Quality assurance in master data maintenance

The volume of data that companies process is growing steadily: digitalization and the Internet of Things as well as Big Data lead to large numbers of datasets flowing into IT systems. It is, therefore, extremely important to be able to process and save these methodically because poor data quality has a negative impact on your business success.

What does quality assurance mean for your master data maintenance?

Basically, the term data quality refers to whether a dataset is suitable or good enough for its purpose. A simple example is the address of a customer, which must be properly maintained in the database so that a sales representative can visit.

Although this may sound very trivial, this is not always the case. Due to poor data maintenance or the presence of multiple records which are not constantly updated and synchronized, the address of the customer that is displayed to your field representative may be inaccurate or obsolete, resulting in dissatisfied customers and additional costs for you.

For smooth processes, it is particularly important that the quality of your master data is guaranteed, kept to a standard and monitored. You can implement processes that ensure that new data always meets your quality requirements, for example with mandatory fields in input masks for the databases.

The quality of data can be assessed in three categories:

Intrinsic data quality – the value of the data itself, i.e. whether all data and values are correct and current

Context-related data quality – whether the data meets the requirements of the situations in which they are used

Presentation and availability quality – whether the data is presented in a comprehensible and consistent manner and can be accessed quickly

“Fitness for use” – what is usability?

Estimates in the US show that the US economy loses $ 600 billion every year due to poor data quality, so poor data clearly has a direct impact on business success. A lack of or poor quality control and quality assurance not only lead to a decrease in customer satisfaction, as in our example, but also to serious mistakes being made in corporate strategies or market constellations.

In specialist literature, the quality standards which companies should have regarding their data is often referred to by the term “fitness for use” – the data should be suited to a company’s needs and to achieving certain goals satisfactorily. In concrete terms, this means, for example, that every product should only appear once in your product database, together with all variations, information and technical data sheets. Different tools can be used to achieve this so that all duplicates are removed and all relevant information is collected in one place.

Quality assurance – but how?

The main problem of most companies is not an unwillingness to clean up their databases, but rather, in most cases, a lack of strategy or suitable tools. In a study by Camelot Management Consultants, more than half of the companies questioned said they were behind with master data quality. Less than a tenth of respondents said they used software to analyze and control data quality.

The Excel spreadsheet has become obsolete: There are now many powerful ERP (Enterprise Resource Planning) and CRM (Customer Relationship Management) tools such as SAP. These not only enable you to save and maintain your master data in databases, but also support or assume quality control and assurance for you. The target state on the data level does not have to be created from scratch, but it is important to ensure that the requirements for “clean data” are met, especially when new systems are introduced.

To ensure a smooth transition to clean data, quality assurance should be seen as a multi-step process. Change management is recommended here, as it helps to plan this process from the start to the final destination.

1. Convince staff and management

The importance of clean data for the digital transformation of your company is obvious to you. But is it also obvious to your employees? Use concrete objectives to raise awareness among employees and help them understand the importance of high-quality data, even if it entails extra work initially.

2. Analyze status quo

In order to get an idea of where exactly the weaknesses in the quality of your company’s master data lie, a simple analysis can be carried out using one of several quality management software tools. These tools give you an overview of redundant and incomplete datasets and can identify inconsistencies within the database on the basis of rules you have defined.

3. Rulebook and cleanup

If they are not already in place, you should now set clear rules for clean records: Which information is necessary, which is optional? How should the data be formated in individual fields? Once exact rules have been established, you can start the initial cleanup. Your goal should be a level of quality which can serve as a benchmark for the future.

4. Quality management system and collection of new data

Of course, ensuring the quality of your master data cannot be a one-off affair. You should make sure that you keep to your benchmark and your data remains fit for its intended use. It is, therefore, essential to schedule ongoing adjustments and controls. Again, a quality management software can be used for this. Please note: Most errors can be avoided at the data collection stage if input masks are adapted to your data requirements. You can do this by defining mandatory fields. ERP software can also be used to carry out an automatic completeness and plausibility check with each entry.

5. Data quality as a permanent component of corporate culture

Try to integrate quality assurance in master data maintenance into your company culture permanently and to create an awareness for its importance among your employees. Regular employee training helps to identify existing weak points and to anchor the importance of clean data management in the minds of the workforce.

Master data quality management is essential for the step into Industry 4.0 and the digital transformation of your company. While ensuring high data quality is a challenge, innovative ERP and analysis tools greatly simplify the process, and human errors can be largely avoided. A change of system or the upcoming digitalization of your business are great opportunities to clean up your data. Take advantage of them!


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