|
|

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

David Chung, Ph.D.
Associate Partner
Synpulse Management Consulting Ltd.
The importance of data governance for financial institutions has long been self-evident, yet the prioritization of resource allocation toward data governance has often remained relatively low. However, in recent years, with the rise of data science, AI, and even generative AI (Gen AI), the importance of data governance has been further—albeit passively—highlighted due to the advancement of technology. This article series will focus on data governance, introducing and exploring it from various perspectives.

This article discusses the common pain points in data governance.



We have conducted interviews with several clients in the financial sector on the topic of data governance. The most important question we raised during these interviews was:
What are the most frequently encountered pain points?
Below are the three most common pain points we have compiled:
  1. Poor Data Quality
    Poor data quality is a nearly universal issue—at the very least, we have rarely heard a client say their data quality is “good enough.” There are various manifestations and possible causes of poor data quality. In general, these include: data values or information that are inaccurate or insufficiently precise, and discrepancies found during data consolidation—often during system integration—where supposedly identical data turn out to have different values.

    What’s more frustrating is that when attempting to trace the source of these problems, one often finds that the data lineage (i.e., upstream and downstream relationships) is incomplete, making root cause analysis difficult.
  1. Unclear Roles and Responsibilities in Data Governance
    In our experience, organizations with established data governance teams are already relatively uncommon, and those with clearly defined roles and responsibilities (R&R) are even rarer. When responsibilities are undefined, staff may be confused when executing related tasks, which leads to misaligned priorities and ultimately undermines the effectiveness of the entire data governance initiative.

    To put it more clearly: without well-defined performance indicators, promoting data governance across an organization becomes exceedingly difficult.
  1. Inability to Evaluate Future System Tool Implementation
    Most organizations have already acquired a significant number of system tools to handle data generated from business applications. However, we still frequently hear complaints from clients that the tools they purchased do not meet their needs or are not applicable. Even more problematic is that they are often unable to assess what additional system tools should be implemented in the future, or in what priority order.
The above are the most common data governance pain points we have heard from financial institutions. In future articles, we will continue to discuss the root causes of these pain points and explore potential solutions.



This article has been authorized by Dr. David Chung. Reproduction without permission is prohibited.
Original source:
https://www.linkedin.com/posts/davidchungtw_jfchavndvioz-nrdjgjkppkzk-oxconmlkhkzk-activity-7226870561858011137-fI_w?utm_source=share&utm_medium=member_desktop&rcm=ACoAACHogbkBPE4SYpsz6UQCUffXH-qlNMoauVI

Scroll Top