Case Study: Azure Tagging and Showback Transformation

From 2% tagging coverage to meaningful showback and major cost reduction

A large enterprise had been operating Azure at scale for years without a reliable tagging model or clear showback capability. The environment had grown to approximately 6,000 Azure resources, spread across around 600 resource groups and 32 subscriptions.

Environments were scattered across subscriptions, resource ownership was unclear, and tagging had grown organically with no agreed standard. Some resources had tags, some did not, and many tags used different names or values for the same purpose. The result was simple: the organisation could not reliably explain who owned what, what workloads were running, or how cloud costs should be attributed.

Management had been asking for clear workload ownership and showback for years. Internal teams had attempted tagging clean-up several times, but the work repeatedly started and stopped. The scale was too large, the process was too manual, and there was no reliable automation or shared view to drive progress. After five years, little meaningful progress had been achieved.

The Challenge

The organisation needed to turn Azure tagging from a fragmented technical task into a controlled business and operational process.

Key issues included:

• Around 6,000 Azure resources across 600 resource groups
• 32 Azure subscriptions with mixed and scattered environments
• No agreed or communicated tagging policy
• Random tag names and inconsistent tag values
• Only around 2% useful tagging coverage
• No reliable showback model for business or Digital ownership
• Limited internal automation capability for tag export, analysis, and controlled updates
• Multiple previous tagging initiatives had failed to reach completion
• Cost ownership, workload purpose, and lifecycle status were unclear

The business impact was significant. Without accurate ownership and workload classification, the organisation could not confidently identify waste, legacy resources, non-production workloads, or cost accountability gaps.

What We Did

CloudQbit helped turn the tagging problem into a structured, repeatable, and measurable delivery process.

The first step was to automate the export of Azure tagging information into one unified view. This gave the organisation a clear baseline across subscriptions, resource groups, existing tags, missing tags, inconsistent names, and inconsistent values.

The analysis confirmed that only around 2% of the environment had usable tagging coverage. Even where tags existed, naming and value consistency were poor, which meant the data could not be reliably used for showback, reporting, or lifecycle decisions.

CloudQbit then recommended a practical baseline tagging model that was simple enough to implement but strong enough to support showback, ownership, and operational decision-making.

The organisation deliberately started with a tight scope of mandatory tags and controlled values. This kept the work achievable and allowed discovery to continue without overcomplicating the process.

Baseline Tagging Model

This model gave the organisation enough structure to support cost allocation and ownership without creating a tagging framework that was too heavy to implement.

Delivery Approach

CloudQbit developed and refined the Azure tagging export process so the team could repeatedly retrieve the current state and track progress from one shared view.

In parallel, CloudQbit developed a controlled Azure tagging update script. The script allowed tag changes to be prepared, reviewed, backed up, and applied in a repeatable way. Documentation was created, and internal engineers were trained so they could safely operate the process themselves.

The work was then broken into manageable scope. A small group of engineers was assigned sections of the environment, starting with lower-risk non-production subscriptions and resource groups.

The team began with only around 10 resource groups per engineer. This gave them time to investigate each resource group properly, identify owners, understand the purpose of the workloads, and agree the correct tag values.

Daily workshops were established to review progress, unblock questions, validate ownership, and refresh the shared tagging view. This gave the team a firm grip on the work and avoided the common problem of tagging projects losing momentum.

Once a scope was completed and agreed, the tagging values were captured in the unified infrastructure document. A change request was then raised and presented through the change approval process. Existing tags were backed up before updates were applied, providing a rollback position even though no dependency on the old tagging model was identified.

Outcome

The full tagging implementation took approximately two months.

The following two months were then used to act on what the tagging process uncovered.

That second phase delivered the most significant value. Once ownership, environment, workload purpose, and business alignment were visible, the organisation could finally identify resources that were no longer required or were incorrectly placed.

The team discovered:

• Legacy resources no longer required
• Proof-of-concept and test workloads still running
• Non-production workloads sitting in production subscriptions
• Resource groups with unclear or outdated ownership
• Services that no longer had a business owner
• Workloads that could be retired, consolidated, or moved
• Cost areas that had previously been invisible to management

The final result was a major cost optimisation outcome, with approximately 35% cloud cost reduction achieved through the follow-up actions enabled by tagging visibility.

This was not just a reporting improvement. It became a significant reinvestment opportunity for the Digital department and a major win for the company.

Why This Matters

Many organisations talk about Azure tagging, but very few complete the work properly. Tagging is often treated as a policy or governance discussion, when the real challenge is execution.

Every organisation will have its own tagging requirements, but this case proved that a practical baseline model can unlock major value when it is backed by automation, ownership, reporting, and disciplined delivery.

The key success factors were:

• Establishing a simple and agreed mandatory tagging model
• Creating one unified view of current tagging state
• Automating export and controlled tag updates
• Training internal engineers to own the process
• Breaking the work into manageable scopes
• Using change control and rollback planning
• Following up on what the tagging data revealed
• Turning tagging from metadata into actionable business insight

CloudQbit Value

CloudQbit’s controlled Azure tag export and import process is now a reusable asset that can help other organisations finally complete tagging properly.

It supports organisations that need to:

• Establish Azure showback
• Improve cloud cost visibility
• Identify ownership gaps
• Clean up legacy or unused resources
• Standardise tag names and values
• Support FinOps reporting
• Improve governance across subscriptions
• Build a practical foundation for chargeback, accountability, and lifecycle management

Tagging is not the final goal. It is the foundation that allows organisations to understand their cloud estate, assign ownership, manage cost, and make better decisions.