Data quality issues – who wants to deal with it?

Data quality issues are a pervasive problem in many organizations, yet data owners are often reluctant to address them. I’m Fede Nolasco, and in this article, we’ll explore the reasons behind this reluctance and discuss ways to encourage data owners to take ownership of their data quality.

Nobody wants to deal with data quality issues

Nobody wants to deal with data quality issues

1. Lack of Awareness and Understanding

Many data owners may not fully comprehend the importance of data quality or the impact that poor data quality can have on their organization. They may not realize that inaccurate, incomplete, or outdated data can lead to poor decision-making, decreased productivity, and even regulatory penalties.

2. Limited Resources and Competing Priorities

Data owners often have multiple responsibilities, and data quality management may not be their primary focus. With limited time and resources, data owners may prioritize other tasks, such as meeting project deadlines or addressing urgent issues, over data quality initiatives.

3. Fear of Accountability

Addressing data quality issues may reveal underlying problems or errors that could reflect poorly on the data owner. Some data owners may prefer to avoid the spotlight and potential criticism, choosing instead to ignore data quality issues.

4. Lack of Skills and Expertise

Effective data quality management requires specialized skills and knowledge, which many data owners may not possess. Without the necessary expertise, data owners may feel overwhelmed or uncertain about how to address data quality issues.

5. Inadequate Support and Collaboration

Data quality is a shared responsibility that requires collaboration between various stakeholders, including data owners, data users, and data governance teams. In organizations with siloed teams or weak collaboration, data owners may feel unsupported in their data quality efforts.

6. Insufficient Tools and Technology

Without the proper tools and technology, addressing data quality issues can be a daunting and time-consuming task. Data owners may be hesitant to embark on data quality initiatives if they lack the necessary resources to efficiently identify, diagnose, and resolve data quality issues.

Encouraging Data Owners to Address Data Quality Issues

To overcome these challenges and motivate data owners to tackle data quality issues, organizations can take several steps:

  1. Promote data literacy and awareness: Educate data owners on the importance of data quality and its impact on the organization through training and resources.
  2. Prioritize data quality: Establish data quality as a strategic priority and allocate sufficient resources to support data quality initiatives.
  3. Foster a culture of accountability: Encourage a culture of transparency and accountability, where data owners feel responsible for maintaining the quality of their data assets.
  4. Provide support and training: Offer training and resources to help data owners develop the skills and expertise needed to effectively manage data quality.
  5. Strengthen collaboration: Break down silos and promote collaboration between data owners, data users, and data governance teams.
  6. Invest in tools and technology: Equip data owners with the necessary tools and technology to efficiently identify, diagnose, and resolve data quality issues.

By addressing these barriers, organizations can encourage data owners to take ownership of their data quality and foster a data-driven culture. If you have any thoughts or experiences related to data quality, I’d love to hear from you! Reach out to me at datatunnel, and don’t forget to connect with me, Fede Nolasco, on LinkedIn or Twitter. Together, let’s improve the quality of our organization’s data and unlock its full potential!


  1. Data Quality Fundamentals [Book] (
  2. Data Quality Improvement Process

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