Tag Archives: Syndication

B2B2C in MDM, PIM and DQM

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The Business-to-Business-to-Consumer (B2B2C) scenario is becoming of increasing importance in Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM).

This scenario is usually seen in manufacturing including pharmaceuticals as examined in the post Six MDMographic Stereotypes.

One challenge here is how to extend the capabilities in MDM / PIM / DQM solutions that are build for Business-to-Business (B2B) and Business-to-Consumer (B2C) use cases. Doing B2B2C requires a Multidomain MDM approach with solid PIM and DQM elements either as one solution, a suite of solutions or as a wisely assembled set of best-of-breed solutions.

B2B2C MDM PIM DQM

In the MDM sphere a key challenge with B2B2C is that you probably must encompass more surrounding applications and ensure a 360-degree view of party, location and product entities as they have varying roles with varying purposes at varying times tracked by these applications. You will also need to cover a broader range of data types that goes beyond what is traditionally seen as master data.

In DQM you need data matching capabilities that can identify and compare both real-world persons, organizations and the grey zone of persons in professional roles. You need DQM of a deep hierarchy of location data and you need to profile product data completeness for both professional use cases and consumer use cases.

In PIM the content must be suitable for both the professional audience and the end consumers. The issues in achieving this stretch over having a flexible in-house PIM solution and a comprehensive outbound Product Data Syndication (PDS) setup.

As the middle B in B2B2C supply chains you must have a strategic partnership with your suppliers/vendors with a comprehensive inbound Product Data Syndication (PDS) setup and increasingly also a framework for sharing customer master data taking into account the privacy and confidentiality aspects of this.

This emerging MDM / PIM / DQM scope is also referred to as Multienterprise MDM.

What’s in a Product Name?

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Within Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) one of the critical data elements that needs to be governed is the product name (product description).

Usually the product name is held in two forms:

  • A short form as it appears internally in an organization and is held in an ERP application. Everyone working with SAP knows about the challenge of keeping the product description in SAP within 40 characters. The short form is typically governed within the MDM discipline and is kept in the single one official language in the organization.
  • A long and customer friendly form as it is used when presenting the product in sales channels as for example on a web shop. The long form is typically governed within the PIM discipline and is kept in all languages in use in the organization.

The data quality dimensions that applies to the product name are namely:

  • Uniqueness, meaning that:
    • The product name must be the same (in short or long language form) across data stores in order to avoid duplicates.
    • Each product most have a distinct name, so you are able to distinguish one from the other.
  • Consistency, meaning that you must build the product name by sub elements that are sequenced the same way and have a common vocabulary.

The most common sub elements in a product name are:

  • Brand name or product line
  • Model number
  • Kind of product
  • Size
  • Color
  • Product classification (group) specific characteristics
  • Country

What is in a product name

It is a challenge to implement data governance around product names within a single organization. Even more it is a challenge to rely on / be depended on your trading partners when governing product names in business ecosystems as told in the post Toilet Seats and Data Quality.

Master Data, Product Information, Reference Data and Other Data

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There is a trend on the data management market that the solutions are either going very niche (best-of-breed) in the data domain covered or they are encompassing a broader range of data types.

This can be seen in the spectrum of master data and product information as reported in the post MDM, PIM or Both.

We also see that governance and management of reference data is included in addition to managing master data as told in the post What is Reference Data Management (RDM)?

Some MDM (and RDM) solutions also extend the reach to cover aspects of transaction data and big data. The main scenarios covered are:

  • Matching of party entities in traditional systems of record with the parties referenced in social streams and weblogs (systems of engagement) as well as in sensor data. This can be used in creating a Customer Data Platform (CDP).
  • Extending data quality and data performance dashboards related to master data to cover aggregated transaction data and big data held in data warehouses and data lakes by using a shared set of reference data.

When product information is to be shared in business ecosystems through Product Data Syndication (PDS), this can be accelerated by using a data lake concept and new data stores as staging areas. This is due to that a main challenge here is that the data quality standards on the providing side most often are different from the data quality standards on the receiving side.

MDM PIM RDM and other data

The diagram is a variation of a diagram included in the whitepaper Intelligent Data Hub – Taking MDM to the Next Level. The original is developed together with Salah Kamel, CEO at Semarchy. 

What is Product Data Syndication (PDS)?

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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.

Product Data Syndication World

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 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 Multienterprise MDM.

Extended MDM Platforms

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There is a tendency on the Master Data Management (MDM) market that solutions providers aim to deliver an extended MDM platform to underpin customer experience efforts. Such a platform will not only handle traditional master data, but also reference data, big data (as data lakes) either directly or by linking to the data in there as well as linking to transactions.

The recent acquisition of AllSight by Informatica is an example hereof.

In this context traditional MDM will, supplemented with Reference Data Management (RDM), enable the handling of:

  • Customer, supplier and product identity
  • Customer, supplier and product hierarchies
  • Customer, supplier and product locations

Additionally, the data lake concept can be used for:

Extended MDM Platforms

What is your view: Should MDM solution providers stick to traditional master data or should they strive to encompass other kinds of data too?