Area 15 · Data, AI and consulting

Data and analytics

When sales and finance turn up to the management meeting with two different revenue figures, it is rarely because somebody miscalculated. Each team pulls numbers from its own system and applies its own definition. We tidy up master data, bring the sources together and build reports where a figure needs no footnote.

Discuss your reporting See the services

services in this area

One

version of the truth

Power BI

and Microsoft Fabric

data centres and GDPR

Three services, in the right order

Each builds on the last: rules for your master data first, then a shared data warehouse, and finally automatic reading of the documents that pour in.

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Master data management

A single register of customers, suppliers, products and departments, with CVR lookups, rules for creating records and a hunt for duplicates. Skip this step and you simply end up processing a mess faster.

Data warehouse

Data from e-conomic or Business Central, the webshop, stock control and marketing platforms is consolidated in Microsoft Fabric or Azure in an EU region. On top we build Power BI reports for sales, inventory, finance and production - identical results whoever runs them.

Intelligent document processing

Invoices, delivery notes and contracts arriving as PDFs or scans are read automatically. Numbers, dates and amounts drop into the system, leaving staff to check and approve. OIOUBL invoices via NemHandel are handled natively.

Symptoms we see again and again

The request often sounds like "we'd like a nice dashboard". Behind it there is almost always one of the issues below, and that is what needs fixing.

Every team has its own figures

We agree shared definitions. The same KPI is simply calculated in several ways.

One customer, three records

We cleanse master data and set rules. Duplicates creep in wherever nobody checks at creation.

Month-end reporting takes three days

We build a self-refreshing warehouse. Excel exports and manual copy-paste eat the analyst's week.

Figures are stale by meeting time

We schedule automatic loads. A monthly extract cannot keep pace with the decisions being made.

Finance is buried in paperwork

We add automatic document capture. Invoice lines are still keyed in by hand, one after another.

A polished dashboard on top of poor data is worse than none at all. When a report is compiled by hand, errors tend to get noticed along the way. A tidy chart looks credible, and management acts on it without questioning what lies underneath. That is why we always begin with data quality.

How we build your reporting

We begin with one department and a small set of KPIs. Trying to cover the whole organisation in one go drags on for months and seldom ends well. All work is done remotely.

Agree the KPIs

What counts as revenue, a shipped order, an active customer? The dullest step and at the same time the most important one.

Review the sources

We find out where data lives, who owns it and how much cleansing is needed. Personal data is mapped with GDPR in mind.

First report

Automatic loading goes live, and the first Power BI report answers the questions management really asks.

Expansion

Further systems and teams are connected. Each new source costs less than the one before, because the foundation is already in place.

Questions and answers

If everything sits in one system and nobody doubts the numbers, you do not need an extra layer. The need arises once data comes from several places: finance, webshop, stock control, Google Ads and Meta. Hooking all of that onto the production database is awkward, and heavy queries slow the system down for everyone else.

With reasonably well-kept data, roughly a month, including agreement on the KPIs. If master data is in poor shape, most of the time goes on clean-up and the timeline shifts. So we look inside the databases before committing to a date.

In an EU region, usually Azure North Europe or West Europe. We sign a data processing agreement, restrict access by role and log who views what. Reports containing personal data get row-level security, so a sales rep sees only their own customers.

Either option works. Under a service agreement we monitor the loads, adapt when a source system changes and add new KPIs on request. Alternatively we hand everything over to your own people with documentation. This is settled at kick-off.

Related areas

All services

Area 14 Artificial intelligence What you can build on clean data Area 10 E-commerce and integrations Data flows between webshop and finance Area 16 Consulting and audit Which KPIs you actually need Solution GDPR compliance Personal data in reports, under control

Let's get your numbers in order

Tell us which reports are built by hand and where the figures disagree. We will review the source systems and suggest where to begin.

Email
office@apply.dk

Coverage
All of Denmark, delivered remotely

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