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How to Go Beyond Git or Azure DevOps for Power BI (And Why You Should)

DevOps For Power BI

Many Power BI professionals search for answers to a familiar question: “How do I set up Git or Azure DevOps for Power BI reports?”

This question arises from the increasing demand for Power BI version control, source control, and automated deployment pipelines. But before diving into how, it’s essential to understand why this question matters.

Why Power BI Needs DevOps

Power BI was built to democratize data, empowering both analysts and business users to create reports and dashboards without relying on centralized IT teams. Power BI enables governed self-service at scale, unlike traditional BI platforms like Oracle BI, SAP, Cognos, or Strategy, which require specialized skills within a BI Competency Center (BICC). 

As a result, traditional BICCs are evolving into Centers for Enablement (C4Es), tasked with training, standardization, governance, and the delivery of certified data products. These modern BI teams typically manage data infrastructure (e.g., data mesh, semantic models, and data governance policies), while business users independently create visualizations and reports.

But this shift creates new hurdles:

  1. Lack of Oversight
    With BI teams removed from day-to-day report development, there’s little control or quality assurance over published content. Iterations made by power users often go undocumented and may introduce errors. Since these changes are not recorded, BI teams miss the insight into who changed what, why, and when.
  2. Limited Development Discipline
    Power users, often not trained in software engineering, tend not to follow best practices. This affects report performance, maintainability, and reusability.
  3. Explosive Growth of BI Assets
    The number of Power BI content creators is far greater than that of traditional developers. Without retention policies or lifecycle governance, organizations face report sprawl, making it challenging to find the right reports, let alone manage obsolete, duplicate, or unused assets.

This context answers the broader question: “Why do I need DevOps in Power BI?”
DevOps for Power BI is not just about Git integration or automating deployments; it’s about establishing repeatable processes for:

  • Source control and collaboration (PBIX versioning, Git workflows)
  • CI/CD deployment pipelines across development, test, and production
  • Artifact lifecycle governance (creation, approval, archiving)
  • Monitoring and auditing Power BI assets at scale

Since DevOps for Analytics is the practice of integrating development (analytics design and modeling) and operations (deployment, monitoring, and maintenance), it addresses the hurdle C4Es are facing and would provide the insights to tackle the issue they face.

What are the Reasons Behind The Lack of Successful Power BI DevOps implementation?

At the 2025 Microsoft Fabric Community Conference, a survey of over 100 participants revealed that 17% rated their DevOps maturity in Power BI as “barely existing,” 54% called it “basic,” and 29% described it as “moderate.” It was shocking to find out that 0% rated it “excellent.” 

Of this group, 60% are using Azure DevOps.

Why the disconnect? 

Organizations tend to select Azure DevOps or GIT as their Go-To tool. At first sight, this makes sense. While Azure DevOps is a separate service, Microsoft Fabric offers native Git integration that supports Azure DevOps Repos. 

However, AzureDevOps is not part of Fabric and is not specifically built for Power BI. Companies therefore face four major roadblocks to successful Power BI DevOps Adoption.

  1. DevOps Tools Are Built for Code Development, Not for Analytics
    Git concepts, such as merging, conflict handling, and branching, are unfamiliar to business analysts. Most citizen developers are confused by Git and lack software engineering training. It is not their world, and it shouldn’t be.
  2. Setup and Maintenance Are Labor-intensive
    Implementing DevOps for Power BI requires heavy administrative support, manual configuration, and ongoing troubleshooting. This slows down adoption and frustrates power users.
  3. Limited Power BI Artifact Support
    Many DevOps tools fail to support the full range of Power BI objects. When power users fail to check in their work, it exacerbates the situation, leading to incomplete metadata catalogs, limited auditability, and a lack of capabilities for consistency checks. Also, monitoring and alerting changes to critical tenant settings is not available.
  4. No Cleanup or Lifecycle Management
    Git and Azure DevOps do not offer a built-in way to handle report sprawl, apply retention rules, or clean up unused datasets, reports, and dashboards.

