Perfect product data quality with PIM

In B2C, the quality of product data is primarily characterized by three criteria: Completeness, correctness and timeliness. In addition, the consistency of the product data must be maintained in all contexts. This aspect in particular represents a major challenge for many companies, especially if the product data is managed decentrally and in folder structures, and if many different marketing and sales channels have to be served. In B2B, there are additional industry-specific quality criteria that must be met to ensure smooth data exchange. These are the so-called data standards, which specify formats and rules for handling product data and are intended to help companies along the supply chain to cooperate with each other as efficiently as possible.

In order to be able to meet quality standards on all levels without having to use excessive resources, you need a single source of truth – in other words, a reliable data source for all employees and areas of application. With a PIM system that centralizes and standardizes the management, maintenance and enrichment of product data, you can establish such a data source in your company.

As the hub for managing and controlling product data, a PIM system covers the entire product content lifecycle and ensures the quality of all product communication, step by step.

The first step involves onboarding the product data into the PIM system from various data sources. These include internal sources such as individual Excel sheets or an ERP system, as well as external data pools or data sources from business partners.

The data imported into the PIM system is then validated, meaning it is examined for correctness and completeness. Quality deficiencies such as duplicates, deviating file formats or missing information are marked so that corrections can directly be made.

Next, the quality-checked data is transferred to the previously defined data model of the PIM system. The data model determines how the products and the associated information are structured, i.e. created and linked with each other.

The data is then enriched according to the previously defined classifications, resulting in freely customizable communication elements. For instance, product information can be combined with images or marketing copy.

The final step involves the release and automated distribution of the finished product content to the connected communication channels. After that, it is important to monitor the reactions of the target group in the individual channels and adjust the communication processes if necessary.

Ultimately, a PIM system like mediacockpit not only enables the onboarding of quality-assured data, but also ensures that quality standards are maintained in all downstream data processes. The prerequisite for this is a well thought-out data model that covers all requirements that a company must fulfill within an industry, as well as strong data governance that defines the quality requirements within a company and regulates the handling of product data.