DataFramed #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
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Apr 3, 2024 Stefaan Verhulst, Chief Research & Development Officer at NYU Governance Lab, and Malarvizhi Veerappan, Senior Data Scientist at the World Bank, join the conversation on data governance and quality. They discuss the essential components of data quality, comparing it to baking a cake for effective decision-making. The duo shares insights on balancing innovation with governance while establishing trust through the 'five Ps' framework. Their practical examples, especially from the banking sector, highlight the collaborative effort needed to enhance data management.
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Data Quality Definition
- Data quality involves having the right data, in the right format, at the right time.
- It enables better decisions, improves client experience, and reveals opportunities.
Cake Analogy
- Esther Munyi uses a cake analogy to explain data quality: wrong ingredients or quantities lead to a bad cake.
- Similarly, poor data quality can result in bad business decisions and frustrated clients.
Data Quality Challenges
- Data quality is a continuous challenge due to the dynamic nature of data and its context.
- Data governance and a culture of data quality are crucial for maintaining good data quality.