Reliable information in controlling: Why data quality is crucial

Data quality is not just a buzzword in modern controlling but the crucial factor that determines success or failure in analysis and decision-making. Particularly in controlling, where every decision is based on precise data, poor data quality can have serious consequences. But what exactly does “data quality” mean? At its core, it describes how reliable and accurate the information is on which evaluations and forecasts are based. Data is the fuel that guides companies through their daily business operations, and like a vehicle, the quality of the fuel has a significant impact on performance. Poor data quality can lead to misjudgments, inefficient processes, and, in the worst case, wrong business decisions.

But what causes such data quality issues, and how does this affect the controller? Data deficiencies can occur at various points: during data entry in departments, due to incorrect maintenance of master data, or through poorly maintained transactional data. These errors not only affect the analysis but also the decisions based on this data.

To illustrate the problem, let’s consider the following example: A company that manufactures T-shirts wants to analyze its sales figures. The controllers notice that two black T-shirt models generate more sales than five white models.

The sales figures are presented as follows:

At first glance, it seems that black T-shirts are particularly successful – despite only having two models in the range, 300 units were sold. This would quickly lead to the conclusion to increase the production of black T-shirts.

However, upon closer inspection, it turns out that three of the items categorized as “white” are actually black. A simple error distorts the entire data analysis and leads to decisions based on false assumptions. After correcting the master data, it becomes clear that black T-shirts are even more successful than initially thought. A decision based on faulty data could have led to missing out on valuable opportunities.

The corrected table looks like this:

Now it shows that there are not two but five black T-shirts in the range – and with 500 units sold, they are the clear best-seller. An error in data quality could have led to the company not adequately increasing the production of black T-shirts, thereby missing out on sales potential.

To illustrate the potential magnitude of a seemingly small error, consider that it’s not always about such small quantities and attributes. Imagine dealing with 10,000 different products instead of just four items. And instead of four master data fields like material, fit, size, and color, there are suddenly over 200 attributes per item. In practice, this means dealing with more than a million data points. It’s easy to lose track, and erroneous data doesn’t immediately surface. Often, such errors are discovered by chance when data is evaluated as part of a new reporting structure. At the beginning of a new reporting process, many or major master data errors often emerge, which need to be corrected. Over time, these errors usually become fewer and more detailed.

Returning to our T-shirt example: Now that the controller has discovered the error, the design team must be informed that the master data is incorrect and needs to be corrected. However, this often reinforces the controller’s image as a “fault-finder,” as they regularly point out such inconsistencies. Unfortunately, this often cements the negative image of the controller as someone who only points out errors.

Data Errors: An Everyday Phenomenon for Controllers

Unfortunately, data errors like these frequently occur in a controller’s daily work. Reality is often even more complex. In many companies, there are thousands of products, with hundreds of attributes per item. With such volumes of data, a small oversight in data maintenance can have severe impacts on evaluations. Controllers, therefore, not only need to perform analyses but often act as “data guardians,” identifying and correcting errors.

One common problem in a controller’s workday is dealing with transactional data. Transactional data arises from business processes like orders, inventory movements, or cancellations. How can you imagine this concretely? A sales department deletes canceled order items from the system instead of booking them correctly as canceled. As a result, the database cannot recognize the cancellation, and the order intake figures remain erroneously high. The result is a flawed analysis, forcing the controller to dive deep into the data structure to find the cause of the discrepancy.

The Controller’s Role in Ensuring Data Quality

Ideally, controllers identify potential data errors early and can fix them. In many cases, however, this is not so simple, especially when data sources are inconsistent or unreliable. A particularly problematic scenario arises when companies rely on Excel for their planning, with numerous people working on different versions of the same document. In such cases, errors are almost inevitable. Different edits, manual input errors, or outdated information often force the controller to embark on a tedious error hunt – almost like the proverbial search for a needle in a haystack. This makes thorough analyses difficult and significantly impacts the decision-making foundation.

Improving Data Quality with Centralized Platforms

With a centralized, cloud-based planning platform like QVANTUM, this problem is significantly alleviated. Instead of struggling with numerous Excel files and their susceptibility to errors, all participants work with the same, consistent data. This is where QVANTUM comes in and offers a solution. The direct data transfer from systems like SAP or DATEV ensures that the information is always up-to-date and synchronized. This means that the controller spends less time cleaning data and can instead rely on solid analyses. Additionally, the manual data comparison is eliminated, as all changes are automatically recorded in the central platform.

The result is a much more efficient planning process, enabling the controller to respond faster to changes and make more accurate decisions. QVANTUM’s centralized approach not only simplifies data management but also improves the quality and reliability of the planning foundation.

Conclusion: Data Quality as the Key to Success

Data quality is not just a technical detail but the foundation for informed decisions in controlling. As shown, even small errors in master data can lead to significant misjudgments that negatively impact business strategy. By using centralized platforms like QVANTUM, these sources of error can be minimized, allowing controllers to make precise and efficient plans. Ensuring high data quality is therefore one of the most critical tasks to optimally respond to changes in the company over the long term.

Your Next Step: Proactively Ensure Your Data Quality

Whether you’re already struggling with inconsistent data or want to avoid potential errors from the start – being proactive is better than reactive. With QVANTUM, you can optimize your planning processes and ensure that data quality remains consistently high. This way, you can avoid the typical errors that arise from Excel-based processes right from the start.

Feel free to contact us for a no-obligation consultation to find out how QVANTUM can support your controlling and planning tasks.

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