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[Data Governance #3] Common Pain Points in Data Governance

David Chung, Ph.D.
Associate Partner
Synpulse Management Consulting Ltd.
Data governance has long been recognized as crucial for financial institutions, yet the allocation of resources for data governance has often been deprioritized. However, in recent years, the rise of data science, AI, and especially generative AI (Gen AI) has forced a reevaluation of its importance. This series will focus on different aspects of data governance, starting with common pain points.

Common Pain Points in Data Governance
  1. Poor Data Quality
    Poor data quality is a nearly universal issue. Clients seldom report having sufficient data quality. The issues range from incorrect or imprecise data to inconsistencies discovered during data integration, particularly during system consolidations. More frustratingly, tracing the root of these problems is often hampered by incomplete data lineage.
  1. Unclear Data Governance Role and Responsibilities
    It is rare to find organizations with established data governance teams and clear roles and responsibilities (R&R). Without clear definitions, the execution of data governance tasks becomes chaotic, leading to ineffective overall governance. Clear performance metrics are necessary to drive data governance initiatives successfully.
  1. Inability to Assess Future System Tools
    Organizations frequently complain about existing tools being inadequate or unsatisfactory. Worse, they often lack the ability to evaluate and prioritize future tools, further complicating data governance efforts.
Above are the most common data governance issues in financial institutions. Future discussions will delve into the causes of these problems and potential solutions.



This article has been authorized by Dr. David Chung. Reproduction without permission is prohibited.
Original source:
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