Making data management more efficient: What are the hidden opportunities for optimization?

A customer journey tailored to the target group is essential when it comes to selling effectively. And the decisive nudge is usually done nowadays through product information. Complete, correct and high-quality product data is therefore an absolute must for both retailers and manufacturers – consistent across all channels. This is made possible by an efficient data management system, such as a Product Information Management (PIM) system.

As a “Single Point of Truth”, a PIM system consolidates all data available in the company. For example, this data might come from ERP, e-Commerce systems, MAM/DAM and other individual “data pots” held in a central location. As a result, a PIM system not only provides central access to data for all employees, but can also maximize customer satisfaction in the long term. By providing uniform and easily maintainable data, it forms the basis for a target group-specific, up-to-date and channel-specific customer approach (cross-channel publishing), combined with the highest data quality. Companies also benefit from a significantly reduced time-to-market and more efficient processes.

By taking a close look at the following seven areas, you can determine just how future-proofed your data management really is. Examining these areas carefully will create transparency and provide incentives to get the best out of your data or PIM system. We recommend you test where your data management is already mature, where there is room for improvement and where urgent action is needed.

1) Make the effectiveness of your PIM system measurable and comparable through KPIs

PIM systems are often implemented without a specific baseline goal being defined first. Yet such goals are needed to make a judgement about whether the system supports the business objectives or not. Assessing the extent to which a PIM system contributes to efficient data management, and where there is room for improvement, cannot be done without KPIs that make the efficiency of the system measurable.

In the worst case scenario, the software implemented proves to be an uneconomical cost sink that doesn’t provide support for the actual business purpose. KPIs which were defined at the outset illustrate the effectiveness of a PIM system, and make companies capable of making sensible decisions. They form the baseline, which determines the next course of action, 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 business objectives

Efficient data management only works if the process is viewed within the bigger corporate picture, and the PIM system can meet the requirements set. For example, a high volume of returns or a loss of public image can indicate that your product data is inconsistent or incomplete in the output channels, and therefore leads to customer dissatisfaction. A clear data strategy that supports business objectives and maintains high data quality minimizes these risks.

3) Organizational roles ensure structure and clear areas of responsibility

Once KPIs and strategies have been defined, it’s necessary to focus on the organizational framework, and to check whether the necessary resources are available to achieve the specified goals. The focus is on the performance of the organization: Which roles are defined in the process, and what purpose do they serve?

It’s necessary to align the setup of the PIM system with the corporate goals in order to identify potential areas for optimization. It should also be ensured that the employees working on the system are sufficiently trained to be able to perform their tasks.

The success of initiatives around uniform, consistent or automated processes in the PIM system will not be achieved if data management is not organizationally anchored in the company, and responsibilities, tasks and competencies are regulated at varying levels.

4) Maximize your data quality by adhering consistently to data governance rules

Data can only be processed in an economically viable way if standards (such as product descriptions, units of measurement, dates and item or customer numbers) are defined and adhered to. In addition, there must be permanent adherence to legal framework conditions, such as the guarantee of data protection.

Indicators that there is potential for improvement, especially in the area of data governance, can include duplicates, or incomplete or incorrect address lines. It becomes particularly tricky if the lack of data governance means that compliance with GDPR is only possible to an insufficient extent. Permanent data governance ensures that data quality is maintained through compliance with standards – but only if appropriate specifications and data models are available beforehand that enable product data to be provided in a uniform and complete manner.

5) Lean processes avoid duplicate data management and enable fast reactions if changes are required

It is often the case that many departments work with a PIM system. This makes it necessary to harmonize or link processes so that all those involved can work with the same data set. In particular, release processes of products relevant for approval that are subject to legal requirements (e.g. in the food or medical sector) must be clearly structured and documented. In addition, companies should consider monitoring existing processes in order to be able to eliminate sources of error at an early stage.

In the case of product recalls, 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 item is removed from the product range, the online shop and the shelves, and even cash register systems receive the information. 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

A well-thought-out data architecture which is tailored to the company’s objectives is essential to keep the complexity associated with a large number of items or product data as low as possible, and thus increase transparency for all those involved who work in the PIM system and with the product data. The more data that needs to be managed, the more the subsequent maintenance effort pays off: required information can be found more quickly thanks to a clear and intuitive structure. The software therefore becomes a central knowledge database. In order to prevent chaos in further product data management, data models should be catalogued routinely and checked for consistency.

7) Find the right balance between automation and customization, and use technologies according to your business goals

Optimization possibilities can also be identified for the technology itself. In order to identify these possibilities, one should be clear about the purpose of the PIM system. For some companies, the online shop is the linchpin of all activities, while for others the print catalogue with technical data sheets still plays the most important role. And then other companies might need “a bit of everything”. These requirements then affect the subsequent procedure.

If you want high-quality and individual information, this means a large amount of manual maintenance efforts. If a large amount of information, possibly in several languages, needs 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 far as possible in order not to tie up too much capacity.

Even though many of these questions already play a major role during PIM implementation, it makes sense to regularly evaluate the answers. Likewise, the objectives and the requirements set should also be regularly reviewed for their meaningfulness and topicality, and adjusted if necessary. Data management is a process that requires a permanent examination of optimization possibilities in order to get the best out of your own data – the actual company treasure – and to achieve your goals!

This article is based on the parsionate Health Check, which provides incentives and actionable recommendations for the optimization of already-implemented product data management systems by focusing on seven central questions. Further information can be found at PIM-Health Check.



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