Financial Data Quality: Your Strategic Advantage in Times of Economic Uncertainty

Economic uncertainty is on the rise. Enquiries from management, the board of directors, and banks are becoming more frequent and require a quicker turnaround. Finance departments often react by adjusting budgets and forecasts. However, the real challenge lies deeper. It is about the quality of the financial data that serves as the basis for these important decisions. The issue of financial data quality uncertainty is a silent but significant corporate risk. In such times, the demonstrable reliability of one's own figures becomes a decisive strategic advantage.

The Invisible Danger: Poor Data Quality

Every month-end closing involves manual steps. Data is exported from the ERP system and processed further in Excel or other tools. Errors can occur during these transfers. A cell can be copied incorrectly, a formula accidentally overwritten, or a link to an old file can go unnoticed. Viewed individually, these errors are often insignificant. In aggregate, however, they can distort the result and lead to incorrect business conclusions.

This complexity in financial reporting is a risk factor in itself. Complexity arises from many manual interventions, unclear process steps, and the consolidation of data from various sources. A study has shown that high complexity can be a significant predictor of fraudulent financial reporting (doi.org 2024a). Where processes are convoluted, the potential for errors — and in the worst case, for manipulation — increases.

In a stable market environment, such inaccuracies might be tolerated. There is more time for checking and correction. In volatile phases, this is different. Decisions must be made more quickly, often based on preliminary figures. The pressure on the finance department increases. A flawed data foundation can then lead to wrong conclusions with far-reaching consequences. The trust of stakeholders in the finance department can be permanently damaged as a result.

Digital Transformation as a Response to Uncertainty

The answer to this challenge is a systematic improvement of process quality. The digital transformation of financial processes is the decisive lever for this. It is not about replacing the proven ERP system. It is about digitising and automating the processes that exist around the ERP — the chain from data export to the final report.

A recent study impressively confirms this connection. Digital transformation has a positive and statistically significant effect on the quality of accounting information (doi.org 2024a). The study quantifies this effect with a coefficient of 0.596. Automated and digitised processes demonstrably lead to better, more reliable data.

This effect is particularly relevant in an unstable business environment. The same study shows that the positive impact of digital transformation can become even stronger with increasing uncertainty (doi.org 2024a). It is precisely when the environment becomes difficult that investments in data quality can pay off the most. A robust reporting system becomes an anchor of stability for the entire company.

The improvement in information quality is achieved through several factors. Digital processes increase transparency. Every step is documented and traceable. They reduce information asymmetry, as all parties involved work with the same, audited data foundation. At the same time, clear, automated rules can restrict opportunistic management behaviour (doi.org 2024a). The figures become more objective and resilient, which strengthens the basis for good corporate governance.

Conservative Accounting as an Anchor of Stability

High data quality is the prerequisite for the consistent application of the prudence principle. In theory, conservative accounting — i.e., the faster recognition of losses than of profits — is a cornerstone of bookkeeping. In practice, unclean data and manual processes can make implementation difficult.

Automated processes can support the application of the prudence principle. A system can be designed to report negative deviations, such as cost overruns or declining margins, immediately and consistently. The 'bad news' can thus be systematically identified earlier.

This can have a measurable, stabilising effect. A study that analysed macroeconomic data found that conditionally conservative accounting can have a negative and statistically significant effect on economic volatility (doi.org 2023). In other words, cautious and high-quality reporting can contribute to stability because it can reduce surprises.

This effect is also evident in forecast accuracy. The same study showed that conservative accounting can also significantly reduce the forecast errors of the gross domestic product (doi.org 2023). Applied to a company, this means: Better and more reliable historical data are the basis for more accurate and thus more useful forecasts. The 'garbage in, garbage out' principle applies here without reservation.

Automation: The Path to Reliable Processes

The concrete path to higher data quality is through the automation of recurring procedures. Skopa automates the Excel layer that many companies have built around their ERP system. We do not replace the ERP system itself, be it SAP, Abacus, bexio, or another solution. We stabilise and automate the chain from data export and recurring reports to the final submissions to banks, authorities, or the board of directors.

Through automation, error-prone manual steps such as copying and pasting data can be eliminated. The processes are standardised, documented, and repeatable at the touch of a button. This not only reduces the complexity that is considered a risk factor (doi.org 2024a). It also frees up valuable time for analysing the figures instead of preparing them.

In addition, automated systems create the opportunity to systematically detect anomalies. A study on fraud detection showed that an Autoencoder Neural Network algorithm could achieve an accuracy of 93% in identifying irregularities in financial data (doi.org 2024a). Such systems can recognise patterns that escape the human eye during manual review. This enhances internal control.

The development of new tools like Microsoft 365 Copilot also underscores the importance of structured data. These assistants are not magic tools that create order out of chaos. Their usefulness depends directly on the quality and structure of the underlying data. Features for improving access control and regulatory compliance show that governance can remain a central theme (learn.microsoft.com 2026).

What You Can Do This Month

Improving data quality begins with an honest assessment. It is not necessary, and rarely sensible, to overhaul the entire reporting system at once. A focused approach is more effective. Start with a single, but important, process to gain concrete experience.

• Identify a recurring report that regularly requires manual corrections and is used for important decisions. This could be a monthly management report, a liquidity plan, or project controlling.
• Document the entire process in writing and without gaps. Clarify: Who supplies which data? What manual adjustments are made? Who reviews and approves the result?
• Measure the time spent each month on creating, reconciling, and correcting this report. Track the net working time and also consider the time for queries and correction loops.
• Examine which of these steps could be simplified with existing tools. Often, Power Query, which is included in every recent version of Excel, is a powerful tool for stabilising data preparation.

This analysis creates a clear, fact-based foundation. It shows where the greatest risks to data quality lie and where the leverage for process improvement is greatest. On this basis, you can make informed decisions for the next steps and regain control of your data.

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