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1. The cloud has accelerated everything, including invisible errors.
The cloud promised agility, scale, and efficiency, and it delivered. The problem is that, in this movement, many companies accelerated more than they realized.
With each new integration, automation, or API layer, data multiplies, transforms, and exists in places that are not always under full control . And the result is governance that struggles to keep up with a constantly changing ecosystem.
According to the report Survey: Data Quality and Governance Issues Hold Back AI (DBTA, 2024) , 62% of organizations cite a lack of data governance as the main obstacle to advancing their artificial intelligence initiatives. This is a clear symptom that the problem isn't a lack of data, but rather a lack of clarity about it.
These visibility gaps don't arise through carelessness, but as a side effect of speed . And so, governance, originally designed for stable environments, now needs to deal with elastic flows, transient integrations, and decentralized decisions.
In the following topics, we'll explore the seven most common barriers that arise in this scenario and understand how to overcome them so that data governance can once again fulfill its essential role : ensuring trust, traceability, and context amid the dynamism of the cloud.
Let's dive in!
2. Barrier #1: Lack of visibility between environments and providers
As companies expand their cloud ecosystems, data no longer has a single destination . It moves between providers, integrations, and APIs, transforming and replicating at rates that often escape teams' radar.
It is in this movement that one of the most critical barriers to modern governance emerges: the loss of visibility into where data actually resides and how it circulates . When each environment adopts its own monitoring and control standard, without integration between views, the organization loses its sense of wholeness, and with it, trust.
The effect is silent but profound: duplicate data, redundant flows, and incomplete trails that weaken audits, reporting, and the decision-making process itself. After all, you can't protect or govern what you can't fully see.
Overcoming this barrier requires continuous visibility. Data discovery and data lineage platforms help map the data lifecycle, showing its origin, transformation, and destination in near-real time. More than control, what's sought is clarity —the ability to understand data in motion.
When this vision takes hold, governance shifts from reacting to incidents to anticipating risks . And from there, a new need emerges: ensuring that rules and policies evolve at the same pace as this increasingly agile operation—the topic of the next section.
3. Barrier #2: Governance policies that don't keep pace with the business
Data governance often starts out well-intentioned : defined policies, documented flows, and implemented controls. But in many companies, it stalls while the business moves forward. And when this happens, the rules no longer reflect reality.
Cloud environments are dynamic by nature : new systems come online, integrations change, teams adopt different tools. If policies don't keep pace, they end up being ignored, replaced by operational shortcuts or isolated decisions .
This lag creates a dangerous misalignment: data begins to be used without the same rigor with which it was created. Access controls lose validity, quality parameters become obsolete, and reports begin to diverge between departments. Gradually, governance ceases to be strategic and becomes bureaucratic .
Overcoming this barrier requires living policies, reviewed and integrated into the operational flow, not forgotten manuals in shared folders. Automating the application of these guidelines, using context-based rules (who accesses, from where, and for what purpose), is what maintains control without hindering the pace .
When policies reflect the present, not the past, governance once again becomes a business partner . And with this more mature foundation, the next challenge arises: ensuring that distributed identities and access maintain the same consistency from end to end.
4. Barrier #3: Fragmentation of identities and access
In the cloud, each new system brings its own authentication model . Without a unified identity strategy, control is dispersed : duplicate credentials, overlapping permissions, and untraceable access become part of everyday life.
This fragmentation creates another of the most critical vulnerabilities of modern governance: not knowing who accesses what , or with what justification.
In a multicloud , where teams and providers constantly share data, the absence of a centralized identity management (IAM) model and principles like Zero Trust opens the door to security breaches and gaps .
And the impact goes beyond technical risk. Without visibility into access, and, therefore, ensure regulatory compliance
is also lost To overcome this barrier, it is necessary to consolidate identity governance as a central part of the data strategy , relying on solutions that apply federated authentication, dynamic permission policies, and continuous privilege review. All of this aims to reduce fragmentation and strengthen control.
When identity and access are treated as layers of governance , not just security, data gains contextual protection, aligned with operations.
And with access under control , the next hurdle arises: ensuring that data, even when well protected, remains consistent across systems and clouds.
5. Barrier #4: Inconsistent data between systems and clouds
Even with advanced integrations and automation, it's still common for a company to have different versions of the same data circulating in different systems . A customer with divergent information between CRM and ERP systems, for example, is a classic symptom of a lack of consistency. And this is a "silent nightmare" for governance.
multicloud or hybrid environments standardization in data update and synchronization flows . And small differences in integration models or delays in replication can generate distortions that multiply quickly.
The impact is direct: reports become inaccurate, analyses lose credibility, and decisions begin to be based on partial truths. In the long run, this undermines trust in the very source of data, which is the organization's most important asset.
The solution involves quality-oriented governance and data unification Master Data Management tools and automated validation help establish this "single version of the truth," reconciling records, metadata, and business rules across different environments.
When data ceases to compete with each other and begins to converge, governance gains traction . And with this solid foundation, the next challenge arises: dealing with the hidden costs of maintaining compliance and governance under control.
6. Barrier #5: Rising compliance and rework
Ensuring regulatory compliance in cloud environments is expensive, and the real cost is rarely in the technology itself, but in rework .
Each time data needs to be reclassified, access needs to be reviewed, or a process needs to be manually audited, part of the IT budget is consumed by repetitive efforts that could be automated.
The problem is compounded when different areas treat compliance as isolated tasks rather than as a shared responsibility within governance. Without standardization, each department creates its own spreadsheets, controls, and evidence, generating redundancies, inconsistencies, and audit delays.
