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7 barriers that can hinder your data governance in the cloud
The cloud promised agility, scale, and efficiency, and it delivered. The problem is that, in this movement, many companies accelerated more than they could see.
With each new integration, automation, or API layer, data multiplies, transforms, and begins to exist in places that are not always under total control . And the result is governance that tries 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 point to a lack of data governance as the main obstacle to advancing their artificial intelligence initiatives. This is a clear symptom that the problem is not a lack of data, but rather a lack of clarity about it.
These visibility gaps don't arise from negligence, 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 sections, we will explore the 7 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 amidst the dynamism of the cloud.
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As companies expand their cloud ecosystems, data ceases to have a single destination . It moves between providers, integrations, and APIs, transforming and replicating at rates that often escape the radar of teams.
It is in this movement that one of the most critical barriers to modern governance arises: the loss of visibility into where the data actually is and how it circulates . When each environment adopts its own monitoring and control standard, without integration between views, the organization loses the sense of totality, and with it, trust.
The effect is silent but profound: duplicated data, redundant flows, and incomplete trails that weaken audits, reports, and the decision-making process itself. After all, it is not possible to protect or govern what cannot be seen in its entirety.
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 is sought is clarity , that is, the ability to understand the data in motion.
When this vision takes hold, governance stops reacting to incidents and starts anticipating risks . And from there, a new need arises: ensuring that rules and policies evolve at the same pace as this increasingly agile operation—the topic of the next section.
Data governance is often born with good intentions : defined policies, documented workflows, and implemented controls. But in many companies, it stagnates while the business advances. And when that happens, the rules cease to reflect reality.
Cloud environments are dynamic by nature : new systems are introduced, integrations change, and 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 areas. Gradually, governance ceases to be strategic and becomes bureaucracy .
Overcoming this barrier requires dynamic policies, reviewed and integrated into the operational flow, not manuals forgotten 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 progress .
When policies reflect the present, not the past, governance once again becomes a partner to the business . And with this more mature foundation, the next challenge arises: ensuring that distributed identities and access maintain the same end-to-end consistency.
In the cloud, each new system brings its own authentication model . When there is no unified identity strategy, control becomes dispersed : duplicate credentials, overlapping permissions, and untraceable access become commonplace.
This fragmentation creates another of the most critical vulnerabilities of modern governance: not knowing who accesses what , nor with what justification.
In a multi-cloud , where teams and providers constantly share data, the absence of a centralized Identity Management (IAM) model and principles such as Zero Trust opens the door to security flaws and gaps .
And the impact goes beyond technical risk. Without visibility into access, , and therefore to 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 this with the aim of reducing fragmentation and strengthening control.
When identity and access are treated as layers of governance , and not just security, data gains contextual protection, aligned with operations.
With access under control , the next obstacle arises: ensuring that data, even when well protected, maintains consistency across systems and clouds.
Even with advanced integrations and automations, it's still common for a company to have different versions of the same data circulating in different systems . A client with conflicting information between CRM and ERP systems, for example, is a classic symptom of inconsistency. And this is a "silent nightmare" for governance.
multi-cloud environments , there isn't always standardization in data update and synchronization flows . And small differences in integration models or delays in replication can generate distortions that multiply rapidly.
The impact is direct: reports become inaccurate, analyses lose credibility, and decisions are based on partial truths. In the long term, this undermines trust in the very source of the data, which are the organization's most important assets.
The solution lies in governance focused on data quality and 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 stop competing with each other and begin to converge, governance gains traction . And, with this consistent foundation, the next challenge appears: dealing with the hidden costs of keeping compliance and governance under control.
Ensuring regulatory compliance in cloud environments is expensive, and the real cost is rarely in the technology itself, but in rework .
Every time data needs to be reclassified, access reviewed, or a process manually audited, part of the IT budget is consumed by repetitive efforts that could be automated.
The problem is exacerbated when different areas treat compliance as isolated tasks, and not as a shared responsibility within governance. Without standardization, each department creates its own spreadsheets, controls, and evidence, generating redundancies, inconsistencies, and delays in audits.
This rework cycle not only increases costs but also compromises data reliability and operational agility . And in a scenario 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 is integrated into routine, and 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 discernment? Keep reading to find out!
Automation is essential for scaling governance, but when control starts operating on autopilot, the risk changes form .
Without supervision or context, automation can reinforce large-scale errors , applying outdated rules, misclassifying data, or propagating unauthorized access between connected systems.
This is the paradox of efficiency : what was created to reduce human error can end up amplifying it. This occurs mainly when automated workflows are not periodically reviewed , or when tools operate in isolation from the data strategy and business changes.
Automation is only effective when guided by purpose and 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 audit models, sample validations, and supervision based on quality indicators help ensure that automations maintain a balance between agility and compliance.
Governance maturity is not about automating everything, but about 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 does not adapt quickly becomes obsolete.
Many companies create solid governance models, but treat them as something ready-made and definitive. The problem is that, in the cloud, nothing remains the same for long , as new integrations, tools, regulatory requirements, and ways of using data emerge all the time.
When policies and processes don't keep up with these changes, governance loses its grip : controls cease to reflect real 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 own operation. And, in a scenario where data changes in minutes, this delay is enough to compromise reliability .
Avoiding this requires governance that evolves along with the business. This means frequently reviewing rules, 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 with the cloud and supporting decisions with security. Because, in the end, data only has value when the governance that guides it continues to evolve.
Data governance is no longer solely 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 have 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 balance is dynamic . Flows change, access evolves, contexts reconfigure themselves, and governance needs to keep up. Therefore, the companies that thrive in this environment are those that transform complexity into predictability , using technology not to rigidify 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 in a secure and strategic way.
If your company seeks to evolve in this direction, to see better, act with more precision, and transform complexity into clarity, talk to one of our specialists! Together, we can help you transform governance into an engine for growth, not an obstacle to innovation.
Even with the advancement of cloud solutions, data governance still raises many questions, especially about where to start, what to automate, and how to manage multi-cloud .
Below, we've compiled straightforward answers to some of the most common questions on the topic.
1) Where to begin with data governance in the cloud?
The first step is to map what exists, not what is "imagined" to exist. This means identifying where the data is located, who has access to it, and how it is used across systems and providers. From there, define simple but applicable policies, starting with access controls, data classification, and audit trails.
The secret is to start small, but with visibility: without understanding the data flow, there is no way to govern effectively.
2) Does automation replace human curation?
No. Automation is a support, not a replacement, for curation and human oversight. It helps standardize processes, reduce errors, and accelerate operational tasks, but it still depends on human supervision to ensure context and interpretation.
In governance, the role of people is to make sense of the data, validate exceptions, and adjust rules to the reality of the business. Automating without supervision is like driving with your eyes closed: the movement continues, but the risk increases.
3) multicloud feasible?
Yes, it's entirely feasible, provided the strategy is integrated. The most common mistake is trying to apply isolated policies to each provider, which fragments control. Ideally, tools and practices should be adopted that unify the management of identities, access, and metadata in a single layer of visibility.
Multicloud is n't the problem; the challenge is maintaining consistency in the rules and clarity about where each piece of data is located.
4) What is the biggest mistake companies make in cloud governance?
The biggest mistake is treating data governance as a one-off project, not as an ongoing process. Many organizations create robust policies but fail to review them as the business evolves. The result is outdated governance that no longer reflects real operations and loses relevance.
Effective governance is dynamic: it learns, adjusts, and evolves along with the company and its data.
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