Breaking Down Data Silos: Essential Considerations

Article summary: This second article of Gable’s two-part series on data silos shifts focus from the common causes and complications they create for company data to the value of breaking them down between business units—and the strategic imperatives data leaders should prioritize to make that possible.

But as the previous piece on data silos highlighted, just because silos are old doesn’t mean data leaders can afford to underestimate them. Quite the contrary—increasingly, they can stifle the high stakes digital transformations that many modern orgs are relying on to stay competitive.

This is due to how easily the inaccurate, duplicative, inconsistent data that silos produce can cripple operational performance, stifle informed decision-making from leadership, and, over time, lower an organization’s trust in its own information. Therefore, in this second article on data silos, we’ll quickly review the benefits of breaking down silos in the first place before covering four organizational imperatives that data leaders can focus on to do so. 

Key benefits of breaking down data silos

So what do organizations stand to gain when they dismantle these silos? 

Before we explore how to do it, let’s first look at the tangible benefits of doing so effectively:

Improved data accessibility and visibility

When data is securely accessible across the organization, leaders can gain and maintain a holistic view of operations, performance, and their customers. On the whole, this enables faster, more accurate reporting and empowers leaders to identify internal bottlenecks, spot trends, and respond more proactively to market shifts. Often, this also results in more effective planning and strategy, as well as improved customer satisfaction. 

Streamlined compliance and governance

Regardless of whether organization data is centralized or federated, data teams that implement standardized policies and control reduce ongoing risks of costly compliance violations and data breaches. This not only protects the organization from legal and reputational harm, but this standardization also forms the foundation of trust—both internally across departments and roles and externally between customers and partners. 

Higher data quality and increased consistency

Unified data environments also prevent the inefficiencies, inconsistencies, duplicate data, and workarounds that are endemic to silo-plagued organizations. This unification helps everyone ensure that they’re working with the same high quality information, which boosts confidence in analytics and reporting, supports customer engagement, and reduces occurrences of risk and error. 

Bolstered business intelligence and decision-making

For data-driven decision-making to be sound, the data it’s based on needs to be sound as well. When data teams actively break down silos, they ensure that leaders and stakeholders have direct access to comprehensive, real-time insights from across the business. This leads to more accurate forecasting, improved resource allocation, more sound business decisions, and the ability to better identify new revenue streams and cost-saving opportunities. 

Holistically, this access and trust also fosters more robust internal cultures and supports accountability, transparency, and shared expectations. 

Enhanced collaboration and organizational agility

Finally, when teams trust that they’re working with the same high-quality data, cross-functional collaboration becomes easier and more productive. That way, barriers between departments are easier to keep to a minimum, projects run into less operational friction, and teams can pivot more quickly when new opportunities or threats emerge.

Ultimately, each of these benefits carries real weight on its own, whether it means strengthening data security and regulatory practices, helping legacy systems integrate more seamlessly into modern data ecosystems, or simply improving customer experience. But together, their collective impact makes the effort to break down silos—and keep them from reforming—more than worthwhile.

That’s why data leaders can’t afford to ignore the causes of siloing within their organizations. Therefore, they should approach the problem strategically and with the right imperatives in mind.

Four organizational imperatives for breaking down data silos

Now that the benefits of breaking down data silos are clear, it’s time to focus on what it takes to bring them down. Especially for data leaders who are looking to make sustainable progress in this respect, breaking down these silos cannot be a passive process. 

Here are four basic yet potent imperatives that can help data leaders begin making productive and lasting progress:

1. Establishing data governance as a foundation

First, data leaders should firmly ground any serious data silo elimination efforts in strategic data governance. This is because governance efforts and frameworks form the operational and authoritative understructure that the tactics and tools that break silos down require. For this reason, data leaders must embrace ongoing governance efforts as the most crucial aspect of managing their organization’s data assets. 

By prioritizing the ongoing implementation of strategic data governance, data leaders avoid the common trap of focusing on the technical challenges and limitations that may be all too familiar. Instead, by holistically focusing on both the human and technical aspect of governance efforts, leaders can address the important structural and cultural barriers that so many data silos naturally emerge from. 

However, governance efforts’ ongoing potency in breaking down data silos rests on two things: securing long-term executive support and establishing clear accountability structures throughout the organization. To garner this support and structural alignment, data leaders must make it clear that data governance frameworks and efforts don’t exist solely for their own sake. Quite to the contrary, their primary goal is to solve business problems and deliver on key operational outcomes. 

This, in turn, requires leaders to form a dedicated committee that includes key representatives from different departments who, together, provide comprehensive oversight and clearly define roles and responsibilities for data stewardship across the organization. 

Frameworks that extend from these strategic governance efforts must also include both technical and business dimensions. In practice, this means organizational data governance must establish policies for data quality, security, privacy, and compliance in addition to defining business rules and data definitions that foster consistency across teams and departments. 

But data leaders who build them in this way—from the ground up as company-wide, executive-supported initiatives—can begin breaking through the most silent and insidious organizational barriers that hinder data sharing and allow data silos to take root.

