When we think of Power BI, the first thing that comes to mind for most is reporting, visualisations and its drag-and-drop interface for self-serve reporting, and that makes sense. But an integral part of any tool, not just Power BI, is governance. You can’t talk about Power BI adoption or implementation without considering a solid data governance strategy.
As I said above, the first thing people associate with Power BI is reporting and that is because in the beginning, that is exactly what it was. It was not an enterprise reporting tool from day one, for an IT or a dedicated reporting team, it was built for the business. A self-service data visualisation tool. Years back, so many elements were missing for it to be classified as an enterprise tool, such as large data models, incremental refresh, composite models, XMLA endpoints, to name a few. Now, while it has evolved into an enterprise-ready platform, it still holds onto its self-service roots. But if we do not implement the right guardrails such as ensuring proper usage, offering support, maintaining data integrity, and enforcing security and processes…. things can quickly spiral out of control.
What do I mean by spiral out of control? Well below are a few things that come to mind:
• A proliferation of workspaces, reports, semantic models, etc.
• Self-service not being a benefit, but a burden to the core reporting team.
• Everyone debating who has the right numbers.
• No distinction between enterprise and self-service.
• No internal community that offers support to end-users.
• Reporting team loses control.
• No best practices in deploying solutions.
• No processes for report, license, change requests.
You are in one of these two
Over the years, I have helped countless organisations with their Power BI adoption journey and ensuring they have a governance strategy. When I walk in, I typically find one of two situations:
Power BI Already In Full Swing: This is usually through organic adoption - which has its pros and cons. But this is when things start to get messy and the items listed above come to life. So, what’s the approach here? In short, centralising control. Not permanently, but just long enough to embed the right practices before slowly releasing control back to the organisation. And let me be clear, I am not saying to suddenly lock everything down or pull the plug. Oh please, do not do that. That is a sure way to piss off people.
Power BI Selected as Enterprise Tool: The organisation has gone through a process of selecting their tool of choice and Power BI was the winner. Meaning, the organisation is now looking for advisory support to ensure they set themselves up for success by having a governance strategy. This is obviously the less chaotic approach, but that does not mean it is smooth sailing. Here, I often see three main challenges. First, some teams want to over plan, trying to have every answer in place before moving an inch. Second, while they are on the right track by aligning with best practices, they often become overly restrictive in an effort to ensure everything is secure and governed, losing sight of what truly matters: empowering end users. Third, end users push back against the new tool. This is where the advantage of organic adoption comes into play. Users already want to use it! So, what is the best approach? It is all about understanding the end users, what worries them, what they love about their current tools, and what actually matters to them. Find small wins early and those will snowball into wider adoption. You’d be shocked at what I found working with various organisations about why some business areas wouldn’t use Power BI. Their concerns were misplaced... simple features that their current tools had but they believed Power BI didn’t. In fact, they have been in Power BI for a very long time. Understand your end users.
Each Organisation is different
It is important to understand that every organisation is unique. Even when they share similarities, the advice and recommendations for their enterprise Power BI journey should be tailored to their specific business goals and challenges. The organisations I have worked with vary widely. Whether it is their industry, specific pain points, data maturity, data culture, team structure, governance approach, objectives, data literacy or company size. However, despite these differences, I see the same common issues arise time and time again.
The Key to Success For All Organisations
The most important thing is having a solid plan. Can Power BI provide value and help organisations answer business questions? Absolutely. But rolling it out across an organisation should not be a big bang approach. I always recommend a phased rollout. Instead of deploying Power BI everywhere at the same time, start with a single business area. However, as I mentioned above and this is important to remember, this does not mean stopping everyone from using it already. Use this as a test, identify challenges, refine processes and address issues before moving to the next area. This way, you continuously improve as you scale, rather than firefighting after a full rollout.
Control vs Self-Service: Why Governance Needs Both
When creating a Power BI governance strategy, self-service analytics has to be part of it. Here is the thing, you cannot just lock everything down and be overly restrictive. That will only frustrate users, push them to find workarounds and create their own data silos outside of the core reporting team. This leads to delays in insights, disconnected reporting, and chaos. But on the flip side, you cannot just let it loose on everyone either. I have seen it, when self-service reporting is rolled out with no guardrails, it turns into a hot mess. Just because you give the business access to self-service tools and a vast data model does not mean they will instantly know how to derive insights or create meaningful reports. Without structure, you will end up with duplicate reports, conflicting numbers, security risks, and an overwhelmed core reporting team dealing with more requests than ever.
The key here is balance. We need to empower end users while also putting the right guardrails in place to ensure data is used appropriately and governance is maintained.
Do not overlook this. I see so many discussions about Power BI governance and self-service analytics, but the importance of supporting and enabling end users is missed. Governance is not just about control, it is about helping users get the most out of the data while ensuring accuracy and security. If we fail to provide the right level of guidance, training, and support, self-service will never work the way it should. We need to bring the people on this journey with it - not turn them against us. And that should be a core part of any governance strategy.
You should now understand that a balance is needed. So, let's get into the three Power BI deployment approaches.
