#193 [Radar Recap] From Data Governance to Data Discoverability: Building Trust in Data Within Your Organization with Esther Munyi, Amy Grace, Stefaan Verhulst and Malarvizhi Veerappan
Experts Esther Munyi, Amy Grace, Stefaan Verhulst, and Malarvizhi Veerappan discuss strategies for improving data quality, fostering trust in data governance, and balancing governance with accessible data. They explore the significance of data quality in decision-making, establishing trust in data governance, navigating the balance of data use and protection, measuring and ensuring data quality, and building trust in data discoverability within organizations.
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Quick takeaways
Improving data quality is essential for driving trust, enabling better decisions, and gaining a competitive edge.
Balancing data governance with accessibility requires measuring data quality, industry standards, and aligning key components for success.
Deep dives
Importance of Data Quality for Effective Decision-Making
Data quality plays a crucial role in enabling business leaders to make better decisions by ensuring that data is accurate, relevant, complete, and consistent. Organizations can improve client experience, increase revenues, and gain a competitive advantage by focusing on enhancing data quality. Poor data quality can lead to incorrect decisions, frustrated clients, missed opportunities, and diminished product offerings.
Challenges in Achieving Perfect Data Quality
Data quality is a complex and dynamic subject influenced by various factors such as the evolving nature of data, changing contexts, and decisions made throughout the data life cycle. Organizations need to adopt a cultural shift towards prioritizing data quality, integrate data stewardship practices, and ensure a holistic approach to maintaining data quality. A culture of data quality is emphasized to prevent data inaccuracies and encourage efficient data stewardship.
Balancing Data Governance with Agility and Accessibility
Balancing data governance with agility and accessibility involves showcasing the value of good data governance to encourage innovation while safeguarding data. Organizations should focus on measurable data quality dimensions, industry standards, regulatory requirements, and business rules to assess and improve data quality. Incorporating data quality metrics in reports and streamlining data governance practices can help drive business decision-making.
Principles and Frameworks for Effective Data Governance
Establishing effective data governance requires aligning five key components: purposes, principles, processes, practices, and positions. By defining a clear purpose aligned with principles and implementing robust decision-making processes, organizations can ensure data quality and governance success. Leveraging frameworks like the five-piece model and incorporating principles like equity, value, and trust can guide organizations toward robust data governance practices.
Driving trust with data is essential to succeeding with analytics. However, time and time again, data quality remains an issue for most organizations today. In this session, Esther Munyi, Chief Data Officer at Sasfin, Amy Grace, Director, Military Engines Digital Strategy at Pratt & Whitney, Stefaan Verhulst, Chief Research & Development Officer, Director of Data Program at NYU Governance Lab, and Malarvizhi Veerappan, Program Manager and Senior Data Scientist at the World Bank will focus on strategies for improving data quality, fostering a culture of trust around data, and balancing robust governance with the need for accessible, high-quality data.
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