2. Unifying data architecture through a modern integration

As governance efforts work to temper data silos’ human drivers, data leaders must build that foundation to tamp down technical enablers, which involves implementing a unified data architecture in their organization. In practice, that architecture should be capable of physically and logically connecting various sources of data—especially those that are disparate or fragmented—while maintaining optimal performance and security standards.

With the operative goal of breaking down data silos in mind, data integration approaches such as data fabric provide an approach to architecture that facilitates the end-to-end integration of an organization’s data pipelines, data flows, and cloud environments using intelligent and automated systems. As such, it helps data leaders address the technical reality of modern orgs: despite the positive influence of governance efforts, some departments will remain invariably inclined to purchase their own technologies or adopt different systems or data platforms that may be incompatible with or disconnected from others. 

This is why a complementary data mesh approach to data fabric architectures can be instrumental in helping data leaders effectively break down data silos—data mesh decentralizes data management and ownership within individual business domains. Each decentralized team then should treat data as a product—meaning they’ve become directly responsible for maintaining their share of data quality in the overall ecosystem—to ensure its quality, usability, and discoverability for the organization at large. 

Depending on the industry they’re operating in, leaders may also want to invest in master data management (MDM) practices to ensure maximum consistency across systems. By producing and refining workflows that add and streamline processes to guarantee data handling across an organization, MDM practitioners create a unified, single source of truth—a “golden record,” as some call it—that can consolidate key enterprise data assets. This further eliminates the confusion and inconsistencies that so often characterize organizations with inconsistent and siloed data environments.

3. Fostering cross-functional data culture and literacy

After strategic governance and architecture efforts have begun to control many of the tech and human drivers of data silos, data leaders can focus their efforts on company culture. This is often one of the most challenging yet critical dimensions of silo elimination that data leaders will face, as it requires meaningful cultural change—but it’s essential for sustainable progress.

This great importance is largely due to data literacy—the ability and mindset of employees across the organization to read, understand, and use data to solve challenges, drive innovation, and create value, both in their roles and cross-functionally as part of different teams and departments. Organizational leaders need to embrace the reality that they must actively build and support data literacy as opposed to expecting that it will somehow develop and thrive on its own, especially in data-driven orgs.

Leaders can and should do more to bolster data literacy among teams and departments. This means actively modeling the “data as a product” mentality they expect to see in others and potentially incentivizing and rewarding data behaviors that fuel a more pragmatic, productive, and data-driven culture. 

Developing cross-functional data literacy—especially to the level that it can contribute to silo-busting—also often requires establishing a comprehensive training program that extends beyond technical teams to include business stakeholders at all levels. These programs foster a greater understanding and appreciation of data governance efforts, such as treating data as a product, in addition to supporting ongoing data privacy, security, and compliance requirements.

The most effective programs, however, do more by helping employees understand how data connects to their specific roles and business outcomes while confronting the common inclinations for teams and individuals to view their data a proprietary asset, not an organizational resource. That, along with leaders in the organization expecting data sharing and collaboration to be the norm rather than the exception, is what makes these programs truly effective.

4. Enabling data democratization and self-service data access

To complement and, ultimately, bolster ongoing governance, architecture, and cultural efforts, data leaders should ensure that they’re actively democratizing data in their organizations. True democratization—which involves strategic employment that leaders support with healthy data literate cultures—means giving employees throughout the org the ability to access data appropriately, not just the tools and training they need to understand it. 

As part of ongoing democratization efforts, leaders should also prioritize self-service capabilities and provide business stakeholders direct access to the integrated data they need while maintaining all appropriate governance controls. However, in doing so, data leaders must also understand that self-service success requires providing key stakeholders with intuitive interfaces and automated data preparation to remove barriers for less technically savvy users. 

Finally, to ensure that they’re beneficial in breaking down data silos, democratization strategies must involve comprehensive data cataloging and discovery capabilities that help them quickly and efficiently locate necessary data sets and assets. In larger organizations or those that deal with relatively large data volumes, efficient cataloging that supports seamless data self-service in this way actively counters another major contributor to siloed information—where valuable data does exist somewhere but remains all but inaccessible since the right people are unable to access it when necessary, if at all.

It’s important to note, though, that while the four imperatives above can function as a solid foundation, leaders should still treat them as the beginning of any silo-breaking initiative, not the end. To keep silos from re-emerging, organizations must continue evolving—especially by moving data practices closer to their sources.

Breaking down data silos with continuous improvement and shift-left thinking

Once they’re working in concert, governance, integration architecture, culture, and data democratization efforts make it exceedingly difficult for data siloing to thrive, let alone exist, in a data-driven organization. However, taking this a step further—by shifting data left and introducing data contract implementation across the organization—can strengthen these efforts even more.

This is because, in the end, breaking down data silos and preventing them from recurring requires an ever-evolving, multi-dimensional approach, one that’s robust enough to address strategic, technological, cultural, access, and operational challenges simultaneously. But as shift-left efforts are proving to an increasing number of data leaders, silo-related issues require a holistic solution that’s located as close to data sources as possible. 

To learn more about what this means, why it matters, and how you can add shift left data thinking to your own organization’s efforts, dive into The Shift Left Data Manifesto, which Gable’s own Chad Sanderson has written specifically for data leaders, just like you.

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