How MDM Inflates

Since the emerge of Master Data Management (MDM) back in 00’s this discipline has taken on more and more parts of the also evolving data management space.

The past

It started with Customer Data Integration (CDI) being addressing the common problem among many enterprises of having multiple data stores for customer master data leading to providing an inconsistent face to the customer and lack of oversight of customer interactions and insights.

In parallel a similar topic for product master data was addressed by Product Information Management (PIM). Along with the pains of having multiple data stores for product data the rise of ecommerce lead to a demand for handling much more detailed product data in structural way than before.

While PIM still exist as an adjacent discipline to MDM, CDI mutated into customer MDM covering more aspects than the pure integration and consolidation of customer master data as for example data enrichment, data stewardship and workflows. PIM has thrived either within, besides – or without – product MDM while supplier MDM also emerged as the third main master data domain.   

The present

Today many organizations – and the solution providers – either grow their MDM capabilities into a multidomain MDM concept or start the MDM journey with a multidomain MDM approach. Multidomain Master Data Management is usually perceived as the union of Customer MDM, Supplier MDM and Product MDM. It is. And it is much more than that as explained in the post What is Multidomain MDM?

As part of a cross-domain thinking some organizations – and solution providers – are already preparing for the inevitable business partner domain as pondered in the post The Intersection of Supplier MDM and Customer MDM.

The PIM discipline has got a subdiscipline 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.

The future

Interenterprise MDM will be the inflated next stage of the business partner MDM and Product Data Syndication (PDS) theme. This 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.

Extended MDM is the inflated next scope of taking other data domains than customer, supplier, and product under the MDM umbrella.

Reference Data Management (RDM) is increasingly covered by or adjacent to MDM solutions.

Also, we will see handling of locations, assets, business essential objects and other digital twins being much more intensive within the MDM discipline. Which entities that will be is industry specific. Examples from retail are warehouses, stores, and the equipment within those. Examples from pharma are own and affiliated plants, hospitals and other served medical facilities. Examples from manufacturing are plants, warehouses as well as the products, equipment and facilities where their produced products are used within.

The handling of all these kind of master data on the radar of a given organization will require interenterprise MDM collaboration with the involved business partners.

Organizations who succeed in extending the coverage of MDM approaches will be on the forefront in digital transformation.

Augmented MDM is the inflated next level of capabilities utilized in MDM as touched in the post The Gartner MDM MQ of December 2021 and Augmented MDM. It is a compilation of utilizing several trending technologies as Machine Learning (ML), Artificial Intelligence (AI), graph approaches as knowledge graph with the aim of automating MDM related processes.

Metadata management will play a wider and more essential role here not at least when augmented MDM and extended MDM is combined.    

Mastering this will play a crucial role in the future ability to launch competitive new digital services.

What is Collaborative Product Data Syndication?

Product Data Syndication (PDS) is a sub discipline within Product Information Management (PIM) as explained in the post What is Product Data Syndication (PDS)?

Collaborative PDS can be achieved at scale with 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.

With Collaborative Product Data Syndication, you can get the best of two worlds:

  • You can have the market standard that makes you not falling behind your competitors.
  • However, you can also have unique content coming through that puts you ahead of your competitors.

The advantages of collaborative PDS versus other PDS approaches was examined in the post Collaborative Product Data Syndication vs Data Pools and Marketplaces.

The Product Data Lake solution I am involved with utilizes that data lake concept to handle the complexities of having many different data standards for product information in play within supply chains and encompass the many different preferences for exchange methods.

Our approach is not to reinvent the wheel, but to collaborate with partners in the industry. This includes:
·       Experts within a type of product as building materials and sub-sectors in this industry, machinery, chemicals, automotive, furniture and home-ware, electronics, work clothes, fashion, books and other printed materials, food and beverage, pharmaceuticals and medical devices. You may be a specialist in certain standards for product data. You will link the taxonomy in use at two trading partners or within a larger business ecosystem.
·       Product data cleansing specialists who have proven track records in optimizing product master data and product information. You will prepare the product data portfolio at a trading partner and extend the service to other trading partners or within a larger business ecosystem.
·       System integrators who can integrate product data syndication flows into Product Information Management (PIM) and other solutions at trading partners and consult on the surrounding data quality and data governance issues. You will enable the digital flow of product information between two trading partners or within a larger business ecosystem.
·       Tool vendors who can offer in-house Product Information Management (PIM) / Master Data Management (MDM) solutions or similar solutions in the ERP and Supply Chain Management (SCM) sphere. You will be able to provide, supplement or replace customer data portals at manufacturers and supplier data portals at merchants and thus offer truly automated and interactive product data syndication functionality.
·       Technology providers with data governance solutions, data quality management solutions and Artificial Intelligence (AI) / machine learning capacities for classifying and linking product information to support the activities made by other delegates and subscribers.
·       Reservoirs, as Product Data Lake is a unique opportunity for service providers with product data portfolios (data pools and data portals) for utilizing modern data management technology and offer a comprehensive way of collecting and distributing product data within the business processes used by subscribers.