The Power BI (MS Fabric) Deployment Approaches
Over the years, I presented this topic at various Power BI User Groups, Data Events, etc. So, I always pushed people to read the Power BI Adoption Roadmap documentation, now known as the MS Fabric adoption roadmap. For the deployment approaches, these now come under Content Ownership and Management. However, I like the term Power BI Deployment Approaches, so will stick to this.
I find the three deployment approaches are a great way to frame governance discussions, provide structure for Power BI adoption and help organisations decide how best to balance control and self-service. They assist the delivery of Power BI at the enterprise level. I almost use them as a roadmap on where to start and where to go. So yes, below is a description of each, but with the focus on ownership and management.
1. Enterprise BI
The first Power BI deployment approach and the most common starting point for organisations is Enterprise BI, previously known as Corporate BI. In this approach, the BI team or the equivalent of a core reporting team owns and controls everything. From Power BI semantic models to reports consumed by the business. Users are purely report consumers. But why should we start with Enterprise BI? This is a common question. The reason is foundation. Starting with Enterprise BI allows us to establish key BI principles that every organisation needs. Think, source of truth, version control, scalability, usability, performance and integrity.
The real value of starting with the Enterprise BI deployment approach is that it sets the foundation, putting us in the strongest position to eventually transition into self-service. But the key is to transition into Managed Self-Service. Staying in Enterprise BI for too long will frustrate end-users, especially those who were previously use to self-serving their own reports.
Before moving to Managed Self-Service, there are a few key things that should be in place to ensure a smooth transition. I plan to cover these in a future blog - so stay tuned or reach out!
2. Managed Self-Service
The next step towards self-service BI is Managed Self-Service BI, previously known as IT-Led Self-Service. This is where the business starts to take some control, but within defined boundaries. Within this approach, users can create their own reports, but they do not develop their own semantic models as this remains a responsibility of the core reporting team. Also, by users, this can mean everyone or selected individuals such as champions, it really depends.
Many people ask, why do we need this phase? If the goal is to get to full self-service, why not skip straight to Business Led? The answer lies in the challenges of both traditional BI and unstructured self-service BI. Historically, BI was fully centralised, IT controlled all reporting requests, ensuring data consistency but also causing bottlenecks, slow response times and frustrated users who found their own workarounds. However, skipping straight to Business-Led Self-Service introduces a different problem, inconsistent data models, lack of required skills, risk of incorrect reports being circulated and security risks when business users build solutions without governance.
By implementing Managed Self-Service, we avoid these issues. It's a middle layer that allows the business to be more agile, while still maintaining governance and control over the core data models. So, it offers a balance between both approaches.
Before moving to Business-Led Self-Service there are also some things that should be in place. Again, I plan to cover these in a future blog or just reach out!
Business-Led Self-Service
The final stage of Power BI deployment is Business-Led Self-Service. This is where the business not only builds reports but also takes full ownership of creating and managing their own semantic models.
Now, this is the phase where some people start to panic, and I think its justified. Handing over such control to the business can feel like a risky move. But let’s be clear, not every organisation or every team within an organisation needs to reach this approach.
For many organisations, only specific teams or champions will be capable of building their own models. The decision to move into Business-Led Self-Service should always be intentional, based on factors like the complexity of the data environment, the skills and maturity of the business teams, the level of governance and training with a strong internal community.
I would say for most, Managed Self-Service BI is enough. However, for others, a mix of all three models will make sense, depending on various factors. This is why Power BI governance needs to be flexible, allowing for a mix of Enterprise, Managed, and Business-Led Self-Service approaches.
Summary of Deployment Approaches
As we have covered the three deployment approaches above, I want to emphasise a few key points:
• Enterprise BI does not disappear: Even as organisations move toward Managed or Business-Led Self-Service, the fundamental BI objectives, governance, security, etc. must remain intact. Governance is an ongoing process, not something that is “completed” once self-service is introduced.
• Transitioning between models should be gradual: Moving from Enterprise BI to self-service is not a single-step process. It should be an incremental transition. Rather than a company-wide shift, focus on transitioning specific departments or teams based on their readiness, data maturity and business needs.
• Not all functions need to transition to self-service: Business-Led Self-Service should not be the default goal for every department. It should only be pursued when there is a genuine use case, backed by a mature data culture and/or a team with the right skills to manage their own data models effectively.
Final Thoughts: The Balance Between Control and Self-Service
Power BI governance is not about choosing between control and self-service but striking the right balance. Organisations that succeed empower users while maintaining governance, ensuring data integrity, security and scalability.
So, what's the key takeaway? Governance and self-service are not opposing forces. They complement each other. Locking everything down frustrates users, while unchecked access creates chaos. A strong governance strategy with guardrails in place allows self-service to thrive, keeping insights trustworthy. Power BI governance is about structured adoption, balancing control and empowerment. Get this right and Power BI becomes more than just a reporting tool, it drives data culture and smarter decision-making.
Need Help with Power BI Governance?
Watch the video to see our proven approach to governance - establishing guardrails, empowering end-users, and ensuring secure, consistent analytics. Then, take the next step: Book a free consultation call here.
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