9 Data Governance Challenges for Data Leaders

Data governance programs are paradoxical. They define how teams manage, access, protect, and use data assets across an organization, but from organization to organization, the regulations dictating adherence may not all be the same. The data governance challenges that data leaders face in a given organization may be completely unique, as everything from business type to stakeholder needs will determine the particulars of that governance landscape.

That said, some governance challenges occur much more frequently than others. So while it’s important to keep yourself open to nuance, data leaders who keep an eye out for common challenges are often more strategically prepared to overcome them.

9 critical data governance challenges that every data leader must address

Below are nine challenges that data leaders commonly experience across industries:

1. Unclear roles and responsibilities

People tend to make things complicated, which is why defining clear roles and responsibilities in large organizations most likely predates the pyramids. As organizations grow, more people get involved—and as more people enter the fray, data leaders need to increasingly ensure that an increasing number of roles and responsibilities don’t muddy the waters for ongoing data governance efforts.

When role clarity in an organization degrades, it becomes difficult to know who owns specific data sets, who has the authority to make decisions, and who is accountable for overall data quality (for instance, data stewards).

This disorganization can cripple stakeholder decision-making. Unchecked, it will also prevent data teams from consistently and efficiently implementing necessary governance policies over time.

Example: Imagine a large hospital whose employees are confused regarding who should update allergy information for a patient’s electronic health records. In this case, a nurse might administer a common NSAID—ibuprofen, aspirin, or naproxen—after a minor procedure without realizing that the patient is allergic.

2. Outdated or inconsistent PLM

While implementation can prove problematic, data leaders also need to keep their data governance policies up to date. This means that, as part of policy lifecycle management (PLM), they must manage the full lifecycle of organizational governance policies from creation to retirement, as necessary.

The greater regulatory landscape is in constant flux since new technological, ethical, and societal developments require endless amendments and updates to laws. Therefore, inadequate PLM can cause an organization to fall behind, and outdated or inconsistent governance policies can increase the chances of non-compliance and legal exposure risks, process inefficiencies, and misaligned business objectives.

Example: Due to outdated data retention policies, a regional bank retains sensitive data—including personally identifiable information, credit card details, and transaction histories—for longer than necessary. The ongoing existence of this information increases the bank’s attack surface and could result in significant fines if vulnerabilities expose it.

3. Tradeoffs between accessibility and security

For data to be valuable, it needs to be accessible. However, data leaders need to balance the ability to access organizational data with their ability to keep it secure. This particular tradeoff is why balancing data access with strong security and privacy controls makes for one of the most enduring data governance challenges in this list.

On one hand, the ability for data consumers and stakeholders to readily access data correlates directly with greater productivity and innovation. But lax data security practices expose the organization to data breaches, compliance violations, and reputational damage with consumers.

As a result, data leaders who fail to strike this balance can tragically incur negative consequences on both sides of the accessibility vs. security equation.

Example: In an attempt to maximize security, data leaders at a tech company over-restrict access to product usage data. As a result, the organization’s marketing teams struggle to develop targeted product campaigns that match those of a direct competitor, resulting in missed opportunities and lost market share.

4. Poor data quality and consistency

Data governance strategies should optimize data quality and consistency across data sources, not just control and protect it. This is because poor data quality leads to unreliable analytics, flawed reporting, and compromised business decisions.

The confusion and inefficiencies that these data quality issues create can set off a problematic feedback loop within an organization and eventually erode effective data governance processes.

Example: Data quality and consistency are critical healthcare, as medical professionals rely on the quality of their patient’s data to make accurate diagnoses. This is especially important in emergency medicine, where flawed patient data—like records showing an incorrect blood type—could result in a potentially fatal transfusion.

5. A lack of ongoing monitoring and improvement

Data governance must involve ongoing monitoring and continuous improvement efforts due to the amount of data that courses through an average organization and its demands. Failure to prioritize this increases the chances that data quality issues or metadata inconsistencies will go undetected, resulting in data quality deterioration over time.

