How to Improve Collaboration Between Your Data Tools: From Silos to Synergy
Today’s data teams work with more tools than ever before. From data transformation and modeling to governance and visualization, the modern data stack is powerful—but complex.
While each tool might excel on its own, disconnected systems lead to real business costs:
Miscommunication between teams
Duplicate efforts across platforms
Inconsistent metrics and reporting delays
These gaps aren’t just inconvenient—they slow down insights and weaken trust in data. As organizations embrace best-of-breed solutions like DBT, Microsoft Fabric, and Power BI, the ability to connect these tools into a seamless workflow becomes a competitive advantage.
In this article, we’ll explore how to improve collaboration between your data tools—and how to unify transformation, governance, and visualization using tools like DBT, Microsoft Fabric, and Microsoft Purview. We’ll also show how Plainsight helps organizations build integrated, scalable, and transparent data platforms where every tool works in sync.
Why Collaboration Between Data Tools Matters
In the past, many businesses relied on all-in-one data platforms—rigid, monolithic systems where everything lived under one roof. But as use cases evolved and new tools emerged, most teams moved toward a modular, best-of-breed approach. This allows for greater flexibility, but it also creates a new challenge: ensuring all tools communicate effectively.
Without strong integration between transformation, modeling, governance, and visualization tools, data teams face recurring issues:
Duplication of work: The same logic or transformation is recreated in different tools by different people.
Gaps in governance: No single source of truth for metadata, lineage, or access control.
Inconsistent metrics: KPIs defined in DBT don’t always match what users see in Power BI dashboards.
These challenges are more than technical. They cause delays, misaligned reporting, and lower confidence in data-driven decisions.
True collaboration between data tools unlocks the full value of your tech stack. When models, metadata, and logic flow seamlessly from one layer to the next, your team spends less time on rework and more time delivering insights.
Key Tools That Enable Collaboration
To build a collaborative, high-performing data ecosystem, you need tools that are open, interoperable, and built for integration. Here’s how DBT, Microsoft Fabric, and Microsoft Purview work together to streamline the data lifecycle.
DBT: The Transformation Layer
DBT (Data Build Tool) is the engine behind modern data transformation. It allows data engineers and analysts to build data models using SQL, version them with Git, and document them with built-in metadata tools.
Instead of hiding transformations inside a reporting tool or manual ETL scripts, DBT makes logic transparent, reusable, and testable—an essential foundation for collaboration. Teams can define business logic once and confidently push it downstream to other tools like Power BI.
Microsoft Fabric: The Unified Data Platform
Microsoft Fabric brings together data engineering, data science, data warehousing, and business intelligence in one connected experience. It acts as the central nervous system of the modern Microsoft data stack.
Fabric integrates directly with DBT and OneLake (Microsoft’s unified storage layer), allowing teams to build, store, and visualize data—all in the same platform. With shared semantic models and native Power BI integration, it enables a consistent view of data across the organization.
Fabric simplifies complex workflows and makes it easier for multiple teams—data engineers, analysts, and business users—to collaborate in real time.
Microsoft Purview: The Governance Backbone
While DBT and Fabric handle modeling and analytics, Microsoft Purview ensures everything stays secure, documented, and governed.
Purview acts as the metadata layer and data catalog, automatically scanning your data estate and mapping lineage from source to dashboard. It provides visibility into where data comes from, how it’s transformed, and who has access.
This transparency is critical in multi-tool environments, where data flows through many systems. Purview connects with both DBT and Fabric, allowing teams to trace transformations, audit access, and ensure compliance without slowing down productivity.
Together, DBT, Microsoft Fabric, and Microsoft Purview offer a robust foundation for tool collaboration. In the next section, we’ll explore how this stack works in action—from DBT models to Power BI dashboards—and how Plainsight helps bring it all together.
Real-World Collaboration: From DBT to Power BI
One of the most common collaboration gaps in the modern data stack happens between transformation and visualization. Business users rely on dashboards in tools like Power BI, but the logic behind those dashboards—filters, joins, business rules—is often built elsewhere, out of sight.
DBT bridges this gap by acting as the transformation layer, where your core data models are built and tested. But its real power lies in how those models can flow seamlessly into Power BI, so your business users don’t just see the data—they see trusted, reusable logic behind the numbers.
How It Works
When DBT is integrated with Microsoft Fabric and Power BI, models created in DBT can be exposed as semantic models—shared definitions of metrics, dimensions, and relationships. These models become the single source of truth for Power BI reports, so teams aren’t redefining the same KPIs across different dashboards.
With this setup, business users gain clarity and consistency in the reports they consume, while data teams avoid duplicating transformations or writing custom DAX for every visual.
