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Fabric sprawl: From chaos to clarity in modern data environments | Baker Tilly
Article
Fabric sprawl: From chaos to clarity in modern data environments
April 14, 2026 · Authored by Marcus Radue and Curtis Stallings
As organizations rapidly adopt unified analytics platforms and pursue modern scalable analytics strategies, a new challenge is emerging alongside innovation: the unchecked growth of data assets, pipelines, reports and semantic models. This phenomenon, often referred to as Fabric sprawl, is not simply a scaling issue; it’s a signal that governance, visibility and structure have not evolved at the same pace as adoption.
Fabric sprawl begins with good intentions. Teams are empowered with self-service capabilities, departments move faster and barriers to insight are reduced. But over time, this flexibility can lead to fragmentation. Workspaces multiply, artifacts proliferate and environments become increasingly difficult to manage.
At its core, Fabric sprawl is the uncontrolled expansion of artifacts across an organization’s data ecosystem. Left unaddressed, it introduces inefficiencies, increases risk and ultimately undermines the very agility it was meant to enable.
Recognizing the symptoms
The signs of Fabric sprawl are often subtle at first but become more pronounced as environments grow.
Duplicate datasets and reports begin to surface across teams, each representing slightly different versions of the same logic. As these variations multiply, confidence in data accuracy declines. Compute capacity usage becomes inconsistent, often peaking due to redundant workloads running in parallel. Security gaps emerge as permissions are applied inconsistently across workspaces and items. At the same time, teams unknowingly recreate existing assets, leading to wasted effort and slower delivery.
While having these challenges within a centralized platform is preferable to managing them across disconnected systems, the impact is still significant. Without intervention, complexity compounds quickly.
Why addressing sprawl matters
Managing Fabric sprawl is not just about cleanup — it’s about unlocking efficiency and restoring clarity.
One of the most immediate benefits is cost reduction. Eliminating redundant artifacts and optimizing workloads frees up compute capacity that can be reinvested in higher-value initiatives. IT teams can shift their focus from maintaining cluttered environments to enabling strategic growth.
Equally important is the improvement in data governance. With fewer, more trusted assets, organizations can establish clearer standards, enforce policies more effectively and ensure that users are working from consistent, reliable data sources.
Ultimately, addressing sprawl strengthens both operational efficiency and decision-making confidence.
A practical framework: Know, remove, improve
Effectively managing Fabric sprawl requires a structured approach; one that combines visibility, governance and automation within a well-architected Microsoft Fabric environment. A three-phase framework — know, remove, improve — provides a practical and repeatable path forward.
Know: Establish Visibility
The first step is gaining a comprehensive understanding of the environment. Without visibility, any attempt to optimize or clean up is incomplete.
This involves building an inventory of all artifacts, analyzing lineage, tracking capacity usage and monitoring user activity. Identifying orphaned assets with no clear ownership or usage is particularly valuable.
Modern monitoring frameworks enable organizations to collect and store platform data over time, creating a historical view of activity and trends. Extending these capabilities to include personal workspaces, semantic model metadata and lineage relationships provides deeper insight into how data flows across the environment.
With this level of visibility, organizations can move from reactive management to informed decision-making.
Remove: Take action strategically
Once visibility is established, the next step is to act, but with intention.
Quick wins help build momentum. Low-value or unused artifacts can be identified, prioritized and addressed through a structured backlog. Communication plays a critical role in this phase. Users should be informed, given time to respond and supported through any necessary transitions.
A disciplined removal process typically includes restricting access, monitoring feedback, archiving assets when appropriate and ultimately deleting them. Automation can accelerate this process, especially when combined with insights gathered during the discovery phase.
The goal is not just to reduce clutter, but to build trust in the process and demonstrate value to the broader organization.
Improve: Build for sustainability
Cleanup alone is not enough. Without systemic improvements, sprawl will return.
The challenge lies in balancing user autonomy with organizational control. Overly restrictive environments limit innovation, while overly permissive ones create chaos. The solution is to implement guardrails that guide behavior without restricting productivity.
Artifact consolidation is a key step. Aligning assets with data governance frameworks ensures that ownership is clear, and duplication is minimized. Tagging strategies further enhance visibility by enabling consistent categorization, lifecycle management and compliance tracking.
Endorsements help establish trust. Promoted and certified assets signal reliability, guiding users toward approved data sources and reducing the need to recreate existing work.
Security must also be addressed holistically. Reviewing permissions across workspaces, items and storage layers ensures that access is appropriate and consistent. Role-based access controls and network-level protections strengthen the overall security posture.
Operationalizing through CI/CD and monitoring
Long-term success depends on embedding best practices into everyday workflows.
Implementing CI/CD processes introduces consistency and structure to development and deployment. Changes are tested, versioned and promoted in a controlled manner, reducing risk and improving reliability. This is especially important in environments where both centralized IT teams and decentralized users contribute to development.
Continuous monitoring is equally critical. Detailed logging and analytics allow organizations to answer key questions: Which assets are actively used? Where are performance bottlenecks occurring? Are resources being utilized efficiently?
By integrating monitoring into daily operations, organizations can proactively identify issues and continuously refine their environments.
At the enterprise level, the focus is on governance, standardization and creating a trusted source of truth. Departments require flexibility to build and share solutions tailored to their specific needs. Teams benefit from collaborative tools that support exploration, while individuals need the ability to create personalized insights.
Designing with these layers in mind allows organizations to support innovation at every level while maintaining control and consistency across the broader ecosystem.
From sprawl to strategy
Fabric sprawl is not just a technical challenge — it reflects how an organization manages growth, collaboration and data ownership.
By investing in visibility, acting with intention and implementing sustainable practices, organizations can transform sprawl into an opportunity. Instead of reacting to complexity, they can design environments that are scalable, governed and efficient.
The goal is not to limit growth, but to guide it, ensuring that as environments expand, they remain structured, reliable and aligned with business objectives.
How we can help
Addressing Fabric sprawl can feel overwhelming, especially in environments that have grown rapidly. That’s where a structured, experienced approach makes all the difference.
Baker Tilly helps organizations take control of their Fabric environments by starting with visibility — assessing current-state architecture, identifying duplication and uncovering inefficiencies. From there, we design and implement tailored strategies to clean up existing sprawl, including backlog prioritization, artifact rationalization and automated archiving processes.
Whether you’re just beginning to see signs of sprawl or looking to optimize a mature environment, the right approach can turn complexity into clarity and position your data platform for long-term success.