If teams don’t continuously improve data governance processes, security vulnerabilities will increase proportionally as data breaches and instances of unauthorized access become more common. Additionally, as these security risks grow, productivity can take a nosedive since unmonitored and outdated governance directly contributes to ineffective data management.

Example: A large ecommerce company relies on customer behavior datasets to personalize its product recommendations. However, due to scaling pressures, leadership deprioritizes ongoing monitoring and continuous improvement of data governance processes. Unbeknownst to the organization’s data teams, dataset quality also declines. As a result, the company’s recommendation engine increasingly promotes items that are either out of stock or entirely irrelevant to online shoppers, compromising quarterly sales.

6. Compliance orchestration complexity

Orchestration tends to complement data governance practices, especially as the need for data leaders to harmonize compliance efforts across multiple regulations, standards, and jurisdictions grows more necessary. However, the highly fluid, increasingly complex regulatory landscape makes compliance orchestration as much of an art as a skill.

Much of this art takes place internally, as leaders need to develop governance-related strategies for navigating regulatory complexity and fostering a compliance-oriented culture. What’s more, doing so requires data engineering teams to implement flexible compliance controls and reporting mechanisms that can stretch and grow with the organization’s needs while providing the consistency and transparency that demonstrating compliance requires. Establishing consistent audit trails is also essential for demonstrating regulatory adherence across systems and jurisdictions.

Example: In the finance sector, a global bank fails to coordinate its compliance efforts across different jurisdictions. As a result, it inadvertently violates newly established anti-money laundering regulations in one country while attempting to comply with privacy laws in another, which results in severe penalties.

7. Weak partner ecosystem governance

For larger organizations, business operations may involve collaborative networks that consist of multiple partners, vendors, and data exchanges. The data sharing that then needs to take place in the resulting data ecosystem requires its own frameworks, policies, and practices to remain compliant, let alone functional.

Failure to govern data ecosystems correctly could do more than impact data quality and increase security risks, among other issues. Breakdowns of partner ecosystem governance can also destroy the organization’s ability to function, as increased oversight from regulatory bodies, hampered decision-making, and a sudden need to invest in advanced compliance technologies compound into widespread operational disruptions.

Example: An automotive manufacturer doesn’t extend its data governance practices to suppliers, which results in a data breach through a third-party vendor. This exposes sensitive design specifications to competitors, which results in a loss of competitive advantage and damages its brand reputation—and ultimately hurts the company’s bottom line.

8. Cultural misalignment and resistance to change

Some data governance challenges are less technical and more personal. For instance, change adoption and cultural alignment often contribute to governance issues, too. This is especially true when it comes to an organization’s natural resistance to change. In addition, the all-too-common tendency for confusion or misalignment between departments can work against even the most airtight governance programs.

Often, these particular issues stem from internal cultural misalignment. When professionals within the organization begin to believe that leadership doesn’t consistently apply governance policies and procedures, trust erodes and adoption suffers. These issues can, in turn, quickly cloud governance-related communication and potentially lead to key departments devaluing critical governance policies over time.

Example: Despite a pharmaceutical company’s employees’ shared mission and values, cultural resistance to adopting new data governance practices easily causes problems. For instance, when key researchers opt to use non-standardized data collection methods, the validity of potentially life-saving clinical trial data becomes compromised, severely hampering progress and delaying drug approvals down the line.

9. Insufficient technology enablement

The tools that professionals use in different departments often prove to be their own most significant source of data governance challenges—especially if data leaders don’t vet, implement, and integrate the proper mix of technologies to support ongoing data governance initiatives.

Without the proper “mix” of tools, data teams often rely too heavily on manual, inconsistent, and scale-averse processes. At the same time, leaders may find it daunting to navigate the data governance vendor landscape or seamlessly integrate new tools into the organization’s data environment.