What’s the Best Tool to Implement DevOps for Power BI?

If you’re asking, “What’s the best tool for Power BI DevOps?”, consider this:
Motio’s Soterre delivers zero-touch version control for Power BI. It automatically versions every artifact: reports, semantic models, dashboards, and even tenant settings—and provides visual diffing between report versions. 

Soterre’s singular focus is to provide DevOps specifically tailored for Analytics Platforms, and its key features include:

  • Automated deployments and structured workflow-based releases
  • Integration with existing ticket systems like Jira or ServiceNow
  • Complete metadata cataloging to find DAX inconsistencies, execute a global search function, and the ability to mass update Power BI

Transform Power BI Development with Soterre DevOps

Struggling with PBIX versioning, deployment pipelines, or DevOps governance?
See how Soterre DevOps can simplify Power BI lifecycle management and bring control to your analytics environment.

Learn more HERE

Ready to streamline and automate your BI & analytics platforms?
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As the BI space evolves, organizations must take into account the bottom line of amassing analytics assets.
The more assets you have, the greater the cost to your business. There are the hard costs of keeping redundant assets, i.e., cloud or server capacity. Accumulating multiple versions of the same visualization not only takes up space, but BI vendors are moving to capacity pricing. Companies now pay more if you have more dashboards, apps, and reports. Earlier, we spoke about dependencies. Keeping redundant assets increases the number of dependencies and therefore the complexity. This comes with a price tag.
The implications of asset failures differ, and the business’s repercussions can be minimal or drastic.
Different industries have distinct regulatory requirements to meet. The impact may be minimal if a report for an end-of-year close has a mislabeled column that the sales or marketing department uses, On the other hand, if a healthcare or financial report does not meet the needs of a HIPPA or SOX compliance report, the company and its C-level suite may face severe penalties and reputational damage. Another example is a report that is shared externally. During an update of the report specs, the low-level security was incorrectly applied, which caused people to have access to personal information.
The complexity of assets influences their likelihood of encountering issues.
The last thing a business wants is for a report or app to fail at a crucial moment. If you know the report is complex and has a lot of dependencies, then the probability of failure caused by IT changes is high. That means a change request should be taken into account. Dependency graphs become important. If it is a straightforward sales report that tells notes by salesperson by account, any changes made do not have the same impact on the report, even if it fails. BI operations should treat these reports differently during change.
Not all reports and dashboards fail the same; some reports may lag, definitions might change, or data accuracy and relevance could wane. Understanding these variations aids in better risk anticipation.

Marketing uses several reports for its campaigns – standard analytic assets often delivered through marketing tools. Finance has very complex reports converted from Excel to BI tools while incorporating different consolidation rules. The marketing reports have a different failure mode than the financial reports. They, therefore, need to be managed differently.

It’s time for the company’s monthly business review. The marketing department proceeds to report on leads acquired per salesperson. Unfortunately, half the team has left the organization, and the data fails to load accurately. While this is an inconvenience for the marketing group, it isn’t detrimental to the business. However, a failure in financial reporting for a human resource consulting firm with 1000s contractors that contains critical and complex calculations about sickness, fees, hours, etc, has major implications and needs to be managed differently.

Acknowledging that assets transition through distinct phases allows for effective management decisions at each stage. As new visualizations are released, the information leads to broad use and adoption.
Think back to the start of the pandemic. COVID dashboards were quickly put together and released to the business, showing pertinent information: how the virus spreads, demographics affected the business and risks, etc. At the time, it was relevant and served its purpose. As we moved past the pandemic, COVID-specific information became obsolete, and reporting is integrated into regular HR reporting.
Reports and dashboards are crafted to deliver valuable insights for stakeholders. Over time, though, the worth of assets changes.
When a company opens its first store in a certain area, there are many elements it needs to understand – other stores in the area, traffic patterns, pricing of products, what products to sell, etc. Once the store is operational for some time, specifics are not as important, and it can adopt the standard reporting. The tailor-made analytic assets become irrelevant and no longer add value to the store manager.