First steps to getting ready for your product information management or product master data management initiative.
We recently published an e-book called “Preparing for PIM.” It’s aimed at business decision-makers who are responsible for gathering, managing, and publishing information about the products their companies make or sell. Here’s a summary of the steps it outlines:
1. Quantify or qualify the business problems resulting from product data problems. Organizations often recognize problems with their product information when they first appear as problems with key business processes. After a while you realize that the common denominator among these process problems is product information problems. Having identified these, it will be easier to quantify them in order to build the business case for correcting them.
2. Identify participants, champions and sponsors. An important point to remember is that yours isn’t an IT-driven or IT-sponsored undertaking. You’ll want to include IT personnel, but you and your business colleagues need to be the motive force. You need to identify the key executives who are most affected by inefficient business processes resulting from bad product data. Approach those with the most “pain” and diplomatically recruit them as a sponsor or champion of your PIM initiative.
3. Identify locations and sources of product data. You’ll also need to identify the attributes, descriptions, images, and other content associated with the data. Begin by creating an inventory of where the product content originates, for what purpose it was originally created, where it’s managed, which people and processes use it, and which media and channels consume it. Ideally the metadata for these assets are stored and managed in a registry or repository, which will help in identifying some of these aspects of the data.
4. Create a data quality framework. You need clean, standardized, rationalized, and normalized data as you start your PIM initiative. Which means you also need to determine what constitutes quality data according to your requirements and establish a framework that defines and quantifies those metrics. These may include completeness, accuracy, consistency, continuity, timeliness, redundancy, and uniqueness, among others. It’s up to each organization to identify and define the metrics that matter most to the processes they’re addressing.
5. Perform a data quality audit. The exercise of creating your data quality framework should highlight a number of areas where data problems needs to be fixed. Having identified these, you need to audit your data to determine at which points they fail to live up to the metrics in your data quality framework. The report produced by this audit will help quantify the level of data quality work that needs to be done in advance of the actual PIM deployment.
6. Establish a data governance council. Your organization may not be ready for a formal data governance council with executive overseers and teams of data stewards. It may be best to simply “re-purpose” the existing team of process participants and sponsors into a data governance committee for data relating specifically to your PIM initiative. Note that over time, this group may expand into a more formal data governance council, especially as your efforts intersect and overlap with those of other like-minded people in your organization.
We invite you to download the complete e-book, “Preparing for PIM,” which offers greater details on each of these points. Let us know if you’d like to talk further about your product information challenges and aspirations.