What is Product Data Syndication (PDS)?

Product Information Management (PIM) has a sub discipline called Product Data Syndication (PDS).

While PIM basically is about how to collect, enrich, store and publish product information within a given organization, PDS is about how to share product information between manufacturers, merchants and marketplaces.

Marketplaces

Marketplaces is the new kid on the block in this world. Amazon and Alibaba are the most known ones, however there are plenty of them internationally, within given product groups and nationally. Merchants can provide product information related to the goods they are selling on a marketplace. A disruptive force in the supply (or value) chain world is that today manufacturers can sell their goods directly on marketplaces and thereby leave out the merchants. It is though still only a fraction of trade that has been diverted this way.

Each marketplace has their requirements for how product information should be uploaded encompassing what data elements that are needed, the requested taxonomy and data standards as well as the data syndication method.

Data Pools

One way of syndicating (or synchronizing) data from manufacturers to merchants is going through a data pool. The most known one is the Global Data Synchronization Network (GDSN) operated by GS1 through data pool vendors, where 1WorldSync is the dominant one. In here trading partners are following the same classification, taxonomy and structure for a group of products (typically food and beverage) and their most common attributes in use in a given geography.

There are plenty of other data pools available emphasizing on given product groups either internationally or nationally. The concept here is also that everyone will use the same taxonomy and have the same structure and range of data elements available.

Data Standards

Product classifications can be used to apply the same data standards. GS1 has a product classification called GPC. Some marketplaces use the UNSPSC classification provided by United Nations and – perhaps ironically – also operated by GS1. Other classifications, that in addition encompass the attribute requirements too, are eClass and ETIM.

A manufacturer can have product information in an in-house ERP, MDM and/or PIM application. In the same way a merchant (retailer or B2B dealer) can have product information in an in-house ERP, MDM (Master Data Management) and/or PIM application. Most often a pair of manufacturer and merchant will not use the same data standard, taxonomy, format and structure for product information.

1-1 Product Data Syndication

Data pools have not substantially penetrated the product data flows encompassing all product groups and all the needed attributes and digital assets. Besides that, merchants also have a desire to provide unique product information and thereby stand out in the competition with other merchants selling the same products.

Thus, the highway in product data syndication is still 1-1 exchange. This highway has these lanes:

  • Exchanging spreadsheets typically orchestrated as that the merchant request the manufacturer to fill in a spreadsheet with the data elements defined by the merchant.
  • A supplier portal, where the merchant offers an interface to their PIM environment where each manufacturer can upload product information according to the merchant’s definitions.
  • A customer portal, where the manufacturer offers an interface where each merchant can download product information according to the manufacturer’s definitions.
  • A specialized product data syndication service where the manufacturer can push product information according to their definitions and the merchant can pull linked and transformed product information according to their definitions.

In practice, the chain from manufacturer to the end merchant may have several nodes being distributors/wholesalers that reloads the data by getting product information from an upstream trading partner and passing this product information to a downstream trading partner.

Data Quality Implications

Data quality is as always a concern when information producers and information consumers must collaborate, and in a product data syndication context the extended challenge is that the upstream producer and the downstream consumer does not belong to the same organization. This ecosystem wide data quality and Master Data Management (MDM) issue was examined in the post Watch Out for Interenterprise MDM.

MDM Terms on the Move in the Gartner Hype Cycle

The latest Gartner Hype Cycle for Data and Analytics Governance and Master Data Management includes some of the MDM trends that have been touched here on the blog.

If we look at the post peak side, there are these five terms in motion:

  • Single domain MDM represented by the two most common domains being MDM of Product Data and MDM of Customer Data.
  • Multidomain MDM.
  • Interenterprise MDM, which before was coined Multienterprise MDM by Gartner and as I like to coin Ecosystem Wide MDM.
  • Data Hub Strategy which I like to coin Extended MDM.
  • Cloud MDM.
Source: Gartner

The hype cycle from last year was examined in the post MDM Terms in Use in the Gartner Hype Cycle.

