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Article summary: Data silos (data-specific versions of classic information silos) are exceedingly common in modern businesses. But that doesn’t mean you should ignore them—they have the power to bring even sophisticated data-driven organizations to heel if you leave them unchecked.
Storing and managing grain in a centralized location simplifies monitoring and handling, reduces losses, and improves efficiency. But in modern organizations, silos tend to be anything but beneficial, especially when they relate to the state of data.
Unlike physical silos—which sit out in the open and both preserve and protect—data silos in modern organizations often form unnoticed, coalescing organically through the decisions and structures of day-to-day business operations. As such, they can seem harmless enough. But as they accumulate across different systems, teams, and workflows, data silos can begin to undermine the teams and departments that give rise to them.
This means that you shouldn’t underestimate them. And to prevent this, data leaders need to ensure that they understand, analyze, and, ultimately, address them.
As the first part of this two-part series, below we’ll explore six common causes of data silos and the real organizational consequences that follow—especially when siloed data limits visibility, impacts collaboration, and hinders business leaders from making informed, strategic decisions.
Data silos: 6 common causes in modern organizations
Due to the scale and complexity of today’s data-driven organizations, data silos—while a straightforward concept—are rarely the result of a single factor. Instead, they often form due to a thorny combination of technological, organizational, and cultural dynamics.
While the following causes of data silos are quite common, it’s rare for a silo to result purely from just one of these issues in isolation. Below is an overview of six primary drivers of data silos—and how they often interact:
- Fragmented technology landscapes
Most organizations function through a combination of newer software-as-a-service applications and legacy systems that don’t natively integrate with each other. As a result, data can easily fall through the cracks, in a sense, becoming trapped within individual systems, platforms, and applications. Cross-departmental access and analysis can then become difficult since they require multiple data sources.
Additionally, disparate toolchains across departments can compound the siloing potential of tech landscape fragmentation. This is because, although they’re responsible for similar functions in the org, they don’t all use the same processes and tools. Customer relationship management (CRM) software and analytics platforms are common examples of this.
- Technical debt
Beyond the complications that a mix of old and new systems introduces, the age of an organization’s tech infrastructure can directly contribute to data siloing. Legacy systems on their own often impose rigid limits on flexibility and interoperability—characteristics that are increasingly essential for meeting the volume demands of modern data storage.
As a consequence, data professionals within organizations that experience substantial technical debt frequently struggle to consolidate or share data effectively across platforms. And when initiatives to upgrade or replace legacy systems are financially prohibitive, even well-intentioned workarounds and temporary process shortcuts can unintentionally deepen existing data silo issues.
- Organizational structure and the demands of growth
Growth is a good problem to have in organizations—but the sudden and sustained variety in particular can cause plenty of problems of its own, especially regarding data quality and management. This often happens because, when growth occurs, departmental leaders and managers often scramble to add new processes, systems, and databases to support their unique business needs.
As these attempts to organizationally compensate drive space between an organization’s operational organs, data management can become decentralized. This sudden negative space is particularly problematic in cases of mergers or acquisitions. That’s because, despite the unification that’s taking place on paper, siloing can become particularly rife as factions that once operated as separate entities bring their own data stores, workflows, and IT budgets under one roof, complicating the overall integration of essential company data.
- Security, privacy, and compliance concerns
An organization can also stay more or less the same size and still face increasing issues with data silos as their industry’s compliance and regulatory pressures grow and evolve. For instance, some industries require organizations to internally silo data to a certain extent to protect sensitive personally identifiable information (PII) or comply with regulatory requirements like the California Consumer Privacy Act.
For businesses that are operating in such industries—healthcare, finance, and government, most commonly—some amount of siloing will often be both necessary and legally justified. However, these necessities come at a cost, as they naturally hinder broader data accessibility, cross-departmental collaboration, and ease of use throughout the organization.
- Lack of unified data strategy and governance
In contrast to businesses that are operating in highly regulated industries, some organizations inadvertently foster data siloing due to a lax approach to their overall data strategy and governance. While the absence of centralized data management itself isn’t an issue—increasingly popular federated data governance models prove otherwise—organizations still require a cohesive, unified data strategy and governance in order to avoid major issues like data siloing.
As with other issues in this list, a lack of unification in this regard breeds fragmentation since business units, teams and departments—each with their own goals, budgets, and technical preferences—naturally develop different repositories, tools, and processes. Furthermore, as data standards, terminologies, and definitions grow disparate over time, the absence of a single source of truth makes data silos difficult to tear down, if not avoid entirely.
- Cultural resistance to change and silo mentalities
Finally, human psychology trends toward data siloing as well, regardless of profession or experience level. At the psychological level, when teams take ownership of their data, they tend to treat it as proprietary—they protect what they view as their own rather than sharing it across the organization, even when they understand the need to do so.
How data silos impact organizational success
While the above issues may be more or less endemic in your own organization, understanding all common causes of data issues is important because their individual impacts will ebb and flow organically as your organization matures.
Equally important is outlining how data siloing—in any amount and regardless of causal factors—concretely impacts the organization’s day-to-day operations. In short, these common causes don’t just lead to mere structure or theoretical concerns. Unchecked data silos can also deeply affect an organization’s ability to function.
Just as there tends to be no single common drive of data siloing within an organization, the consequences of siloed data tend to be both varied and interwoven. That said, the following effects are real and tangible and can lead to significant impacts to organizations’ business-critical aspects:
Data accessibility and visibility challenges
As data silos trap information—restricting its visibility, accessibility, and overall flow across an organization—access to valuable, real-time customer and business information, competitive insights, and context for acting on business opportunities begin to degrade. Over a relatively short period of time, this can make it difficult, if not impossible, for individuals, teams, and whole departments to perform even basic aspects of their jobs.
