Microsoft Power BI: what it is, how it works and how to implement it in your organisation

Power BI can turn six Excel files, a CRM and a management system into a dashboard. For it to do so, however, the company must first establish which source holds the correct data and what each indicator really means.

This is the starting point. If Business Intelligence collects, normalises and distributes company information, Power BI is one of the tools through which that system can become operational. Much as with business KPIs: the platform can calculate and display them, but the choice of which indicators should guide management is made upstream.

Microsoft presents Power BI as a self-service and enterprise business intelligence platform integrated into the Fabric ecosystem. It can connect sources, transform data, build semantic models, create reports and distribute them across the organisation (Microsoft).

This, then, is the difference between buying Power BI and implementing it: the licence adds a tool. Implementation builds a shared version of the numbers.

What Microsoft Power BI is

Microsoft Power BI is a Business Intelligence platform used to connect data sources, transform and model them, and deliver the results through interactive reports and dashboards.

It can work with databases, Excel and CSV, ERPs, CRMs, cloud platforms, data warehouses, lakehouses and business applications. The visible output is often a report. Beneath that report, however, sits a model that defines relationships, definitions, calculations and access rules.

Microsoft uses the term semantic model to describe this logical layer: a representation of the domain with metrics and terms the business understands, ready for analysis (Microsoft Learn). From here, Power BI takes on the role of an information infrastructure that goes beyond visualisation.

Power BI Desktop, Service and Microsoft Fabric

In mature installations, several reports can use the same semantic model instead of rebuilding revenue, active customer, margin or pipeline each time. This reusability reduces divergence and makes the metric easier to govern.

Fabric widens the scope with workloads for integration, engineering, lakehouse and analytics. For an SME it may be unnecessary; in organisations with many sources and data teams it can become part of the overall architecture.

How Power BI works: from source to decision

The typical path runs through sources, transformation, semantic model, metrics, reports and distribution. The value depends on the quality of each step.

Connect the sources

A company may have invoices in the ERP, customers in the CRM, budgets in Excel, campaigns on advertising platforms, orders in e-commerce and tickets in a support system. Power BI can bring them into the same analytics environment.

Connection alone leaves identity and quality problems untouched. If the CRM uses the code AZ001 and the ERP contains three variants of the same company name, the system still has to establish that they are the same customer.

Transforming and normalising

Inconsistent dates, empty fields, different currencies, duplicates and local categories require transformation. The most important step, however, concerns meaning.

Finance may define “active customer” as anyone who has purchased in the last twelve months; Sales may include anyone with an open deal. The dashboard makes the conflict visible and forces the organisation to decide which definition to use.

Building the semantic model

The semantic model organises entities, relationships and metrics. Customers, orders, products, dates, sales representatives and campaigns become connected objects on which shared measures are defined, such as revenue, gross margin, win rate or average order value.

The most important advantage is stable definitions: when several reports read the same model, the formula is maintained only once.

Choosing how to query the data

Microsoft currently supports Import, DirectQuery, composite models and Direct Lake in specific scenarios. The choice affects speed, refresh, security, cost and maintenance (Microsoft Learn).

Building reports that answer a question

Visualisation comes afterwards. An executive dashboard can show revenue, margin, pipeline, forecast, CAC, retention and variance against budget. Every element, however, should contribute to a decision.

For the CEO, four questions are often enough: are we above or below target? Where does the variance originate? For how long? Who needs to act?

Distributing information

In the Power BI Service you can publish content, organise workspaces and control access. Row-Level Security allows different users to see different portions of the same model.

The sales director can see the entire network, an area manager their own area and the CEO a summary. The model stays common; what changes is the visible scope.

Definition debt: the hidden cost that Power BI makes visible

Many companies build up, over years, a form of debt that is hard to see: the same concept is defined in different ways by different departments. “Revenue”, “margin”, “active customer”, “qualified lead”, “order” and “churn” seem like shared words for as long as the data stays separate.

When Power BI tries to bring them together, that debt surfaces. Every ambiguity requires a decision, an owner and a formula. The longer definitions have remained local, the more the BI project has to invest in reconciling them.

This explains why a seemingly simple project can become organisationally demanding: the technology brings an existing conflict to the surface.

