Linear Data Flows Versus Data Webs
The last major issue occurring in many enterprise ecosystems is the development of data webs instead of linear data flows. A linear data flow (example below) is straight-forward: Data flows from one system to another in a linear fashion.
Data Webs occur when enterprises link systems in multiple directions, increasing the complexity of the flow of data. This creates timing issues as different events in different systems may require another system to hold on a process until the originating system is complete. Below is an example of a data web.
The differences between these two methodologies is obvious, but the consequences are not. In the linear example the ERP system only needs to know what the PLM system needs it to know. Similarly, the PIM system only needs to know what the ERP systems needs it to know. This makes timing, data availability and workflow easier to manage. In the second example, the CMS must understand what is happening in all 3 system simultaneously to know what the next step in the process is. The timing of this becomes complex, especially when not all data elements are available from the feeding system at the time of origination.
If you have a data web a PIM implementation will be more difficult and more expensive. Somewhere it will force a downstream system to understand the timing and availability in systems it does not share common purpose with. This will increase the timing it takes to implement a PIM, require more complex integrations between systems to get the timing of data availability correct, and increase the cost of your implementation. Lastly, it will make your PIM bound to these systems in ways that are impossible to abstract from that will make your PIM install less adaptable and scalable in the future.
Product Taxonomy and Data Governance
Two of the least understood elements of a PIM install are how to integrate that tool into a data governance program and the importance of a properly-designed product taxonomy. Product taxonomies provide consist data objectives for category-specific attributes, which are vital to be able to syndicate data to downstream partners. Without a rules-driven, documented, scalable product taxonomy the ability to syndicate data to retailers that have already built their own product taxonomies will be laborious, manual, and prone to error.
Product taxonomies also speed products to market when designed correctly. By allowing a user to understand the downstream requirements for product specification data in a consistent fashion, product taxonomy avoids multiple touch-points over the lifecycle of a product to determine characteristics of that product. No more waiting for email chains to complete to syndicate data: The data requirements are laid out in a simple format that EVERY PIM tool enables.
A PIM install without a governance plan will begin data remediation projects to fix their data often within a year of original implementation.
Building a functional product taxonomy is important, but governing that taxonomy and the other elements of your PIM install is even more critical. If governance is not baked into a data strategy, the product taxonomy and PIM attributes will quickly devolve into a mess. Without the controls to document why an attribute exists, who owns that attribute, when that attribute can be changes, who can change that attribute, and where that attribute originated from and is destined to, chaos ensues. It doesn’t take long. A PIM install without a governance plan will begin data remediation projects to fix their data often within a year of original implementation.
Therefore, forgetting about Product Taxonomy and Data Governance is crucial to the long-term viability of your PIM implementation. Yes, you can implement a PIM without these included. No, your data quality will not increase over time if you do not.
Plan Your Product Data, Not Your Tool
In summary, your PIM implementation will be much more successful if you plan your product data strategy. Understanding the implications of product data webs, product taxonomy, and supply-side versus sell-side data attributes is critical to achieving scalable adaptable PIM implementations. Understanding the importance of Product Taxonomy and Data Governance is also critical to improving your data quality over the long term. Traditionally these activities have been ignored by PIM implementors to reduce the implementation cost they quote at the outset, but at the peril of failed PIM projects.
The most successful PIM implementations do not start by looking at the tool. They begin with understanding your data, your systems, your workflow, and your data strategies. They incorporate all the elements of product data in a cohesive plan that provides an effective scalable data model that is controlled by an effective data governance plan and an adaptable tool design.