What is the difference between BI and plain reporting?

Reporting describes the past in a fixed format; BI lets you explore, cross-reference and decide in real time, on demand. In practice: with reporting you receive an answer, with BI you ask a question.

Do you need a lot of data to do BI?

No. Even a small company gains from putting sales, margin and stock in one place — that is often where the most profitable decisions hide. Volume matters less than the number of sources to reconcile.

How long before a first useful dashboard?

A few days for a first dashboard on a precise question, provided the source data is accessible. What stretches timelines is almost never the visualisation: it is data access and settling definitions.

Do we have to replace our spreadsheets?

Not necessarily. The aim is to remove the spreadsheets that act as a source of truth — the ones several people copy from. A spreadsheet used to test a hypothesis remains perfectly legitimate.

Our data is a mess: should we clean it all first?

No, and it is counterproductive: a cleaning project without an objective never ends. We start from a precise question, clean what it requires, and the exercise reveals what deserves fixing first.

Who maintains the BI once delivered?

Someone on your side must be able to change an indicator definition without calling a supplier. That is a design requirement, not an option: a BI you cannot evolve goes stale in six months.

Decision · Middleware

Data & Business Intelligence (BI)

Bring your scattered data together and make it readable, so decisions rest on facts rather than instinct.

What is it?

Business Intelligence (BI) refers to the tools that collect, cross-reference and visualise your company’s data — sales, margin, stock, cash — in clear dashboards. Data stops being a frozen export and becomes a live reading of your business.

Technically, BI does three things in this order: it fetches data where it lives (ERP, till, e-commerce, spreadsheets), it shapes it according to stable rules — what is called modelling — then it presents it. The third step is the only one your teams see; the first two decide how reliable the whole thing is.

That modelling is what separates a dashboard people consult from a dashboard people argue about. If “revenue” does not mean the same thing to finance and to sales — discounts in or out, at order or at invoice — no chart colour will reconcile the two. BI forces those definitions to be settled once and for all. That is often its most lasting contribution, before the first chart even exists.

Signs you need it

The same indicator gives two different numbers depending on who pulled it.
Someone spends half a day a week rebuilding the same file.
You learn about a product slowdown three weeks late.
Decisions rest on how the team feels, for want of a number available in time.
Your exports live in spreadsheets nobody dares touch.

What is it for?

To decide quickly and correctly: spot a product losing ground, a margin eroding, a channel performing. BI turns scattered numbers into concrete decisions, at every level of the company.

The most visible gain is time: the hours of manual consolidation disappear. But the real gain lies elsewhere — in the delay between something happening and you knowing about it. Dead stock spotted in three days rather than six weeks is cash recovered.

A second, less expected effect: BI reveals the actual quality of your data. The first dashboards almost always surface anomalies — duplicate references, negative margins, ghost customers. That is uncomfortable and useful: those anomalies existed before, they were simply invisible.

Not to be confused with

Reporting: Reporting describes the past, on a fixed schedule, in a fixed format. BI lets you explore: filter, cross-reference, drill down, at the moment the question arises.
A spreadsheet: A spreadsheet is a personal calculation tool. It has no single source, no reliable history, no access rights. BI rests on shared data; the spreadsheet stays useful for testing an idea.
An ERP: The ERP produces data as operations happen. BI reads and cross-references it, including with other sources. The two are complementary: without an ERP, BI lacks material; without BI, the ERP stays hard to read.

How it works

01We start from decisions, not data: which questions do you want to settle every week?
02We connect your sources — ERP, e-commerce, CRM, till, spreadsheets — and confront their discrepancies.
03We model the indicators: a written, agreed definition for every number displayed.
04We deliver real-time dashboards, then adjust them against actual use.

The mistakes that cost the most

Showing everything that is available

A dashboard with forty indicators goes unread. Five numbers that trigger an action beat forty that describe a situation. The test: for each indicator, who does what when it moves? No answer, and it leaves the dashboard.
Plugging BI into data nobody has cleaned

A first dashboard showing a wrong number loses its credibility in a single meeting, and does not get it back. Better to ship a narrow, correct scope and widen it.
Letting each department define its own indicators

Three definitions of margin produce three truths and endless reconciliation meetings. The definition is settled upstream, once, and written down somewhere.
Mistaking the tool for adoption

The most elegant BI is worthless if nobody opens it. Adoption turns on two details: load time, and whether the number shown matches what the team has in mind.

What it costs, how long it takes

The cost of BI splits roughly into thirds: connecting the sources, modelling, presenting. It is the second third — invisible — that is consistently underestimated, and the one that decides the outcome.

The variables that really move the budget: how many sources to connect, how clean the existing data is, and how many indicator definitions must be settled between departments. The number of charts matters little.

Scope is the only genuinely effective lever. A first dashboard on a precise question can be prototyped in days; BI covering the whole company at once is a multi-month project, with a matching risk of being abandoned.

When you don’t need it

If your decisions rest on fewer than ten numbers you know by heart, and your data lives in a single tool that displays them correctly, BI has nothing to offer you. The need appears when sources multiply, or when the decision moves from you to a team.

With Meridian

We prototype your dashboard in days, with your teams, on your real data — then refine it until it is adopted without training.

Frequently asked questions