Avoiding poor data quality - The guide to reliable data

Outdated figures, error-prone Excel tables & countless file versions. Do these facts sound familiar to you? 49% of controllers in a joint survey by research and analyst firm Techconsult and QVANTUM see unreliable data as their main challenge. Poor data quality is your biggest challenge and makes your day-to-day work as a controller more difficult than it needs to be. We shed light on the biggest challenges and provide you with a few strategic intervention measures to make your everyday life easier.

Inconsistent and duplicated data

When working with multiple data sources, deviations occur in the format, unit or spelling. This results in duplicates and overlaps. These inconsistencies are then transferred to a central system when data is migrated. If these problems are not continuously rectified, they multiply and make entire data sets unusable. The result: distorted analysis results.

The solution: A data-driven culture

Companies must explicitly promote values, behaviors and norms that enable the effective use of data. For you as a controller, this means the following: You ensure a standardized definition for data quality with specific measurable key figures. Whether these standards are being adhered to must be monitored and evaluated on a regular basis. If not, appropriate measures are developed to rectify errors. In addition, all other employees should also participate in this solution process. By identifying and reporting data quality problems or, ideally, even solving them themselves.

Incorrect data and excessive amounts of data

Data quality can deteriorate over time as data loses its integrity on its way through different systems. This is particularly serious with customer data, as incorrect or incomplete data can quickly cause problems and significantly more work for you in the controller position. When dealing with this data, it is important that it is always correct. If an error does creep in, it is very difficult to identify the source of the error.

It is the same with too much data, because the more data there is, the more often errors are overlooked. For example, if you are looking for data that is relevant for certain analysis projects, it is easy to get lost in the flood of data. The problem is that even small errors become more serious as the amount of data increases.

The solution: Correct the problem at its source by using the right tool

Data quality problems are often only resolved temporarily so that individual employees can continue working. Such a solution is not effective in the long term. Instead, create a uniform data model with authorization systems and precise workflows. The aim is always to work on the source of the problem to prevent recurring errors.

The key is a software for process-oriented automation and best practices to improve the quality of your data culture. With the right software, you can identify errors at an early stage and eliminate them at the source. Not only will your day-to-day work in controlling be simplified, but you can save a lot of resources that you would have used up searching for errors.

Unclear definitions

You probably know it from your day-to-day work: in sales, turnover is defined differently. And your colleague talks about FTEs, but means headcount. Confusion can quickly arise if everyone has a different idea of these terms. It is precisely these unclear definitions of data that lead to numerous errors in reports and analyses.

The solution: Standardization of the data with a single source of truth

It is important to use a fixed list of values and definitions to avoid such errors. The expertise of a controller is particularly important here, as they are the ones who are most familiar with the necessary standardization. You should define terms clearly in order to create a single source of truth. There are also helpful standardization tools and techniques that help to eliminate ambiguities and improve data quality.

Outdated data

Old data usually only consumes storage and creates confusion. They are often not deleted for years and occupy several unsorted folders. This creates additional work for you when you have to process this data after a long time. It also requires considerable resources to eliminate this data chaos once it has arisen.

The solution: An effective data management strategy and more frequent planning

To avoid these problems, it is important to regularly check, update and cleanse data. Develop a strategy that ensures the data is up to date. In addition, a rolling forecast or intra-year planning is recommended instead of a one-off annual plan. This prevents data from becoming outdated and increases data quality.

Find out how to promote a data-driven mindset in your company and what measures you need to establish a data culture in your company in this blog post https://getqvantum.com/blog/datenkultur-in-unternehmen-etablieren/