The Benefits, Risks, and Realities of Silo-Busting

The term “silos” comes up fairly regularly in my professional life. Here’s one example:

I recently spoke with a group of data leaders, and one story stood out. A chief data officer, new to their role at a large company, expressed frustration over the sheer number of BI tools scattered across their org. They’d inherited a situation where, rather than a unified approach, small groups had each adopted their own preferred tools for reporting and analysis.

It was a big problem. There was a sprawling, redundant collection of tools, each chosen for its unique functionality because existing options didn’t quite meet users’ needs. Without robust governance, this fragmentation meant that each group was building its own version of the truth with siloed data, making alignment difficult and causing undue confusion.?

This unsustainable patchwork of BI tools? It’s silos.

Types of Silos

Data silos come in many forms, including:

• Technical silos from data infrastructure or security barriers, often surfacing as basic access issues.
• Organizational silos caused by a lack of centralized data leadership, with small teams scattered across departments, leading to skill gaps, redundancies, and inconsistency. (See my article about data org structures here.)
• BI tool silos arise when multiple tools are used for reporting and analysis without central oversight, as I illustrated earlier. This scenario can lead to duplicated effort and under-resourced tools that become neglected and inefficient.
• KPI silos crop up when different teams create their own metrics that don’t align with the company’s overall strategy, leading to inconsistent performance measurements and conflicting decisions - plus that dreaded question: “Hey, why don’t these numbers match?”

• Communication silos form when data-producing and data-consuming teams lack sufficient communication, limiting knowledge sharing and collaboration. Poor communication erodes partnerships, stunting the data team’s influence and impact.

Benefits: The Case for Busting Silos

It’s easy to see why companies want to break down silos. The benefits include:

• Efficiency: Silos hinder consistent decision-making and complicate workflows. Breaking them down streamlines operations.
• Cost savings: Removing redundant systems can save money by consolidating tools and reallocating resources.
• Consistency: Unified data is easier to manage and govern, which builds trust among stakeholders. As I’ve written about here, trust is key.
• Collaboration and innovation: Busting silos can help cross-functional teams collaborate more effectively. A broader view of the data landscape can spark new ideas that wouldn’t emerge in isolated silos.

Risks: How Silo-Busting Can Backfire

Despite its benefits, silo-busting has its risks:

• Coordination tax: Consolidating efforts requires more meetings and buy-in, adding overhead and slowing down decision-making.
• Loss of partnership: Centralizing can distance the data team from day-to-day business needs. Without close partnership, data folks may miss important details that affect the relevance and actionability of insights, leading to a decline in trust and perceived value.
• Restricted autonomy: Unified tools and structures may limit how teams collect, manage, and analyze data for their specific needs. This can frustrate stakeholders, especially those with unique requirements that don’t fit within standardized solutions.
• Incentivizing workarounds: Standardization, while efficient, can lead to generalized approaches that don’t fully address the needs of different teams. This lack of customization often sparks workarounds and shadow BI, diminishing the value of a unified approach.
• Creating bottlenecks: Routing all data work through a single team can slow processes and reduce responsiveness. In turn, longer cycles for insight generation reduce agility and limit the business’s ability to adapt to new needs.

Realities: Tips for Silo-Busting

If you’re dealing with silos, here are some practical tips:

• Start by understanding why: Examine why silos exist before taking action. Building in flexibility while maintaining consistency is key - otherwise, you risk a vicious cycle where silos are dismantled only to reform.
• Balance standardization and agility: Foster a culture that balances governance with innovation. Conduct regular reviews to help identify where workarounds or frustrations exist. Keep communication open with stakeholders to surface potential issues early.
• Ensure top-down support: Clear guidance on approved KPIs, tools, and structures helps prevent unnecessary workarounds and duplication. Data leaders must align their teams around a shared vision and direction.
• Use data literacy: Educate teams on existing solutions to empower collaborative work and avoid inadvertently bypassing established systems. (Read my article on data literacy here.)

What are your challenges with data silos? I want to hear your stories and solution ideas!