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 Temp Mail 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:
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.
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.
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.
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