Inbound and Outbound Product Data Syndication

If you google for the term Product Data Syndication you will get the explanation in a post on the sister site to this blog. The Disruptive MDM / PIM / DQM list blog post is called What is Product Data Syndication (PDS).

Inbound and Outbound Scenarios

Digging further into this subject one can divide the Product Data Syndication (PDS) scenarios as seen from the individual organization within a supply chain into inbound and outbound product data syndication.

As a merchant/retailer/dealer being downstream in the supply chain you will have these main scenarios:

  • Outbound product data syndication to marketplaces. This is the scenario covered by most solutions that are marketed as PDS solutions. The challenge here is that there are hundreds of marketplaces both internationally and nationally. These marketplaces have each their way of getting the product information. The advantage of such a PDS solution is that you as a merchant only need one downstream feed to (in theory) all marketplaces.
  • Inbound product data syndication from suppliers either directly from the manufacturer or through distributors. There are many ways this is done today stretching exchanging spreadsheets, getting the product information in your supplier portal, fetching the product information from each of the manufacturers customer portal, through data pools and, still in the emerging stage, utilizing a collaborative PDS solution (see further down).
  • Outbound product data syndication to large end users often being manufacturers utilizing MRO (Maintenance, Repair and Operation) parts.

As a manufacturer/brand owner being upstream in the supply chain you will have these main scenarios:

  • Outbound product data syndication to marketplaces, which most often only covers a fraction of the revenue.
  • Outbound product data syndication of product information for finished products to distributors and/or merchants. There are many ways this is done today stretching exchanging spreadsheets, putting the product information in each of the distributors/merchant’s supplier portal, exhibiting the product information in your customer portal, through data pools and, still in the emerging stage, utilizing a collaborative PDS solution (see further down).
  • Inbound product data syndication of product information for raw materials and MRO (Maintenance, Repair and Operation) parts from suppliers being other manufacturers, distributors and/or merchants. There are many ways this is done today stretching exchanging spreadsheets, through data pools and, still in the emerging stage, utilizing a collaborative PDS solution (see further down).

As a distributor/wholesaler being midstream in the supply chain you share the outbound PDS scenarios at manufactures and the inbound PDS scenarios at merchants.

In some cases, a marketplace can act as a data pool too.

Collaborative PDS

A Collaborative PDS Solution

In my eyes a collaborative PDS solution have these capabilities:

  • Catering for a win-win scenario between trading partners by allowing one uniform way of outbound push of product information from upstream trading partners (manufacturers, distributors) and one uniform way of inbound pull of product information at downstream trading partners (distributors, merchants).
  • Ability to work with all in-house Product Information Management (PIM) solutions and/or other in-house applications where product information is managed both for outbound push and inbound pull.
  • Can encompass outbound push to and pull from data pools and even other PDS solutions as elements in the total product information flow embracing both market standard product information and flow of individual product information that makes the merchant stand out from the crowd.

Right now, I am working with a collaborative PDS solution. This solution welcomes other (collaboratve) PDS solutions as part of the product information flow. And of course, also every in-house Product Information Management (PIM) solution out there. This PDS solution is called Product Data Lake.

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