Power BI, CRM and Finance: a concrete example

Imagine a B2B company. The CRM holds leads, opportunities, pipeline and acquisition sources. The ERP holds invoices, receipts, margins and costs. Marketing keeps spend, campaigns and leads.

With a shared model, you can follow a complete sequence: Campaign → Lead → Opportunity → Customer → Revenue → Margin.

The question changes in quality. Instead of “how many leads have we generated?” management can ask “which source is producing the most profitable customers?”. The CRM continues to govern the relationship, Finance the financial data, and Power BI builds the layer in which they can be read together.

Power BI and Excel can coexist

Excel often remains the starting point. Many organisations have built logic, reports and local knowledge into spreadsheets that it would make little sense to discard wholesale.

The problem arises when the file becomes the official source without ownership, versioning or shared rules. Power BI can use Excel as a source and progressively move the most critical information into a centralised model.

The useful question, then, is: which information needs to stop depending on an individual file?

What Power BI costs in 2026

As of September 2026, Microsoft Italia lists Power BI Pro at 12,10 euro per user per month and Power BI Premium Per User at 20,80 euro, billed annually and excluding VAT. The free account remains available, while Embedded and Fabric capacities have variable pricing (Microsoft).

The cost of the project almost always exceeds the cost of the licence

The price of the software is easy to calculate. The cost of implementation includes source reconciliation, data pipelines, the model, report migration, training, governance, documentation, maintenance and the time of functional managers.

A significant part of this work consists of reducing the definition debt described above. If two departments need to debate for three weeks how to calculate margin, that conversation is part of the BI project, even if it produces not a single line of code.

How to implement Power BI in your organisation

Start from the decisions

The first scope should be a recurring decision: budget allocation, sales forecasting, margin by product line, production capacity, renewals or variance against plan. This keeps the project contained and makes its value verifiable.

Defining KPIs and ownership

Every KPI needs a definition, formula, source, frequency, baseline, target and owner. As long as the same word produces different formulas across functions, the dashboard is premature.

Map the sources

The Owner column carries as much weight as the technology. Data without accountability tends to deteriorate.

Design the model

This is where the master customer source, duplicate handling, the calendar, revenue and cost attribution and shared dimensions are established. A solid model makes it easier to build many reports; a weak model replicates the same problem in every report.

Putting an MVP into use

An Executive Sales Dashboard with revenue, target, pipeline, win rate, forecast and sales cycle may be enough to test the system. A few weeks of real use generate more useful feedback than months spent designing a universal dashboard.

Establish governance and access

Who can create a semantic model? Who certifies a metric? Who publishes to workspaces? Who modifies the model? Who steps in when a refresh fails? Self-service works when these responsibilities are explicit.

Measure adoption

Active users, frequency of use, unused reports, time saved, manual processes eliminated and decisions supported tell you more than the mere number of dashboards created.

Power BI and Copilot: AI increases the value of correct definitions

In 2026 Copilot is integrated into Power BI and Microsoft Fabric experiences to assist with analysis, querying and building insights, with specific capacity and administration requirements (Microsoft Learn).

The conversational interface lowers the cost of accessing information. At the same time, it makes the quality of the underlying model more important. If margin is poorly defined, AI can explain a wrong margin very quickly.

Automation therefore amplifies both quality and error. Consistent metrics, correct permissions and up-to-date data become even more important prerequisites.

When Power BI is right for an SME

The size of the company matters less than how often decisions recur. If someone copies figures every week from the CRM, Excel, the management system and presentations to rebuild the same report, there is already a process that can be improved.

For an SME, a simple model, a few reliable KPIs and one report used by management may be enough. An oversized enterprise architecture increases cost and dependency without guaranteeing a better decision.

When to choose another tool

Power BI starts with an advantage in companies already immersed in the Microsoft ecosystem. Other platforms may be better suited when the architecture is closely tied to other clouds, there is established expertise in different tools, embedded analytics is central, or the cost of migration outweighs the benefit.

The best criterion remains consistency between architecture, skills and decision-making. A platform that requires external support for every change can become technically powerful yet organisationally fragile.

Three signs of more mature BI

More mature BI tends to produce three effects:

• fewer competing definitions of the same KPI;
• fewer duplicate reports telling different versions of the same reality;
• more recurring decisions taken on a shared information base.

When these three effects appear, Power BI is doing the job it was implemented for: shortening the distance between a piece of data and a decision.