7 Steps to Efficient Data Management

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 achievement is possible by improving customer experience and enhancing visibility into product availability. Moreover, 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, companies implement PIM systems without defining a specific finish line. If this is the case, stakeholders can judge whether the system supports the business goals or not. The exact extent to which PIM contributes to efficient data management. Also, where there are optimization possibilities, cannot be classified without KPIs that make the efficiency of the system measurable. In the worst case, the introduced software 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 relies on viewing the process in the company context and ensuring that the PIM system can meet the requirements. A high number of returns or a loss of public image may indicate inconsistent or incomplete product data in the output channels, leading 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 you have defined KPIs and strategies, it is important to focus on the organizational framework and assess whether you have the necessary resources to achieve your specified goals. This involves evaluating the performance of the organization, determining the roles involved in the process, and understanding its purpose. By comparing the performance of your PIM organization with your corporate goals, you can identify areas for optimization. The employees working in the system must receive adequate training to perform their tasks effectively. It is advisable to consider implementing initiatives that promote uniform, consistent, and automated processes within the PIM system. Integrating data management into the company and clearly defining responsibilities, tasks, and competencies at various levels is essential for the success of data management.

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

Data processing can only be economically profitable when adhering to defined standards such as product descriptions, units of measurement, dates, and article or customer numbers. In addition, there is permanent compliance with legal framework conditions. Indicators that there is room for improvement, especially in the area of ​​information management, can include duplicates or incomplete or incorrect address lines. A lack of this information makes compliance with the GDPR particularly tricky. Adhering to standards ensures that permanent database management maintains high data quality.  It happens only if corresponding specifications and data models are available beforehand. This enables 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. In order to ensure 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. This ensures that sources of error can be eliminated at an early stage.

In the case of recall campaigns. The software architecture, infrastructure, and interfaces must support the processes in such a way that it is possible to react extremely quickly to new circumstances. The range removes the article, the online shop or the shelves make it disappear. Cash register systems receive information. Therefore, companies should carefully examine which scenarios require which measures. They should not forget to evaluate 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 this all while actively increasing transparency for all individuals involved in the PIM system. A well-thought-out data architecture tailored to corporate goals is fundamental for managing product data. Managing more data increases the costs of maintenance. 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 catalogued 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 have a clear understanding of the purpose of the PIM system. While the online shop is the linchpin of all activities for some, the print catalogue with technical data sheets plays the most important role for others. Still, others need “a little of everything”.

These requirements determine the further procedure. If you want high-quality and individual information, this means a lot of manual maintenance. It is advisable to check the technology for rapidly communicating a large amount of information, possibly in several languages. For example, its degree of automation and to standardize or automate the PIM mechanism as much as possible. Even though many of these questions already play a major role in the PIM implementation, it still makes sense to continue grappling with the answers. The objectives and the requirements should also be regularly checked to ensure that they are meaningful and up-to-date. If necessary, adjusted. Data management is a process that requires a permanent examination of optimization options. And all in order to get the best out of your own data, the actual company treasure, and to achieve your goals!