8 Common Data Governance Challenges & Issues Faced By Businesses


8 Common Data Governance Challenges & Issues Faced By Businesses

Poor data governance can lead to a myriad of issues that include data interpretation inconsistencies, security vulnerabilities, operational failures and regulatory non-compliance. These issues not only negatively impact operations but also have legal and financial repercussions. Data governance issues can exist in organizations of any size, but if you don’t know much about data governance and how it works, that’s an indication of a potential issue.

As company data sprawls and grows more complex, there is a growing global initiative to get data governance and management under control, regardless of data size or profile.

Jump to:

  • Top indications of data governance issues
  • Navigating data governance: Next steps

Top indications of data governance issues

1. There are pockets of adoption

When it comes to data and its access, pockets of adoption may not cut it. If you hear this type of conversation, keep in mind that it takes only one problem spot to cause a data handling issue. Adopting data governance has to include the entire cycle and scope of the organization. The reality is that it takes just one system to improperly handle a piece of sensitive data and cause an issue.


To ensure data governance is adopted organization-wide, it’s necessary to implement a comprehensive data governance framework that goes beyond getting buy-in from the C-suite. The business case must effectively communicate benefits, impacts, goals and milestones. Regular audits are also useful to ensure everyone is on board.

2. There is no internal data dictionary or business glossary

The notion of a data dictionary is usually implemented on database systems and enterprise applications. But with as many systems as are involved in today’s complex web of IT systems, it becomes a priority to ensure all data dictionaries and business glossaries are the same. Otherwise, situations may arise where different teams or departments aren’t aligned on certain matters.


It’s a good idea to have one data dictionary for the organization and ensure applications and their data profiles are modeled around that dictionary for data standardization. This data dictionary should be accessible by all departments and updated and audited regularly to ensure it remains the single source of truth for the organization.

3. Issues with data stewardship are causing ambiguity and overlap

A data steward, the person or group in charge of data administration, is pivotal in ensuring data is effectively managed across its entire life cycle. However, the absence of a designated data steward or the presence of multiple people or groups claiming this role in an organization often leads to problems with data governance:

  • Leadership vacuum: The void created whenever there isn’t a clear data steward leads to a lack of a centralized authority on data quality, consistency and security, which often results in inconsistent data management practices that can lead to non-compliance.
  • Confusion of multiple stewards: When you have several people or groups acting as data stewards, you may end up with many varying interpretations of the organization’s data governance policy, leading to inconsistencies or redundancies.
  • Disjointed stewardship flow: Even where you have a designated data steward(s) with clearly defined roles, ambiguous protocols caused by the lack of a well-defined stewardship flow can lead to inefficiencies and errors.


Businesses should designate a chief data steward or a data governance committee with clearly defined roles and responsibilities related to projects, datasets and/or data use cases. This central authority should be responsible for the creation and maintenance of data governance frameworks and protocols. In addition, it’s necessary to specify stakeholders and address policies for implementing technology to tend to data.

SEE: Explore how data stewardship compares to data governance.

4. Multiple systems access governed data

Interoperable systems play a big part in application and infrastructure profiles. While good practices like using strong passwords and common authentication models can be implemented, poorer practices like not letting all steps of the process take requisite care of the data may also coexist.

This can include storage systems, file share permissions, lack of encryption in connected systems, or technologies like logging and command-line interfaces. This is especially relevant for administrative tools, such as remote CLIs or debug logging systems for critical applications. There can be logs or session data that may include credentials, data and more kept on local PCs or other server systems, which would be put at risk without data access policies in place.


The best solution for this scenario is strict access controls and encryption across all systems that access data. Regular security audits are also recommended to identify and correct any security vulnerabilities.

SEE: Check out this IT staff systems and data access policy from TechRepublic Premium.

5. Some issues are ‘too difficult to correct’

There are limits to working around an issue, even if it seems “too difficult” to fix. These types of technology situations can cripple businesses over time as operations and data use cases evolve. Imagine that the size of the business doubled or tripled: Would these workarounds still seem valid?


If there are issues arising that seem too difficult to fix, it may be time to invest in data governance tools to automate the identification and correction of issues. However, investment goes hand in hand with staff training. The staff must be highly adept at configuring and using these tools to get the most out of them.

6. Operational limitations impacting data governance

Operational constraints, such as the inability to close the books of account on a timely basis at the end of the financial year due to multiple and disparate systems, can hamper your data governance efforts. These limitations not only impact operational efficiency but also create data coordination and integrity challenges across departments when staff begin to look for workarounds to get things done.

To be fair, we live in a world where organizations acquire and divest companies frequently. This organizational behavior makes these data situations more common, even if for retention and archival reasons.


In these cases, regular audits and detailed documentation are helpful ways to avoid problems that are rooted in operational visibility issues. In addition, integrating systems and eliminating workarounds can streamline operational processes.

7. Regulatory needs have changed

Requirements for regulatory compliance are constantly changing and evolving. Financial services, insurance and medical organizations know this is a serious responsibility.

If a data profile is in-scope for any regulatory or compliance requirement, it’s important to know where the new boundaries are. This can mean additional costs to go through the compliance drills as well as any corrective actions, but it’s a reality for the businesses we are operating now.


Businesses need to stay on top of regulatory changes and update their data governance policies as needed. This may involve regularly reviewing and auditing current data governance practices in addition to making revisions as privacy laws and regulations, like GDPR and HIPAA, change.

8. Correction processes are too difficult

Mature data management empowers non-data stewards and other end users to start corrective action procedures for data. Corrective actions include fixing incorrect classification, addressing the improper handling of certain data and matching up data that is duplicated.

If this process is too complex and not intuitive, users will not do it. It’s that simple. The process doesn’t necessarily need to be completed entirely by end users in the organization, but a work request to data stewards can greatly improve the overall data quality in an organization.


User-friendly data governance tools are helpful in this regard. They allow for easy reporting and issue correction. Training sessions can also help end users become more adept and confident with these tools, thus encouraging proactive data governance.

As the data held by organizations continues to grow and become more complex, there is a growing need for robust data governance strategies and tools. Before data management and governance issues arise, businesses should look into data governance best practices. These can cover everything from the importance of transparent communication to the need to focus on the right metrics.

SEE: Try using one of these top data governance tools to help you with your data governance efforts.

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Poor data governance can lead to a myriad of issues that include data interpretation inconsistencies, security vulnerabilities, operational failures and regulatory non-compliance. These issues not only negatively impact operations but also have legal and financial repercussions. Data governance issues can exist in organizations of any size, but if you don’t know much about data governance…