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.

Privacy and Confidentiality Concerns in Interenterprise Data Sharing

Exchange of data between enterprises – aka interenterprise data sharing – is becoming a hot topic in the era of digital transformation. As told in the post Data Quality and Interenterprise Data Sharing this approach is the cost-effective way to ensure data quality for the fast-increasing amount of data every organization has to manage when introducing new digital services.

McKinsey Digital recently elaborated on this theme in an article with the title Harnessing the power of external data. As stated in the article: “Organizations that stay abreast of the expanding external-data ecosystem and successfully integrate a broad spectrum of external data into their operations can outperform other companies by unlocking improvements in growth, productivity, and risk management.”

The arguments against interenterprise data sharing I hear most often revolves around privacy and confidentiality concerns.

Let us have a look at this challenge within the two most common master data domains: Party data and product data.

Party Data

The firm CDQ talk about the case for sharing party data in the post Data Sharing: A Brief History of a Crazy Idea. As said in here: The pain can be bigger than the concern.

Privacy through the enforced data privacy and data protection regulations as GDPR must (and should) be adhered to and sets a very strict limit for exchanging Personal Identifiable Information only leaving room for the legitimate cases of data portability.

However, information about organizations can be shared not only as exploitation of public third-party sources as business directories but also as data pools between like-minded organizations. Here you must think about if your typos in company names, addresses and more really are that confidential.

Product Data

The case for exchanging product data is explained in the post The Role of Product Data Syndication in Interenterprise MDM.

Though the vast amount of product data is meant to become public the concerns about confidentiality also exist with product data. Trading prices is an obvious area. The timing of releasing product data is another concern.

In the Product Data Lake syndication service I work with there are measures to ensure the right level of confidentiality. This includes encryption and controlling with whom you share what and when you do it.

Data governance plays a crucial role in orchestrating interenterprise data sharing with the right approach to data privacy and confidentiality. How this is done in for example product data syndication is explained in the page about Product Data Lake Documentation and Data Governance.

Four Ways You as a Merchant Can Exploit Product Data Syndication

Product Data Syndication has become an essential capability to manage within digital transformation at merchants as wholesalers and retailers. There are 4 main scenarios.

1: Inbound product data syndication of resell (direct) products

The process involves getting the most complete set of product information available from the supplier in order to fit the optimal set of product information needed to support the often self-service based buying decision by your customers.

This can be done by direct feeds from suppliers or through feeds via the various data pools that exist in different industries and geographies.

2: Inbound product data syndication for indirect products

You also need product data for parts used in Maintenance, Repair and Operation within facility management around logistic facilities, offices, and other constructions where products for MRO are needed. With the rise of the Internet of Things (IoT) these products are becoming more and more intelligent and are operated in an automatic way. For that, product information is needed in an until now unseen degree.

Every organization needs products and services as furniture, office supplies, travel services and much more. The need for onboarding product data for these purchases is still minimal compared to the above-mentioned scenarios. However, a foreseeable increased use of Artificial Intelligence (AI) in procurement operations will ignite the requirement for product data onboarding for this scenario too in the coming years.

3: Outbound product data syndication to marketplaces

Selling products on marketplaces has become a popular alternative to selling via ones own ecommerce site. While price and delivery options are main drivers here there are still more business to win via this channel if you can provide better and more unique product information than other resellers of the same product.

4: Outbound product data syndication to customers using products as parts

Your business-to-business (B2B) customers may also need product data for parts used directly in production or in Maintenance, Repair and Operation in production facilities and within facility management around logistic facilities, offices, and other constructions where products for MRO are needed. With the rise of the Internet of Things (IoT) these products are becoming more and more intelligent and are operated in an automatic way. For that, product information is needed in an until now unseen degree.

The Need for Collaborative Product Data Syndication

The sharp rise of the need product data syndication calls for increased collaboration through data partnerships in business ecosystems.

In the Product Data Lake venture I am working on now, we have made a framework – and a piece of Software as a Service – that is able to leverage the concepts of inbound and outbound Product Data Syndication and enable the four mentioned ways of utilizing product data syndication to create better business outcomes for you as a merchant.

