Case study

Manual Excel, replaced by governed models, one engagement at a time

Several sectors, mid-size

A series of smaller BI engagements across finance, HR and production, run alongside larger projects. Each one replaced manual Excel reporting with a governed Power BI model on the same pattern.

Read the engagement brief

Period: 18 months
Companies: 50–250 people
Areas: Finance, HR, production

A schematic: the separate reports this engagement started from, the one consolidated reporting layer that replaced them, and the technologies underneath.

Different departments, the same pattern

Finance, HR and production teams each had reporting kept in Excel by hand. Every engagement in the series replaced it the same way: automated loading, one governed model and training for the team.

kinds of engagement in the series: 4
teams trained: finance, HR, production: 3
months, alongside the main projects: 18

No outcome figures are published for these engagements: nothing exists that would let you check them, so what was built is described in full instead. Anonymised by sector and size, because the confidentiality that protects these clients would protect you too. Sector and outcomes are real; identifying detail is not.

What each engagement replaced

Manual Excel

Financial reportingExcel workbooks
HR performance reportingManual Excel
Production KPI dashboardsManual Excel
Recurring reportsPrepared by hand

Governed models

Power BI financial reportingConsistent KPI logic, defined once
HR performance reportingOn a governed model
Production KPI dashboardsOn a governed model
Automated reportingETL and Dataflows instead of copy and paste

The 4 kinds of engagement in the series, each moving from manual Excel to a governed model.

The engagement in full

Client: Several organisations of 50 to 250 people, each with a contained reporting problem in one department.
Situation: Each team rebuilt its reporting in Excel by hand every period, so the report depended on somebody doing the copy, and the figures were calculated a little differently each time they appeared.
Mandate: Replace a manual Excel report with a governed Power BI model, automate how it is fed, and leave the team able to use it.

Performance underneath the reports

SQL performance tuning for BI workloads sat under the series: slow queries rewritten, and indexes matched to how BI actually reads the data rather than to how it is written.

The recurring pattern

Every engagement in the series followed the same 4 steps, whatever the department.

Manual ExcelReporting rebuilt by hand each period
Automated loadingETL and Dataflows instead of copy and paste
Governed Power BI modelConsistent KPI logic, defined once
EnablementThe team trained on its own model

Architecture decisions

Decision	Rejected alternative	Why
One pattern for every engagement	A new approach per department	Each engagement started from something already proven.
Automated loading through ETL and Dataflows	Copying data into Excel each period	The report stops depending on someone doing the copy.
KPI logic defined in the model	Formulas spread across workbooks	A figure is calculated the same way every time it appears.
Training as part of delivery	A handover document only	The team uses and extends the model rather than going back to Excel.

What the clients have today

Governed Power BI models in place of manual Excel for financial, HR and production reporting.
Automated ETL and Dataflows feeding the models, with consistent KPI logic inside them.
SQL tuned for the BI workloads that read it, through query optimisation and indexing.
Finance, HR and operations teams trained to use and extend their own reporting.

Where it left them: Smaller engagements, each closed with a governed model and a team that can use it, built on a pattern the larger projects use as well.
Stack: Power BI
DAX
SQL
Azure Synapse
Azure Data Factory
Dataflows

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The other engagements

Retail
Foundation then Activation then Acceleration alongside Stewardship
Store revenue, executive questions and forecasting on one set of definitions
A retailer with Microsoft Fabric already in production, semantic models over Direct Lake and store revenue streaming in, who wanted forecasting, Copilot questions and anomaly detection on top of it.
- Microsoft Fabric
- Power BI semantic models
- Direct Lake
- Copilot in Power BI
- Fabric AI Functions
- Activator
Industrial manufacturing
Foundation then Activation then Acceleration
Governed reporting across a production network
An industrial manufacturer reporting across a production network of sites, each with its own view of what was running.
- Microsoft Fabric
- OneLake
- OneLake Security
- Direct Lake
- Spark
- MES
- OPC UA
- Power BI
- DAX
- Azure Key Vault
- Purview
- Azure DevOps
Hospitality and real estate
Foundation then Activation
A CFO cockpit over a group that closed its books separately
A holding group across hospitality and real estate, with entities that each closed their own books and a CFO who had to answer for all of them.
- Microsoft Fabric
- OneLake
- Direct Lake
- Databricks Mirrored Catalog
- Fabric Data Factory
- Power BI
- DAX
- Calculation groups
- Row-level security
- Azure DevOps
- Azure Key Vault
- Purview
Multi-brand retail
Foundation then Activation
One governed model across a multi-brand retail group
A retail group running several brands, each with its own systems, its own reporting habits and its own answer to what a margin is.
- Microsoft Fabric
- Direct Lake
- Databricks
- Eventstream
- KQL Database
- Power BI
- DAX
- SAP
- Salesforce
- Azure DevOps

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