Compared to last year this has happened to MDM:

  • Multidomain MDM has moved on from the Trough of Disillusionment to climbing up the Slope of Enlightenment. I have been waiting for this to happen for 10 years – both in the hype cycle and in the real-world – since I founded the Multi-Domain MDM Group on LinkedIn back then.
  • Interinterprise MDM has swapped place with Cloud MDM, so this term is now ahead of Cloud MDM. It is though hard to imagine Interenterprise MDM without Cloud MDM, and MDM in the cloud will also according Gartner reach the the Plateau of Productivity before ecosystem wide MDM. The promise of this is also in accordance with a poll I made as told in the post Interenterprise MDM Will be Hot.

You can get the full report from the MDM consultancy parsionate here.

Interenterprise MDM Will be Hot

Interenterprise Master Data Management is about how organizations can collaborate by sharing master data with business partners in order to optimize own master data and create new data driven revenue models together with business partners.

It is in my eyes one of the most promising trends in the MDM world. However, it is not going to happen tomorrow. The quest of breaking down internal data and knowledge silos within organizations around is still not completed in most enterprises. Nevertheless, there is a huge business opportunity to pursue for the enterprises who will be in the first wave of interenterprise data sharing through interenterprise MDM.

A poll in the LinkedIn MDM – Master Data Management group revealed that MDM practitioners are aware of that Interenterprise MDM will be hot sooner or later:

For the range of industries that work with tangible products, one of the most obvious places to start with Interenterprise MDM is by excelling – in the meaning of eliminating excel files exchange – in Product Data Syndication (PDS). Learn more in the post The Role of Product Data Syndication in Interenterprise MDM.

MDM Terms in Use in the Gartner Hype Cycle

The latest Gartner Hype Cycle for Data and Analytics Governance and Master Data Management includes some of the MDM trends that have been touched here on the blog.

If we look at the post peak side, there are these five main variant – or family of variant – terms in motion:

  • Single domain MDM represented by the two most common domains being MDM of Product Data and MDM of Customer Data.
  • Multidomain MDM.
  • Cloud MDM.
  • Data Hub Strategy which I like to coin Extended MDM.
  • Interenterprise MDM, which before was coined Multienterprise MDM by Gartner and I like to coin Ecosystem Wide MDM.

It is also worth noticing that Gartner has dropped the term Multivector MDM from the hype cycle. This term never penetrated the market lingo.

Another term that is almost only used by Gartner is Application Data Management (ADM). That term is still in there.

Data Marketplaces, Exchanges and Multienterprise MDM

In the recent Gartner Top 10 Trends in Data and Analytics for 2020 trend number 8 is about data marketplaces and exchanges. As stated by Gartner: “By 2022, 35% of large organizations will be either sellers or buyers of data via formal online data marketplaces, up from 25% in 2020.”

The topic of selling and buying data was touched here on the blog in the post Three Flavors of Data Monetization

A close topic to data marketplaces and exchanges is Multienterprise MDM.

In the 00’s the evolution of Master Data Management (MDM) started with single domain / departmental solutions dominated by Customer Data Integration (CDI) and Product Information Management (PIM) implementations. These solutions were in best cases underpinned by third party data sources as business directories as for example the Dun & Bradstreet (D&B) world base and second party product information sources as for example the GS1 Global Data Syndication Network (GDSN).

In the previous decade multidomain MDM with enterprise wide coverage became the norm. Here the solution typically encompasses customer-, vendor/supplier-, product- and asset master data. Increasingly GDSN is supplemented by other forms of Product Data Syndication (PDS). Third party and second party sources are delivered in the form of Data as a Service that comes with each MDM solution.

Data Marketplaces and Exchange

In this decade we will see the rise of multienterprise MDM where the solutions to some extend become business ecosystem wide, meaning that you will increasingly share master data and possibly the MDM solutions with your business partners – or else you will fade in the wake of the overwhelming data load you will have to handle yourself.

The data sharing will be facilitated by data marketplaces and exchanges.

On July 23rd I will, as a representative of The Disruptive MDM/PIM/DQM List, present in the webinar How to Sustain Digital Ecosystems with Multi-Enterprise MDM. The webinar is brought to you by Winshuttle / Enterworks. It is a part of their everything MDM & PIM virtual conference. Get the details and make your free registration here.

