Gable Blog | Data Silos

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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.
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.
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.
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.
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.
Cultural resistance to change and silo mentalities
Finally, human psychology trends toward data siloing as well, regardless of profession or experience level.

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.

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 begins to degrade.

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.

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.

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.

Data quality and consistency issues

When different departments independently manage isolated datasets, significant errors, discrepancies, and duplicate entries naturally emerge.

Example:
Due to a recent merger, an organization’s human resources and payroll departments now manage employee information in separate systems.

Increased operational costs and effort duplication

The operational dissonance that data silos create can certainly result in gaps in strategy, insights, and efforts.

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.

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.

Example:
Due to competing IT priorities, an education technology company that serves K–12 districts stores student performance data across separate systems that different product teams have built.

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.

Example:
At a mid-sized tech company, the product team develops a major feature update based on user behavior.

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.

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.

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