Poor data governance can lead to a myriad of issues that include data interpretation inconsistencies, security vulnerabilities, operational failures, and regulatory non-compliance. These problems 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 how it works, that’s an indication of a potential concern.
This guide will explain the challenges and offer solutions for your organization.
SEE: Data Governance Frameworks: Definition, Importance, and Examples (TechRepublic)
Top indications of data governance issues
1. 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 a single lapse can result in a serious data handling failure. 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.
Solution
Implement a comprehensive 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. No 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.
Solution
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 standardization. This 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. Stewardship causing ambiguity and overlap
A data steward — whether an individual or a group —is responsible for data administration. This steward is pivotal in ensuring data is effectively managed across its entire life cycle. However, the absence of a designated steward or the presence of multiple people or groups claiming this role often leads to problems:
- Leadership vacuum: The void created whenever there isn’t a clear data steward leads to a lack of a centralized authority on quality, consistency, and security. This often results in inconsistent data management practices that can lead to non-compliance.
- Confusion with multiple stewards: When you have several people or groups acting as stewards, you may end up with varying interpretations of the organization’s governance policy, leading to inconsistencies or redundancies.
- Disjointed stewardship flow: Even where you have a designated steward with clearly defined roles, ambiguous protocols caused by the lack of a well-defined stewardship flow can lead to inefficiencies and errors.
Solution
Designate a chief steward or a governance committee with clearly defined roles and responsibilities related to projects, datasets, and/or use cases. This central authority should be responsible for the creation and maintenance of governance frameworks and protocols. In addition, it’s necessary to specify stakeholders and address policies for implementing technology to tend to data.
4. Multiple systems
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 and data kept on local PCs or other server systems, which would be put at risk without data access policies in place.
Solution
The best idea is strict access controls and encryption across all systems that access data. Regular security audits are also recommended to identify and correct any vulnerabilities.
5. ‘Too difficult to correct’
There are limits to working around an issue, even if it seems “too difficult” to fix. These types of situations can cripple businesses over time as operations and data use cases evolve. Imagine if the size of the business doubled or tripled: Would these workarounds still seem valid?
Solution
It may be time to invest in governance tools to automate identification and correction. This 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
Operational constraints, such as the inability to close the books on a timely basis at the end of the financial year due to multiple and disparate systems, can hamper your governance efforts. These limitations impact operational efficiency and 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 behavior makes these data situations more common, even if for retention and archival reasons.
Solution
Regular audits and detailed documentation help with 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.
Solution
Stay on top of regulatory changes and update governance policies as needed. This may involve regularly reviewing and auditing current practices in addition to making revisions as privacy laws and regulations, like GDPR and HIPAA, change.
8. Correction processes are too complex
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.
Solution
User-friendly 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.
This article was originally published in October 2022. An update was made in September 2023. The latest update was by Antony Peyton in July 2025.