Welcome EnterWorks as a Featured Solution on The Disruptive MDM / PIM / DQM List – and to Europe

One of the rising stars on the Master Data Management (MDM) and Product Information Management (PIM) scene is EnterWorks.

Enterworks Europe LaunchThe EnterWorks solution has during the latest years, as a small crowd of other solutions on the market, grown from being a PIM solution to be a Multi-domain MDM solution. But they have not stopped there. EnterWorks is also a Multi-enterprise MDM solution and is thus covering the needs of sharing master data and product information within business ecosystems. This is, as stated by Gartner, a particularly interesting value proposition in the context of digital ecosystems.

Last year EnterWorks joined forces with WinShuttle, a major player in the data management realm.

This has led to that I, besides welcoming EnterWorks as a featured solution on the list, also is able to welcome EnterWorks to Europe. The European launch should have taken place on The Gartner Data & Analytics Summit in March. However, this event was as all other events at the moment postponed. But the launch is not. Read about the perspectives of this move in the press release on that Winshuttle Announces European Launch of EnterWorks® MDM/PIM Platform.

No One MDM Solution Can Fully Satisfy All Current and Future Use Cases

The title of this post is taken from the Gartner Critical Capabilities for Master Data Management Solutions.

One implication of this observation is that you when selecting your solution will not be able to use a generic analyst ranking of solutions as examined in the post Generic Ranking of Vendors versus an Individual Selection Service.

Selection Model

This is the reason of being for The Disruptive MDM / PIM / DQM List.

Another implication is that even the best fit MDM solution will not necessarily cover all your needs.

One example is within data matching, where I have found that the embedded solutions in MDM tools often only have limited capabilities. To solve this case, there are best of breed data matching solutions on the market able to supplement the MDM solutions.

Another example close to me is within multienterprise (business ecosystem wide) MDM, as MDM solutions are focused on each given organization. Here your interaction with a trading partner, and the interaction by the trading partner with you, can be streamlined with a solution like Product Data Lake.

So, you have the algorithm! But do you have the data?

In the game of winning in business by using Artificial Intelligence (AI) there are two main weapons you can use: Algorithms and data. In a recent blog post Andrew White of Gartner, the analyst firm, says that It’s all about the data – not the algorithm.

AI iconIn the Master Data Management (MDM) space the equipment of solutions with AI capabilities has been going on for some time as reported in the post Artificial Intelligence (AI) and Master Data Management (MDM).

So, next thing is how to provide the data? It is questionable if every single organization has the sufficient (and well managed) master data to make a winning formula. Most organizations must, for many use cases, look beyond the enterprise firewall to get the training data or better the data fuelled algorithms to win the battles and the whole game.

An example of such a scenario is examined in the post Artificial Intelligence (AI) and Multienterprise MDM.

Why Multienterprise MDM will Underpin Digital Transformation

I read (and write) a lot about why Master Data Management (MDM) is a core capability you need to succeed in digital transformation.

Over at the Profisee blog there is a post about that, extending the capability to be multidomain MDM. The post is called The Role of Multi-Domain MDM in Digital Transformation.

Also, at the Reltio blog as part of the #ModernDataMasters series, Tony Saldanha, author of the book  Why Digital Transformations Fail, explains: “Look at master data in terms of the entire virtual company – the total supply chain including your clients and suppliers – and create an ecosystem to drive standards across that.”

Tony continues: “The investment in master data within ecosystems is going to increase dramatically. People are going to realise that most of the waste that happens is at the seams of large organisations – not having a common language between the accounts payable of one company and the accounts receivable of another company means both companies are wasting resources and money.”

Multienterprise MDM Digital Transformation

This way of looking at MDM as something that goes beyond each organization and evolves to be ecosystem wide is also called Multienterprise MDM.

In my eyes this is a very important aspect of using MDM within digital transformation. This theme is further examined in the post Why is Your Digital Ecosystem and MDM the Place to Begin in Digital Transformation?