Finance

Migration from Excel to PowerBI for controlling

Client: Financial Advisory (+10 SMEs)

Client
Financial Advisory (+10 SMEs)
Sector
Finance
Alignment with today's offering
Enterprise Brains
The Challenge

The starting point

Excel was obsolete, errors were manual and consolidation was impossible across several companies at once.

The Solution

What we built

We carried out a complete migration to PowerBI with normalised modelling, automatic ETL, per-company dashboards and architecture on Power Platform and Dataverse.

Key features
  • Normalised multi-company data modelling
  • Automatic ETL from multiple sources
  • Independent dashboards per company
  • Architecture on Power Platform and Dataverse
  • Automatic cross-company consolidation
  • Permissions per company and role

Anatomy of the solution

Consolidate companies without mixing their contexts

From input to outcome: explore the four parts of the journey.

Sources

Company data and reporting processes.

Explanatory diagram based on the described solution. It simplifies the journey; it is not a deployed infrastructure diagram or a live monitor.

Reading the project

The thinking behind the technology

01 / Business

What really needed solving

Spreadsheets reach their limits when consistency must be maintained across companies, sources and versions. Migration is not simply reproducing Excel on a screen: it requires a model that supports consolidation while retaining each company’s boundaries.

02 / Design

Why this structure matters

Normalisation and automatic ETL reduce reliance on manual transformations. Per-company dashboards and role-based permissions separate access and analysis. Power Platform and Dataverse provide the structure supporting PowerBI visualisation.

Connection to today’s offering

Enterprise Brains

The context connecting the company

Explore the service

Current relevance and possible evolution

The connection to Enterprise Brains is a reliable data foundation accessible within context. Before introducing analytical agents, companies should agree definitions, owners and access rules.

This connects the case experience to our current services. Proposed extensions are not presented as features already delivered.

Impact
80%
less reporting time
95%
errors eliminated
10+
companies migrated
6 months
ROI recovered

Method and scope

How to read the outcome

What to measure

Compare preparation time and corrections across equivalent reports. Include migration, maintenance and licensing costs when assessing payback.

What not to infer

Error reduction depends on the error type and reports measured; it does not mean all source data is automatically corrected.

Basis of this analysis: the description, capabilities and stack published in this case. No new measurements are added and no independent audit of its results is implied.

Tech stack
PowerBIPower PlatformDataverseDAXPower QueryM
In context

How it fits with the rest

NextAI

Does your company need this?

One hour, no sales deck: we analyse your operation and tell you what can be built, where to start and what is not worth touching yet.

Ready to apply this to your business?

Nora helps you choose. Our team scopes your project.

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