One goal of Product Information Management (PIM) is to facilitate that consumers of product information can find a product they are looking for. Facilitating that includes feasible functionality and optimal organization of data.
Search
There is a whole industry making software that helps with searching for products as touched in the post Search and if you are lucky you will find.
However, even the best error tolerant and super elastic search engines are dependent on the data to search on and are challenged by differences in the taxonomy used by the one who searches and the taxonomy used in the product data.
As we are being better at providing more and more data about products that also makes issues in searching, as we are getting more and more hits of which many are irrelevant for the intention of a given search.
Drill down
You can start by selecting in what main group of products you are looking for something and then drill down through a more and more narrow classification.
Again, this approach is challenged by different perspectives of product grouping and even if we are looking for standards, there are too many of them as described in the post Five Product Classification Standards.
Traverse
The term traverse has (or will) become trendy with the introduction of graph technology. By using graph technology in Product Information Management (PIM) you will have a way of overcoming the challenges related to using search or drill down when looking for a product.
Finding a product has in many use cases the characteristic of that we know some pieces of information and want to find a product that match those pieces of information, but often expressed in a different way. This fit very well with the way graph technology works by having a given set of root nodes from where we traverse through edges and nodes (also called vertices) until we end at reachable nodes of the wanted type.
In doing that we will be able to translate between different wording, classifications and languages.
At Product Data Lake we are currently exploring – or should I say traversing – this space. I will very much welcome your thoughts on this subject.
Liliendahl on Data Quality 
This is very interesting, is it something you’re working on with live product data? Would be good to see some actual use cases in operation
Thanks for commenting Richard. We are currently testing different tools and approaches in sandbox in order to deploy in the right way in live production.
Henrik – Have you considered the possibility that Search and Facets (of a Product – or any other area of interest) are related? The problem, for example, that most taxonomies have is they fail to realize that universal classifications have to work along side specific categories.
Indeed John, I see this as a huge challenge as well. And something we have to deal with.