Version Control & Change Management
Another advantage of using DBT in this workflow is its Git-based version control. Just like in software development, changes to models are tracked, tested, and peer-reviewed before deployment.
This brings a structured workflow to your data projects:
Teams can collaborate on model logic in branches
Every change is documented and reversible
Conflicts are resolved through standard code reviews
When this version-controlled model is tied directly to Power BI via semantic layers in Microsoft Fabric, the result is a collaborative, transparent, and reliable data-to-dashboard flow.
Governance, Trust, and Transparency
In a growing data ecosystem, governance is what keeps everything connected—and trustworthy. Without it, collaboration can turn into chaos: different teams using different versions of the truth, limited oversight on who changed what, and compliance blind spots across tools.
This is where Microsoft Purview becomes essential.
Lineage and Governance at Scale
Purview offers automated data discovery, lineage tracking, and cataloging across your data environment. It shows you where data comes from, how it was transformed in DBT, and how it’s ultimately visualized in Power BI.
With end-to-end lineage, teams can:
Understand the origin and journey of any metric
Quickly identify and resolve data issues
Ensure compliance with internal and external regulations
Trust Through Metadata Sharing
In a multi-tool workflow, metadata is often fragmented. One team documents column definitions in DBT, another manages access policies in Azure, and another builds dashboards in Power BI.
Purview acts as a central metadata platform, allowing these tools to share and access the same definitions. This helps eliminate knowledge gaps and ensures that every user—whether technical or not—can trust what they see.
Avoiding Conflicts and Data Quality Issues
By embedding governance into the data workflow, Purview helps prevent the kinds of issues that erode confidence in data. For example:
Outdated fields being used in reports
Conflicting definitions of KPIs
Unauthorized users accessing sensitive datasets
With Purview, governance becomes proactive rather than reactive. Teams get the freedom to build and iterate, knowing there’s a secure, governed framework supporting their work.
In the next section, we’ll explore how Plainsight brings all of this together—strategically and technically—to help organizations build collaborative, modern data platforms that scale.
How Plainsight Enables Seamless Tool Integration
At Plainsight, we understand that the true value of your data stack lies in how well your tools work together. That’s why we focus not just on technology implementation, but on building connected, collaborative ecosystems that eliminate silos and drive faster, more reliable insights.
Step One: Strategic Assessment
Every project begins with a deep dive into your existing data landscape. We assess:
Which tools are in use (e.g. DBT, Power BI, Fabric, custom ETL)
Where integration gaps or redundancies exist
How data is currently modeled, governed, and consumed
This gives us a clear understanding of where collaboration breaks down—and where the greatest opportunities lie.
Step Two: Connected Pipeline Implementation
Once the gaps are mapped, we design and implement connected pipelines that unify your transformation, governance, and visualization layers.
This typically includes:
Integrating DBT with Microsoft Fabric to create reusable, version-controlled models
Leveraging OneLake as a shared storage layer
Aligning semantic models between DBT and Power BI to ensure consistency
Deploying Microsoft Purview to unify metadata, track lineage, and embed governance
Our approach ensures your team can move from raw data to trusted dashboards—without friction, duplication, or data loss.
Step Three: Enabling Collaboration at Scale
Technology is only half the equation. We also help implement collaboration frameworks that allow your teams to work together more effectively:
Shared Git workflows for versioning models
Automated CI/CD pipelines for DBT and Power BI
Clear documentation standards and ownership models
Centralized governance policies with Purview
The result is a data platform where engineers, analysts, and business users can collaborate in real time, confident that they’re always working from a single source of truth.
Final Thoughts
The future of data isn’t just cloud-native—it’s collaborative.
As businesses adopt more specialized tools across their data stack, the risk of fragmentation grows. Without a unified approach, organizations face slower delivery cycles, inconsistent insights, and governance blind spots.
Now is the time to bring order to the chaos. With platforms like DBT, Microsoft Fabric, and Microsoft Purview, it’s not only possible—but practical—to connect your tools into a streamlined, governed, and flexible environment.
To do it right, avoid the most common pitfalls:
Fragmented ownership where no one owns end-to-end workflows
Tool silos that force duplicate work
Unclear governance that undermines trust in your data
Instead, embrace interoperability as your foundation for scale. When your tools—and your teams—are in sync, you can move faster, make smarter decisions, and scale with confidence.
Ready to align your tools, your teams, and your data strategy?
Plainsight helps organizations unify their modern data stack—connecting DBT, Fabric, Power BI, and Purview into a seamless, high-performing ecosystem.
👉 Book a free discovery session with our data experts to assess your current setup and identify integration opportunities.
Make your data stack your competitive edge—with Plainsight.
