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At the core of an efficient data management strategy lies the ability to measure and track key performance indicators (KPIs) that reflect the effectiveness of your PIM process. This requires a clear set of solutions and strategies, along with clearly defined responsibilities within your organization that help to ensure a clear structure of accountability. In order to maximize data quality and reliability, it is also important to consistently follow the rules and guidelines for data governance. 

By following these 7 key steps, you can ensure efficient data management for your organization.

The customer journey plays an important role in e-commerce, especially when it comes to online sales. A PIM (Product Information Management) system offers a variety of benefits for retailers and manufacturers, allowing them to effectively manage their product data across all channels. By having access to complete, accurate, and high-quality product data at all times, businesses are better able to engage with customers and ultimately drive sales. This can be achieved through improved customer experience, increased visibility into product availability and performance trends, reduced costs associated with managing data manually, and more.

How sustainable your data management actually is can be found by carefully examining 7 subject areas. The answers create transparency and provide incentives to get the best out of your data or your PIM system. Test where your data management is already mature, where there is room for improvement, and where there is an urgent need for action.

Data Management Steps

 

1. Make the Effectiveness of Your PIM System Measurable and Comparable Using KPIs. 

Often, PIM systems are implemented without having defined a specific finish line beforehand. If this is the case, it is possible to make a judgment as to whether the system supports the business goals or not. The exact extent to which PIM contributes to efficient data management or where there are optimization possibilities cannot be classified without KPIs that make the efficiency of the system measurable. In the worst case, the software that has been introduced proves to be an uneconomical cost eater that does not support the actual business purpose. Key figures defined at the beginning illustrate the effectiveness of a PIM system and enable companies to make decisions. They form, so to speak, the foundation that determines the further procedure and also allows conclusions to be drawn about the future viability of the software.

2. Program & Strategies: Risk Reduction Through Reliable Data Management in Line with Corporate Goals. 

Efficient data management only works if the process is viewed in the company context and it is ensured that the PIM system can meet the requirements. A high number of returns or a loss of public image can, for example, be an indication that your product data in the output channels is inconsistent or incomplete and therefore leads to customer dissatisfaction. A clear data strategy that supports company goals and maintains high data quality minimizes these risks.

3. Responsibilities Within the Organization Ensure Structure and Clear Areas of Responsibility. 

Once KPIs and strategies have been defined, it is important to focus on the organizational framework and check whether the necessary resources are available to achieve the specified goals. The focus is on the performance of the organization: Which roles are stored in the process and what purpose do they fulfill? It is necessary to compare the performance of the PIM organization with the corporate goals in order to identify optimization potential. In addition, it should be ensured that the employees who work in the system are sufficiently trained to be able to perform their tasks. Initiatives relating to uniform, consistent or automated processes in the PIM system will not be successful if data management is not anchored in the company and responsibilities, tasks, and competencies are regulated at the various levels.

4. Maximize Your Data Quality Through Consistent Compliance With Rules in the Course of Data Governance. 

Data can only be processed in an economically profitable manner if standards such as product descriptions, units of measurement, dates and article or customer numbers are defined and adhered to. In addition, there is permanent compliance with legal framework conditions, for example ensuring data protection.Indicators that there is room for improvement, especially in the area of ​​data governance, can include duplicates or incomplete or incorrect address lines. It becomes particularly tricky when compliance with the GDPR is insufficiently possible due to a lack of data governance. Permanent data governance ensures that data quality is kept high by adhering to standards – but only if corresponding specifications and data models are available beforehand, which enable product data to be provided in a uniform and complete manner.

5. Harmonized Processes Avoid Duplicate Data Management and Enable Quick Reactions in the Event of Changes. 

Many departments normally work with a PIM system. This makes it necessary to harmonize or couple processes with one another so that all those involved can work with the same database. In particular, approval processes for approval-relevant products that are subject to legal requirements (e.g. in the food or medical sector) must be clearly structured and documented. In addition, companies should think about monitoring existing processes so that sources of error can be eliminated at an early stage. In the case of recall campaigns, for example, the software architecture, infrastructure, and interfaces must support the processes in such a way that it is possible to react extremely quickly to the new circumstances, the article is removed from the range, disappears from the online shop or from the shelves, or the information is received by cash register systems. Companies should therefore carefully examine which scenarios require which measures and whether the existing processes meet these requirements in order to avoid risks.

6. A Comprehensible Data Architecture Minimizes the Complexity of Product Data Maintenance. 

In order to keep the complexity that arises with a large number of articles or product data as low as possible and thus increase transparency for all those involved who work in the PIM system and thus with the product data, a well-thought-out and based on Data architecture tailored to corporate goals is fundamental. The more data that has to be managed, the more paid the maintenance costs will be: the information required can be found more quickly thanks to a clear and intuitive structure. This turns the software into a central knowledge database. In order to prevent chaos in further product data management as well, data models should be continuously cataloged and checked for consistency.

7. Find a Balance Between Automation and Customization and Use Technologies According to Your Company Goals. 

Optimization options can also be determined for the technology itself. To find out, you should be clear about the purpose of the PIM system. While the online shop is the linchpin of all activities for some, the print catalog with technical data sheets plays the most important role for others. Still, others need “a little of everything”. The further procedure depends on these requirements. If you want high-quality and individual information, this means a lot of manual maintenance. If a large amount of information, possibly in several languages, has to be communicated as quickly as possible, it makes sense to check the technology for its degree of automation and to standardize or automate the PIM mechanism as much as possible. Even if many of these questions already play a major role in the PIM implementation, it makes sense to continue to grapple with the answers. The objectives and the requirements should also be regularly checked to ensure that they are meaningful and up-to-date and, if necessary, adjusted. Data management is a process that requires a permanent examination of optimization options in order to get the best out of your own data, the actual company treasure, and to achieve your goals! 

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