Product Data Lake acts as a single point of digital contact for suppliers and customers in the product data supply chain which also provide you as a merchant with single place in the cloud from where your Product Information Management (PIM), ERP and eCommerce applications get and put external product data feeds.

This concept enables automated self-service by suppliers and customers who also can subscribe to Product Data Lake. In The Product Data Lake platform you can control the product portfolio and the product attribute set you are sharing with each business partner.

Learn more about Product Data Lake here.

Product Data Supply Chain Management in Resell

Five Ways You as a Manufacturer Can Exploit Product Data Syndication

Product Data Syndication has become an essential capability to manage within digital transformation at manufacturers. There are 5 main scenarios.

1: Outbound product data syndication for finished products

As a manufacturer you need to ensure that self-service buying decisions by the end customer through the channel partner point-of-sale will result in choosing your product instead of a product provided by your competitor.

This is achieved through providing complete product information in a way that is easy onboarded by each of your channel partners – as well as direct customers and marketplaces where this apply.

2: Inbound product data syndication for 3rd party finished products

As a manufacturer you often have a range of products that are not produced inhouse but are essential supplements when selling own produced products.

The process involves getting the most complete set of product information available from the supplier in order to fit the optimal set of product information needed to support the buying decision by the end customer where your own produced products and 3rd party products makes a whole.

3: Inbound product data syndication for raw materials and packaging

Here the objective is to get product information needed to do quality assurance and in organic production apply the right blend in order to produce a consistent finished product.

Also, the increasing demand for measures of sustainability is driving the urge for information on the provenance of the finished product and the packaging including the origin of the ingredients and circumstances of the production of these components. 

4: Inbound product data syndication for parts used in MRO

Product data for parts used in Maintenance, Repair and Operation is an essential scenario related in running the production facilities as well as in facility management around logistic facilities, offices, and other constructions where products for MRO are needed.

With the rise of the Internet of Things (IoT) these products are becoming more and more intelligent and are operated in an automatic way. For that, product information is needed in an until now unseen degree.

5: Inbound product data syndication for other indirect products

Every organization needs products and services as furniture, office supplies, travel services and much more. The need for onboarding product data for these purchases is still minimal compared to the above-mentioned scenarios. However, a foreseeable increased use of Artificial Intelligence (AI) in procurement operations will ignite the requirement for product data onboarding for this scenario too in the coming years.

The Need for Collaborative Product Data Syndication

The sharp rise of the need product data syndication calls for increased collaboration through data partnerships in business ecosystems.

In the Product Data Lake venture I am working on now, we have made a framework – and a piece of Software as a Service – that is able to leverage the concepts of outbound and inbound Product Data Syndication and enable the five mentioned ways of utilizing product data syndication to create better business outcomes for you as a manufacturer.

Product Data Lake acts as a single point of digital contact for suppliers and channel partners in the product data supply chain which also provide you as a manufacturer with single place in the cloud from where your Product Lifecycle Management (PLM), ERP and Product Information Management (PIM) applications get and put external product data feeds.

This concept enables automated self-service by suppliers and channels partners who also can subscribe to Product Data Lake. In The Product Data Lake platform you can control the product portfolio and the product attribute set you are sharing with each business partner.

Learn more about Product Data Lake here.

Product Data Supply Chain Management in Manufacturing

The Role of Product Data Syndication in Interenterprise MDM

Interenterprise Master Data Management is on the rise as reported in the post Watch Out for Interenterprise MDM. Interenterprise MDM 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.

One of the most obvious places to start with Interenterprise MDM is Product Data Syndication (PDS). While PDS until now has been mostly applied when syndicating product data to marketplaces there is a huge potential in streamlining the flow of product from manufacturers to merchants and end users of product information.

Inbound and Outbound Product Data Syndication

There are two scenarios in interenterprise Product Data Syndication:

  • Outbound, where your organization as being part of a supply chain will provide product information to your range of customers. The challenge is that with no PDS functionality in between you must cater for many (hundreds or thousands) different structures, formats, taxonomies and exchange methods requested by your customers.
  • Inbound, where your organization as being part of a supply chain will receive product information from your range of suppliers. The challenge is that with no PDS functionality in between you must cater for many (hundreds or thousands) different structures, formats, taxonomies and exchange methods coming in.

Learn more in the post Inbound and Outbound Product Data Syndication.

