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Automated reporting: hours instead of days

26 July 2026 · 7 min read · Manfred Schönberger

A monthly report that ties up three working days costs more than seven working weeks a year – time missing from the analysis of the figures. Automated reporting promises to compress that effort into hours. This article describes how the ERP connection works, which sources of error disappear with it, and which preconditions decide the outcome.

Where the time actually goes

Break down the effort behind a monthly report and you rarely find the time in the analysis. It sits in four steps: extracting data from several systems, aligning formats, reconciling figures, assembling the report. Only after that does the work begin that the finance department was hired for.

Jens Horstmann of TREVISTO AG names the reason this effort grows over time: a spreadsheet analysis comes together quickly, but manual effort and error counts grow with it until results are "often no longer traceable". Automation addresses the first three steps and largely removes the fourth.

How the ERP connection works

The technical basis is unspectacular, which is exactly why it holds. Instead of an export someone triggers each month, a connection reads the required tables directly from the source system. Three building blocks are involved:

The middle block carries the benefit. As long as each analysis calculates its own metrics, divergent figures arise. Once they are defined centrally, the reports necessarily agree.

Four sources of error that disappear

The author's classification from mandates with Swiss mid-sized companies.
Source of errorWhy it arises in manual work
Stale dataThe export ran before the last adjusting entry
Copy errorsRange shifted, row missed, formula overwritten
Two truthsSales and finance calculate the same metric differently
Silent changeSomeone adjusts a formula and nobody notices

The fourth row weighs heaviest because it goes unnoticed. In an automated pipeline, every change to a definition is a documented intervention in one place.

What the change requires

Three conditions decide whether automation delivers what it promises:

  1. Access to the source system. Your ERP needs an interface or read access to the database. Common systems provide this; for very old or heavily customised installations this check belongs at the very start.
  2. Clean master data. Chart of accounts, cost centres and item master form the structure of every report. Disorder here becomes visible faster under automation, and multiplies.
  3. Binding definitions. One formula per metric, written down and backed by management.

Point three is the only one that triggers debate, and it is the most important. I have seen initiatives stall on the question of whether early-payment discounts reduce revenue. That question can be settled in one meeting – as long as it gets asked.

What realistically comes out of it

On impact I stay cautious, because robust industry-wide figures are lacking. What can be said responsibly: the three steps named above largely disappear, and with them the bulk of the effort. The report is available within hours of the final posting rather than after days. The time saved accrues in the department producing the report – exactly where it is needed for analysis and commentary.

The second effect is harder to measure and lasts longer: reports that arrive reliably and quickly get used. A report appearing three weeks after month-end gets filed.

The author's assessment

Automated reporting offers the best ratio of effort to impact in the whole of finance digitalisation – provided it comes at the right point in the sequence. Automating before the metrics are defined simply accelerates the production of contradictory figures.

My recommendation on scope: automate the report with the highest repetition frequency first, rather than the one with the greatest visibility. The monthly revenue and margin report beats the annual budget pack, because the effort is saved twelve times a year.

And a warning about completeness: the temptation to capture everything in one go is strong. Resist it. A pipeline that runs after six weeks and delivers three metrics builds more confidence than an initiative promising fifty metrics after a year.

The short version

  • The time in a monthly report sits in extracting, aligning and reconciling – rarely in the analysis.
  • Three building blocks: data extract, central data model, distribution. The data model carries the benefit.
  • Four sources of error disappear, including the silent formula change that goes unnoticed in manual work.
  • Preconditions: access to the source system, clean master data, binding metric definitions.
  • Automate the report with the highest repetition frequency first, not the most visible one.

Sources

This article draws on the metadata and full descriptions of the YouTube contributions listed below, together with the chapter markers and quotation highlights the channels published themselves. YouTube no longer releases verbatim transcripts, so statements are attributed to their originator and marked as that person's position.

  1. KIundTECH Podcast with Jens Horstmann, TREVISTO AG: Power BI instead of Excel in controlling (in German), 25 Dec 2022, 42:38 min.
  2. KIundTECH Podcast with Jens Horstmann, TREVISTO AG: Controlling with Power BI: automated reports without Excel (in German), 18 Jul 2023, 35:26 min.
  3. AI or DIE (BI or DIE) with Dennis Hoffstädte, DatenPioniere: BI in mid-sized companies (in German), 4 Apr 2023, 33:56 min.
  4. AXA Switzerland: SME Labour Market Study 2025 – Artificial intelligence is conquering Swiss SMEs (in German), media release of 8 October 2025.

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