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[Data Governance #2] Common Misconceptions 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. However, the prioritization of resources allocated to data governance has often remained relatively low. In recent years, with the rise of data science, AI, and even Generative AI (Gen AI), the importance of data governance has been increasingly—albeit passively—highlighted due to technological advancements. This article series will focus on data governance, introducing and exploring it from various perspectives.

This article will explore common misconceptions in data governance.
We have conducted interviews with several clients in the financial sector regarding data governance. In these interviews, we often asked clients questions highly relevant to daily operations, such as: What is your understanding of data governance? How is it carried out? Who should be responsible?
From the responses we gathered, we frequently identified issues—or even misconceptions—worth further discussion.

Below are three of the most common misconceptions we have identified:
  1. Purchasing a data warehouse system ensures successful data governance
    In our interviews with financial institutions, we often hear the following question:

    “We’ve already purchased a data warehouse system and related tools—why are there still so many data problems?”

    Clearly, many organizations still hold the belief that solving data governance starts with purchasing a system, and that having a system in place is like taking a panacea that allows them to rest easy.
  1. Data governance is the IT department’s responsibility and has nothing to do with business or other units
    Since the vast majority of data is stored in databases—most of which are managed or owned by IT departments—it is commonly assumed that data governance is naturally the responsibility of IT.
  1. Data governance is just a short-term project and doesn’t require ongoing execution
    In many of our interviews, we’ve been asked whether data governance can be “completed” within a certain time period.
    This clearly reflects that enterprises do not view data governance in the same way they treat risk management.
    In fact, data governance is a long-term operational activity that requires sustained effort.

    The above three misconceptions are frequently encountered in our past projects and interviews, and they are often the main reasons clients face obstacles in executing data governance.
    We will continue to explain and address these misconceptions in future articles.
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-7223719797534978048-kC8g?utm_source=share&utm_medium=member_desktop&rcm=ACoAACHogbkBPE4SYpsz6UQCUffXH-qlNMoauVI

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