Example:
Imagine an in-house marketing team launches an aggressive campaign to target and increase new customers’ engagement and purchase frequency. However, the team lacks access to recent purchasing data from the sales department’s CRM. As a result, the marketing campaign includes customers who already buy and promote the product on a regular basis.
This ends up wasting budget and valuable resources. It may also harm the organization’s brand reputation with its most valuable customers, as the campaign negatively affects overall customer experience.
Poorer business intelligence and less reliable decision-making
Because data silos isolate relevant information across different sources, they can directly contribute to fragmented or inconsistent datasets. This prevents professionals in an organization from being able to trust their own data. It also inhibits business intelligence efforts, keeps leaders from making fully informed decisions, and limits an organization’s ability to respond to strategic opportunities in a timely, definitive manner.
Example:
Executives conclude that quarterly growth is strong in their organization after reviewing sales data from one key region that they’ve stored in an isolated system. Yet at the same time, newer data from other strategically significant regions—which they’ve kept separately—reveals substantial underperformance.
Because they lack a complete, holistic, and data-driven overview, leadership makes overly optimistic forecasts for the coming quarter, missing vital opportunities to correct course and reallocate resources.
Data quality and consistency issues
When different departments independently manage isolated datasets, like in the previous example, significant errors, discrepancies, and duplicate entries naturally emerge. Like pressure quietly building beneath shifting tectonic plates, the effects of inconsistent data standards and degrading data integrity accumulate over time—and often only announce themselves when organizations reach a breaking point. And much like an earthquake revealing long-hidden fault lines, the result is often a sudden, disruptive impact on reporting, analytics, and data platform reliability.
Example:
Due to a recent merger, an organization’s human resources and payroll departments now manage employee information in separate systems. Post-merger, as HR updates employee PII, they don't synchronize this information with payroll records. No one noticed this oversight until payroll incorrectly routes paychecks, which causes major employee dissatisfaction and operational disruptions. Leadership then must determine if compliance risks associated with tax reporting now exist.
Increased operational costs and effort duplication
The operational dissonance that data silos create can certainly result in gaps in strategy, insights, and efforts. But they can also have the opposite effect, in a sense, since silo-fueled disconnections or redundancies often lead directly to duplicated efforts.
These corrective needs—like manual information re-entry, reconciliation, and repeated data cleanup—can significantly increase operational and IT costs, stifle organizational productivity, and ultimately result in inefficiencies that waste large amounts of valuable time.
Example:
As part of its digital health platform, a large health insurance company introduces an app that tracks users’ moods and goals and connects them to a licensed therapist. However, when this app collects customer data, it stores it in a data warehouse that isn’t integrated with the company’s internal CRM, which support and onboarding teams both use.
As a result, users who report issues through the app find themselves repeating or re-explaining their concerns when contacting support directly. At the same time, internal teams must spend unnecessary time hunting down missing context, duplicating notes, and filing tickets to request manual data transfers between various data warehouses, data lakes, and on-premises systems.
Compromised compliance and governance efforts
The fragmentation that data silos create in data environments complicates data governance practices while making regulatory compliance significantly more difficult. This is because siloed data often exists beyond the bounds of data teams’ monitoring and control efforts, which makes it harder, if not impossible, for teams to ensure data security, quality, and integrity.
Furthermore, silos often result in inconsistent or incomplete data records, which can directly lead to failed audits, non-compliance, and potential reputational damage.
Example:
Due to competing IT priorities, an education technology company that serves K–12 districts stores student performance data, teacher-generated feedback, and usage logs across separate systems that different product teams have built. As a result, feedback from teachers ends up in a custom tool with limited export capability, while usage logs find a home in the company’s analytics system.
When a district administrator requests a full data export for routine internal auditing purposes, the company finds itself struggling to assemble a consistent, complete record. Due to the amount of work that reconciling the resulting gaps requires, administration has to delay its audit response, which triggers an internal review of the company’s data governance policies and puts its relationship with the district—and future contracts—at risk.
Stifled collaboration and company culture challenges
Finally, any factor within an organization that inhibits knowledge sharing and trust will impede both collaboration and data-driven cultures. This means that data silos—and the mentality of self-sufficiency and aversion to teamwork that they support—negatively impact the synergy and innovation between different teams. The isolation that data siloing creates can also reinforce internal divisions, which make it harder for teams and departments to align on key organizational goals.
Example:
At a mid-sized tech company, the product team develops a major feature update based on user behavior. However, the team tracked this behavior within its own analytics environment. Unfortunately, this means that the marketing team—which relies on a separate CRM and campaign tools—ends up building a go-to-market strategy for the feature launch that it bases on outdated assumptions about the company’s main user needs.
The feature still launches, but adoption is much lower than anticipated since users end up confused about the feature’s value.
Next steps: Benefits of addressing data silos and how to start
Whether they’re due to different systems, legacy infrastructure, or a lack of alignment across teams, data silos clearly have the potential to affect everything from day-to-day business operations to an organization’s long term viability. And as you saw above, when data becomes fragmented, it quickly shifts from being a vital asset to a very real liability.
For business leaders, this means that silo-related stakes extend beyond mere inefficiencies and missed insights. The longer teams allow silos to persist, the harder it becomes for them to reverse their effects, unify data assets, build trust across teams, and re-enable stakeholders and leaders to make decisions with confidence.
The second part of this two-part series will review the benefits of keeping data silos at bay and then explore key ways to break silos down to keep organizations agile and flexible—and able to coordinate, as necessary.
Chad Sanderson
July 8, 2025
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