This cycle of rework not only increases costs but also compromises data reliability and operational agility . And in a landscape of increasingly complex regulations, such as LGPD, GDPR, and ISO 27001, this fragmentation is unsustainable .
Overcoming this barrier requires integration between governance and compliance from the data source . Automating audits, creating continuous evidence trails, and applying standardized retention policies reduces manual effort and prevents human error. Thus, compliance ceases to be a cost center and becomes a natural consequence of well-governed processes.
When governance becomes part of the routine, not just a checklist , it becomes sustainable. And with costs under control, a new dilemma arises: how to ensure that automation brings efficiency without compromising human judgment? Keep reading to find out!
7. Barrier #6: Automation without supervision or context
Automation is essential to scaling governance, but when control starts to operate on autopilot, the risk changes .
Without oversight or context, automation can reinforce errors on a large scale , applying obsolete rules, classifying data incorrectly, or propagating unauthorized access across connected systems.
This is the paradox of efficiency : what was created to reduce human error can end up amplifying it. This occurs especially when automated flows are not periodically reviewed , or when tools operate in isolation from data strategy and business changes.
Automation is only effective when purpose- driven calibrated by human analysis. Therefore, it is essential to create mechanisms that maintain control over what has been automated and ensure that decisions remain aligned with the business context. Here, continuous auditing models, sample validations, and oversight based on quality indicators help ensure that automations maintain the balance between agility and compliance.
Governance maturity lies not in automating everything, but in knowing what should and should not be automated . When balance is achieved, the process becomes intelligent: predictable, scalable, and controllable.
And it is this balance that underpins the next point: the ability to evolve . After all, in governance, what doesn't adapt quickly becomes obsolete.
8. Barrier #7: Lack of continuous evolution in governance
Many companies create solid governance models, but treat them as something ready-made and definitive. The problem is that, in the cloud, nothing stays the same for long , as new integrations, tools, regulatory requirements, and data usage methods emerge constantly.
When policies and processes don't keep up with these changes, governance loses its grip : controls no longer reflect actual operations, indicators become outdated, and monitoring becomes merely a formality.
The risk is clear: the company believes it has control, but in practice, it's looking at an outdated snapshot of its operations. And, in a scenario where data changes in minutes, this delay is enough to compromise reliability .
Avoiding this requires governance that evolves alongside the business. This means reviewing rules frequently, adjusting policies to new contexts, and learning from failures and audits. Not to point out errors, but to continuously improve.
Maturity lies there, in treating governance as a living process that adapts without losing consistency. Companies that maintain this active cycle build stronger governance, capable of growing alongside the cloud and supporting decisions with confidence. Because, in the end, data only has value when the governance that guides it continues to evolve.
9. Why Stagnant Governance Is Valueless Data and What to Do About It
Data governance is no longer just about having control: today, it's about having vision .
In a scenario where everything changes in real time, the greatest risk lies not in the absence of technology, but in the lack of understanding of the data ecosystem itself. And as we've seen, this is where many strategies stagnate, when they confuse stability with security and lose the ability to adapt.
Governing in the cloud means accepting that equilibrium is dynamic . Flows change, access evolves, contexts reconfigure, and governance needs to keep pace. Therefore, companies that thrive in this environment are those that transform complexity into predictability , using technology not to harden processes, but to provide fluidity with traceability.
In short, it's not about monitoring , but about understanding. Not about limiting, but about sustaining growth with confidence.
At Skyone , we believe this is the new role of governance: to be an intelligent, adaptable, and integrated system that unites data, automation, and context to support decisions safely and strategically.
If your company seeks to evolve along this path, see better, act more precisely, and transform complexity into clarity, talk to one of our experts! Together, we can help you transform governance into an engine of growth, not an obstacle to innovation.
FAQ: Frequently asked questions about data governance in the cloud
Even with the advancement of cloud solutions, data governance still raises many questions, especially about where to start, what to automate, and how to handle multicloud .
Below, we've gathered straightforward answers to some of the most common questions on the topic.
1) Where to start with cloud data governance?
The first step is to map what exists, not what you "imagine" exists. This means identifying where the data is, who has access to it, and how it's used across systems and providers. From there, define simple but applicable policies, starting with access controls, data classification, and audit trails.
The key is to start small but with visibility: without understanding the flow of data, there's no way to govern effectively.
2) Does automation replace human curation?
No. Automation supports, not replaces, curation and human oversight. It helps standardize processes, reduce errors, and speed up operational tasks, but it still relies on human oversight to ensure context and interpretation.
In governance, people's role is to make sense of data, validate exceptions, and adjust rules to the business reality. Automating without oversight is like driving with your eyes closed: the movement continues, but the risk increases.
Is multicloud governance
Yes, it's entirely feasible, as long as the strategy is integrated. The most common mistake is trying to apply isolated policies to each provider, which fragments control. Ideally, you should adopt tools and practices that unify identity, access, and metadata management into a single layer of visibility.
Multicloud is n't the problem; the challenge is maintaining consistency in rules and clarity about where each piece of data resides.
4) What is the biggest mistake companies make in cloud governance?
The biggest mistake is treating data governance as a one-off project rather than an ongoing process. Many organizations create robust policies but fail to revise them as the business evolves. The result is outdated governance that no longer reflects actual operations and loses relevance.
Effective governance is alive: it learns, adjusts, and evolves alongside the company and its data.
