision-making.
Compliance Rate: Monitor adherence to data governance policies and regulatory requirements. High compliance rates indicate effective governance practices.
Data Quality Issue Resolution Time: Measure the time taken to identify and resolve data quality issues. Faster resolution times improve data reliability.
User Satisfaction: Gauge the satisfaction levels of stakeholders and users with the quality and governance of data. Positive feedback reflects successful data management.
Training and Awareness: Track the number of employees trained on data governance policies and best practices. Increased awareness leads to better data handling.
Risk Mitigation: Evaluate the effectiveness of risk management strategies in preventing data breaches and losses. Effective risk mitigation ensures data security.
Continuous Improvement: Measure the progress in enhancing data governance practices over time. Continuous improvement indicates a proactive approach to data management.
Essential Requirements:
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