Real outcomes, not hypothetical scenarios

Examples of how we've helped organisations improve their Power BI governance and security posture and reduce costs.

Tenant Settings Review

Strengthening security and governance in a government Power BI environment

The situation

A New Zealand government department with an expanding Power BI environment wanted confidence that their tenant was configured correctly, especially with strict privacy and data sovereignty requirements. The environment had 1,200 users, 2,500+ reports across 380 workspaces, and was growing by 75 reports and 20 workspaces per month.

Most development was driven by business users with limited central oversight. A single Power BI developer and part-time governance manager were responsible for the entire platform. The Data Platform Manager was unsure if default tenant settings provided enough protection, unclear about risks from Fabric trials and new features, and lacked visibility into content sharing practices across the organisation.

The problem

Microsoft enables many tenant settings by default, including preview features and the ability to create Fabric workloads and trials. Settings were either on or off for the entire organisation, with no use of security groups to restrict access to more risky features. Content and workspaces were proliferating rapidly under a self-service model with limited visibility into who was sharing reports broadly, or if content was being published to the web.

Allowing users to start Fabric trials created new, unmonitored workloads, raising the risk of ungoverned shadow IT outside established controls. These factors combined to create greater governance complexity and a higher risk of breaching data sovereignty or privacy obligations.

What we did

We started with a thorough analysis of all 142 tenant settings, mapping the current state of the environment and identifying where configuration had drifted from what a government agency with these privacy and data sovereignty obligations should have in place.

We then ran a workshop with the Data Platform Manager, Governance Manager, and M365 Admin to review our findings in the context of their specific security policies, ways of working, and environment specifics such as the use of Snowflake as the data source of record. That conversation shaped which recommendations were critical, which needed stakeholder management, and which could be deprioritised.

From there, we refined our recommendations into a prioritised remediation plan tailored to the department's context, and worked with their admin team to implement the changes directly, with no downtime or disruption to existing reports.

The outcome

31 high-impact settings were remediated. We disabled risky features including uncertified visuals, tightened controls for sharing and workspace creation, and implemented an automated script to detect and disable new preview features as they are released, ensuring ongoing compliance and keeping data within the region.

Governance maturity was raised from "Unmanaged" to "Managed controls in place."

142
Tenant settings reviewed
31
Settings remediated

Measurable benefits

Reduced risk of data leaks by blocking uncertified visuals and unverified script execution
Data sovereignty maintained by restricting usage to Generally Available features and disabling preview features
Lower governance overhead through automated detection and deactivation of risky features
Fewer broken reports by retiring end-of-life workloads such as Datamarts
Controlled workspace creation, limited to trained users through security groups
Improved security maturity by limiting broad and external sharing to approved users

Environment

1,200 users
2,500+ reports
380 workspaces
+75 reports per month

Capacity Optimisation

Reducing Fabric capacity spend without losing performance

The challenge

An organisation was spending significantly more than necessary on Fabric capacity. Reports were slow, users were frustrated, and the instinct was to add more capacity. But the problem wasn't a lack of resources. It was how the existing capacity was being used.

What we did

We analysed capacity usage patterns, identified performance bottlenecks, and found that a combination of inefficient data models, poorly scheduled refreshes, and unused workspaces were consuming resources without delivering value. We restructured refresh schedules, optimised the largest data models, and consolidated workspaces.

The outcome

Monthly capacity costs reduced by $18,000 while report performance improved. The changes required no reduction in functionality and no disruption to end users.

$18K
Monthly cost reduction

Want to see similar results in your environment?

If you'd like to discuss how these approaches apply to your organisation, we'd welcome the conversation.

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