4 Main Use Cases for Collaborative PDS

There are these four main use cases for exchanging product data in supply chains:

  • Exchanging product data for resell products where manufacturers and brands are forwarding product information to the end point-of-sale at a merchant. With the rise of online sales both in business-to-consumer (B2C) and business-to-business (B2B) the buying decisions are self-service based, which means a dramatic increase in the demand for product data throughput.
  • Exchanging product data for raw materials and packaging. Here there is a rising demand for automating the quality assurance process, blending processes in organic production and controlling the sustainability related data by data lineage capabilities.  
  • Exchanging product data for parts used in MRO (Maintenance, Repair and Operation). As these parts are becoming components of the Industry 4.0 / Industrial Internet of Things (IIoT) wave, there will be a drastic demand for providing rich product information when delivering these parts.
  • Exchanging product data for indirect products, where upcoming use of Artificial Intelligence (AI) in all procurement activities also will lead to requirements for availability of product information in this use case.  

Learn more in the post 4 Supplier Product Data Onboarding Scenarios.

Collaborative PDS at Work

In the Product Data Lake venture I am working on now, we have made a framework – and a piece of Software as a Service – that is able to leverage the concepts of inbound and outbound PDS and enable the four mentioned use cases for product data exchange.

The framework is based on reusing popular product data classifications (as GPC, UNSPSC, ETIM, eClass, ISO) and attribute requirement standards (as ETIM and eClass). Also, trading partners can use their preferred data exchange method (FTP file drop – as for example BMEcat, API or plain import/export) on each side.

All in all, the big win is that each upstream provider (typically a manufacturer / brand) can upload one uniform product catalogue to the Product Data Lake and each downstream receiver (a merchant or user organization) can download a uniform product catalogues covering all suppliers.   

Watch Out for Interenterprise MDM

In the recent Gartner Magic Quadrant for Master Data Management Solutions there is a bold statement:

By 2023, organizations with shared ontology, semantics, governance and stewardship processes to enable interenterprise data sharing will outperform those that don’t.

The interenterprise data sharing theme was covered a couple of years ago here on the blog in the post What is Interenterprise Data Sharing?

Interenterprise data sharing must be leveraged through interenterprise MDM, where master data are shared between many companies as for example in supply chains. The evolution of interenterprise MDM and the current state of the discipline was touched in the post MDM Terms In and Out of The Gartner 2020 Hype Cycle.

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.

In this decade we will see the rise of interenterprise 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.

So, watch out for not applying interenterprise MDM.

PS: That goes for MDM end user organizations and MDM platform vendors as well.

4 Supplier Product Data Onboarding Scenarios

When working with Product Information Management (PIM) and Product Master Data Management (Product MDM) one of the most important and challenging areas is how you effectively onboard product master data / product information for products that you do not produce inhouse.

There are 4 main scenarios for that:

  • Onboarding product data for resell products
  • Onboarding product data for raw materials and packaging
  • Onboarding product data for parts used in MRO (Maintenance, Repair and Operation)
  • Onboarding product data for indirect products

Onboarding product data for resell products

This scenario is the main scenario for distributors/wholesalers, retailers and other merchants. However, most manufactures also have a range of products that are not produced inhouse but are essential supplements when selling own produced products.

The process involves getting the most complete set of product information available from the supplier in order to fit the optimal set of product information needed to support a buying decision by the end customer. With the increase of online sales, the buying decision today is often self-serviced. This has dramatically increased the demand for product information throughput.

Onboarding product data for raw materials and packaging

This scenario exists at manufacturers of products. Here the objective is to get product information needed to do quality assurance and in organic production apply the right blend in order to produce a consistent finished product.

Also, the increasing demand for measures of sustainability is driving the urge for information on the provenance of the finished product and the packaging including the origin of the ingredients and circumstances of the production of these components.  

Onboarding product data for parts used in MRO

Product data for parts used in Maintenance, Repair and Operation is a main scenario at manufacturers related to running the production facilities. However, most organizations have facility management around logistic facilities, offices, and other constructions where products for MRO are needed.

With the rise of the Internet of Things (IoT) these products are becoming more and more intelligent and are operated in an automatic way. For that, product information is needed in an until now unseen degree.

Onboarding product data for indirect products

Every organization needs products and services as furniture, office supplies, travel services and much more. The need for onboarding product data for these purchases is still minimal compared to the above-mentioned scenarios. However, a foreseeable increased use of Artificial Intelligence (AI) in procurement operations will ignite the requirement for product data onboarding for this scenario too in the coming years.

The Need for Collaborative Product Data Syndication

The sharp rise of the need product data onboarding calls for increased collaboration between suppliers and Business-to-Business (B2B) customers. It is here worth noticing, that many organizations have both roles in one or the other scenario. The discipline that is most effectively applied to solve the challenges is Product Data Syndication. This is further explained in the post Inbound and Outbound Product Data Syndication.