MDM and Data Quality

Data plays a key role in the success of marketing and sales processes as well as in supplier and customer loyalty. Companies need to think about which combinations of data they can derive the greatest possible value from. Networked information about products, customers, locations, suppliers and other business partners can provide exciting insights and provide information on how processes can be optimized and profitable communication measures can be strengthened. But for them to be able to lead to such insights, they must be of the right quality – and achieving this is a major challenge for most companies.

Why Is Data Quality So Important?

Data quality is an important and much-discussed topic – but what does this rather vague term actually mean? In fact, it is not for nothing that this term is so vague. What is sufficient quality depends primarily on what the respective data is needed for, who needs it and what it is intended for. An example: The one in a Product information system Product data held is required, on the one hand, by customer service employees in order to be able to answer questions about the products, their use or even ingredients. In this use case, good data quality therefore characterizes the completeness and availability of detailed product data. Marketing employees, in turn, also need media content such as images, videos or documents as well as descriptive editorial content on the basis of which they can create effective messages.

Data quality also varies between the different purchasing systems. For example, the various retailers that a manufacturer supplies with its products often have different product information requirements, websites have different rules for displaying product content than online marketplaces, and management requires different reporting than the product manager. In addition, there are numerous regulatory requirements that companies from the food or medical technology industries, for example, must take into account. For example, supply chains must now be transparently comprehensible and ingredients must also be clearly declared. It is therefore important to precisely record every relevant use case in which data is required and to consider in which form the respective data must be available in the best case.

The consequences of poor data quality are therefore just as varied as the quality features themselves. For example, inconsistent product information can cause uncertainty among consumers and damage the brand's reputation in the long term. Wrong or missing ingredients can even be hazardous to the health of allergy patients and violations of regulatory regulations can have costly legal consequences. In less drastic but nonetheless business-critical scenarios, companies simply cannot share relevant product data with other divisions or international locations and therefore have difficulties selling and marketing the products.

Which Factors Determine the Quality of the Data?

The desired form of data depends on several aspects, depending on the use case. This includes factors such as completeness, timeliness, consistency, validity, uniqueness, compliance or availability. On the one hand, data quality depends on the fact that the information content of the transmitted data is sufficient so that downstream processes can be carried out as desired. The timeliness of data actually always plays a major role – whether in product communication or in reporting to management. If the information is out of date, this poses major risks. Data consistency must also be ensured across all processes, channels and systems in order to maintain the trustworthiness of the brand and thus also its reputation. The validity and uniqueness of data are fundamental quality features that ensure that the attribute values are even admissible and that the available data is stored without redundancy. Compliance is a sign of quality that plays a major role in many areas. In the food or medical technology industry, for example, there are numerous rules and regulations that must already be stored in the data model in order to represent the required information. Since data transparency [CM1] is an important issue for companies today, the availability of data is also playing an increasingly important role.

How Does MDM Ensure the Quality of the Data?

The biggest obstacle to ensuring data quality is data silos – unintegrated data streams and processes and poor access to individual systems. Master Data Management (MDM) as a cross-system instance that connects data sources centrally, consolidates and sensibly regulates data, is therefore the right way to ensure, permanently monitor and manage the quality of the data.

MDM is therefore an important tool for companies to strengthen their product and brand communication, support sales processes, optimize organizational processes and ensure compliance. This applies in particular, but not only, to companies with fragmented system architecture that are active in heavily regulated markets.