The month-end closing does not mark the end of the month. For the controlling department, this is only the moment when the real test of the organisation begins.

After the financial close, the focus shifts to consolidating and reconciling data, updating forecasts, and preparing management reports. In many organizations, this process is still highly manual, prone to errors, and carried out under significant time pressure. Not because of a lack of talent or expertise, but because of inefficient data structures and fragmented information flows.

Why the Closing Process Takes So Long… and Why It Doesn’t Have To

In most companies, the post-close reporting process still relies heavily on manual work:

• data exports from ERP, CRM, WMS, spreadsheets, and other supporting systems,
• data cleansing, consolidation, and the addition of formulas, adjustments, and eliminations,
• manual data reconciliation and variance analysis,
• creating multiple versions of reports for different stakeholders.

As a result, preparing the management reporting package often takes 10–14 days. The controlling team has increasingly less time for analysis and more for data logistics. Meanwhile, many of these steps can be automated and structured into a monthly cadence—without replacing the ERP system or requiring an organizational overhaul.

The Business Intelligence ecosystem – the backbone of modern closing

MMicrosoft Fabric has brought together multiple tools into a single, unified ecosystem, which today is one of the most effective ways to structure the reporting close process:

• Power Query and Dataflows – automated extraction, cleansing, and integration of data from multiple sources (ERP systems, spreadsheets, SQL databases, APIs),
• Power BI – a shared data model and interactive reporting for controlling, CFOs, and managers, with easy distribution across the organization and to external partners,
  
• Power Automate / Data Activator – automated alerts about data quality issues or KPI threshold breaches,
  
• Excel – the ability to connect directly to the same data model, without exports or file copying.
  

As a result, controlling stops being a “file operator” and becomes a curator of business data.

A shared data model – the foundation of trust

The breakthrough comes when the entire organization works on a single, shared data model. In a Power BI semantic model, data is structured, regularly refreshed, and governed by consistent quality rules. Such a model becomes a central repository of trusted information—a single source of truth for controlling, finance, and the executive board.

In a properly designed process, validation doesn’t happen “at the end” in Excel—it is embedded directly into the data flow. Rules for completeness, consistency, and uniqueness are checked during data loading, and a dedicated data quality report shows what has passed the checks and what requires attention.

Excel, meanwhile, doesn’t disappear. It remains a convenient interface to the same underlying model. Analysts connect workbooks directly to the model and retrieve selected measures or dimensions—without exports or manual copying. This preserves Excel’s flexibility while eliminating parallel, conflicting versions of the data.

In this setup, report distribution can happen entirely without files. Users simply access a set of reports, while Row-Level Security (RLS) in Power BI ensures that each person only sees their own slice of data (company, department, region). It creates a clear access structure, an auditable usage trail, and significantly less time spent on distribution logistics.

The result in practice:

• the same logic and KPI definitions across all reports and spreadsheets,
• built-in data quality validation and fast alerts instead of manual “data checking,”
• no more sticking individual sheets together or making uncertain calculations,
• greater confidence in the figures, as each value has undergone the same, repeatable process.

Data quality automation – reports that monitor themselves

An automated data pipeline doesn’t end with data integration. In a well-designed BI environment, data quality control is an integral part of the model.

In practice, this means:

• a dedicated quality report in Power BI (e.g. ‘Unreconciled data’, ‘Duplicates’, ‘Missing values’),
• an alert system that notifies the source owner when an error occurs,
• the ability to track data quality trends over time – e.g. which sources require the most intervention.

Power Automate or Data Activator can send an automatic notification (email or Teams) if, for example, sales data for a specific day has not appeared in the CRM system, a KPI is showing unlikely results, or a new accounting account or analytical dimension has been created.

This is a huge change: the system automatically detects errors before they appear in reports. The time previously spent manually checking data for accuracy can now be devoted to analysis and interpretation.

Not everything has to wait for the accounts

Traditionally, the closing of the books is the point at which the figures become final.
However, many areas – sales, cash flow, stock, and receivables – can be analysed on an ongoing basis, as data is updated daily.

The BI model enables the controlling department to monitor monthly trends, respond proactively and prepare preliminary estimates of results before the books are formally closed. After closing, the financial data (Profit and Loss Account, Balance Sheet) are simply ‘added’ to the monthly overview and their status changes to Closed.

And that’s not all. A well-designed data pipeline, supported by repeatable monthly settlement patterns, even enables the automatic estimation of key performance indicators. As early as in the first few days of the following month , it is possible to generate an estimated pro-forma income statement or estimated KPIs (provided that data quality is maintained and settlement processes are predictable). Of course, this is still an estimate. However, with mature budgeting processes, a known cost structure and a consistent data model, the accuracy of such estimates can be very high. What is more – with increasing transparency and pressure to speed up settlements (e.g. regulations under KSeF) – the reliability of rapid estimates will naturally increase.

This is the point at which reporting ceases to be a one-off event on the calendar and becomes an ongoing process of financial information management.

Reports that guide the user – UI/UX in practice

The speed of analysis depends not only on the data, but also on how it is presented. A poorly designed report can be just as useless as no report at all.

That is why an increasing number of companies are turning to the IBCS (International Business Communication Standards), which, although not fully, can be applied to a significant extent in Power BI:

• a consistent way of presenting a specific type of data in all reports,
• consistent colour scheme, formatting, choice of visuals and layout of the sections,
• reducing the number of ineffective visualisations, such as static pie charts or tables lacking context,
• a focus on differences, trends and interrelationships, rather than just figures taken out of context.

A well-designed Power BI report guides the user: the CFO can immediately see where something significant is happening, whilst the controller has a tool that allows them to drill down into the data without having to wade through 20 spreadsheets. It’s a user experience that reduces the time from click to decision.

Adoption and data culture

Even the best BI model won’t work without a change in working practices. Many implementations are abandoned halfway through because users revert to Excel “because it’s quicker”.

DThat is why adoption and coaching are key:

• short workshops for report users (CFOs, controllers, line managers),
• joint data reviews in Power BI,
• coaching on interpreting indicators, drawing conclusions and basing your day-to-day decisions on data.

That’s when a team truly becomes data-driven – it starts to trust the data and use it as part of its daily routine, not just “at month-end”.

A closing that works

Modern reporting for financial close doesn’t require a new ERP system or hundreds of hours of development work.
This requires streamlining: the process, the data sources and the way we work. When data flows automatically, errors are detected before they make it into reports, and Excel and Power BI use the same model – controlling can finally return to its proper role: that of a business partner, not a data handler.