Example: Due to recent budget cuts, a newly formed government agency aims to minimize its costs. However, leadership opts to adopt outdated data management tools without fully assessing long-term governance needs. As a result, the agency’s data professionals struggle with data integration across departments, which leads to fragmented reporting, persistent data silos, inefficient public services, and increased operational costs.

Ultimately, each of these nine issues can create significant problems on its own—but together, they have the potential to damage a business beyond repair. That’s why, when considering how to address common governance issues like those above, data leaders should endeavor to prioritize strategic approaches that resolve multiple governance challenges at once.

Key strategic considerations for overcoming data governance challenges

Every data governance challenge warrants a thorough understanding because each one can trigger wide-ranging issues on its own. However, effectively addressing these challenges requires tailoring solutions to the specific industry, business model, and organizational structure in question. Failure to account for this context means that even well-intentioned efforts will lead to diminishing returns.

That said, some strategic considerations can prove especially beneficial to data leaders since, more so than others, they can address the root causes that many of these challenges share. The following considerations in particular map to these root causes while offering benefits across multiple problem areas:

Build governance on clearly defined roles and responsibilities

It’s hard to overstate the impact and ongoing benefits of a centralized accountability structure based on straightforward RACI and related matrices. This approach does more than simply prevent role ambiguity and enable cross-functional collaboration. It also supports complex data governance needs, like those related to partner governance, as it extends accountability frameworks to third-party vendors through contractual data handling agreements.

Additionally, cultural alignment emerges more holistically when teams understand their specific governance contributions through regular competency mapping exercises.

Create a data-driven culture with stakeholder support

Culture doesn’t change on its own. For governance initiatives to take root, executive backing needs to be an active force, not just a rubber stamp. After all, without strong stakeholder support, even the most well-intentioned data initiatives stall out in the face of competing priorities, lack of clarity, or plain old inertia.

That’s why data leaders should treat executive sponsorship as an ongoing effort, not a one-time buy-in. This means ensuring that key stakeholders stay aligned, governance priorities remain visible, and teams get the training they need to operationalize best practices. Without this level of engagement, governance risks becoming an abstract concept rather than a functional part of how an organization works with data.

Adopt flexible technologies and governance processes

Governance is not a “set it and forget it” function—especially as organizations scale, regulations shift, and business needs evolve. The risk of rigid governance frameworks is that they tend to break due to the chaos of real-world conditions. Instead, successful data governance best practices require an iterative approach that embraces flexibility in both process and tooling.

For data leaders, this should involve investing in governance solutions that scale with their business, automating governance workflow processes wherever possible to reduce friction, and integrating technologies like AI to strengthen both compliance and data quality. 

Just as importantly, however, this also requires a mindset shift. Governance isn’t a one-time project—it’s an ongoing, adaptive process that continuously refines itself based on real-world feedback.

Ultimately, in combination, the above considerations create a closed-loop system where accountability frameworks guide human decisions, policy orchestration manages regulatory complexity, and AI platforms automate operational governance.

A new way of thinking: Counteracting data governance challenges with a shift-left approach

Data governance regulations may be the same across industries, but the challenges that data leaders face are anything but. Each organization’s mix of stakeholders, systems, and priorities means that governance hurdles will often take different shapes. 

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That’s why the key isn’t just compliance—it’s building a data governance framework that actually works in the real world.

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Strategic considerations like those above can serve as a solid place to start, but to take their intended effects even further, data leaders should ultimately look to shift-left governance and data contracts. These provide a proactive way to manage data at its source and keep quality high and compliance effortless. After all, the more that data leaders bake strong governance into data processes from the start, the less reactive firefighting teams have to be later on.

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That’s where Gable comes in. By keeping these governance challenges in mind, this data contract platform helps organizations like yours bake governance into their data pipelines from the start. That way, you can more easily scale, adapt, and maintain visibility across complex systems.

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Ready to get started? Sign up for Gable’s product waitlist today to learn more.