However, many companies have accumulated unmanageable mountains of data. Over the years, data has been stored in various formats, from different sources, and in different systems. Often, this data includes outdated, irrelevant, erroneous, or duplicate records. Especially in the context of digitalization, this poor data quality results in massive administrative and operational overhead. The resulting costs are not the only problem. Employees also lose track of where to find specific information and what data they can use for what purpose. This slows down daily business processes.
Duplicating master data, in turn, can cause confusion, data misuse, and business disruptions. For example, duplicate addresses can lead to unnecessary postage costs or misdirected deliveries.
Not only in highly regulated industries is awareness growing of the potential problems caused by inadequate data. Many companies now understand data as a key success factor for their business strategy and have recognized how crucial the right data quality is.
Data cleansing is just the beginning
Smaller companies often find it easier to identify and correct data errors during data cleansing, simply due to the smaller amount of data they store. However, if the data volume is larger or unmanageable for other reasons, a holistic approach to improving data quality is recommended. For rapidly growing data volumes, planned system changes, or extensive digitization projects, comprehensive data cleansing followed by ongoing data quality assurance is essential to manage the data effectively.
The process begins with a data audit, which analyzes in detail what data exists within the company and how it supports business objectives. Key questions include: Which data is central to our business processes? How do we want to use the data? Where are the data gaps? What requirements must our data meet to be truly valuable to us? Such a thorough analysis not only uncovers potential data problems but also forms the basis for future quality guidelines, enabling long-term improvement of the entire data portfolio.
When defining these data quality goals, it is advisable to look to best practices of comparable companies, as well as industry standards and compliance requirements. Interface requirements are also important. Ultimately, the data should be able to be exchanged seamlessly between systems and business units, as well as with suppliers and customers.
Establishing best practices
To consistently implement the new data strategy, some companies create a corresponding organizational structure, for example by appointing a Chief Data Officer or an internal "Data Champion." Best practices for handling data are always based on three pillars: people, processes, and technology. A solution for the sustainable improvement of data management must therefore encompass all three areas.
Numerous software solutions for data transformation, data cleansing, and data management are available on the market. Which ones are suitable depends on the company's current data situation, the volume and complexity of the data to be managed, and also on future requirements, such as those that planned digitization projects might bring. High-end tools offer flexible options for defining detailed data quality guidelines, validating data, correcting data errors, and handling exceptions. Such a platform facilitates the establishment of best practices and enables continuous quality assurance, even with growing data volumes. Comprehensive reporting and automatic alerts provide a transparent overview of the data's status at all times.
The return on investment achievable through improved data quality depends, among other things, on the initial data audit. High-quality data directly reduces the time and costs associated with troubleshooting or manually processing data. Clean data also enables smooth business processes and reliable analyses. It facilitates compliance with GDPR regulations and forms the basis for valuable customer relationships and successful strategic decisions – all factors that strengthen a company's competitiveness. In conclusion: Ensuring data quality may be a challenge for many companies, but the effort is worthwhile.