Multienterprise MDM

From Platforms to Ecosystems

Henrik Gabs Liliendahl2 Comments

Earlier this year Gartner published a report with the title Top Trends in Data and Analytics, 2023. The report is currently available on the Parsionate site here.

The report names three opportunities within this theme:

  • Think Like a Business,
  • From Platforms to Ecosystems and
  • Don’t Forget the Humans

While thinking like a business and don’t forget the humans are universal opportunities that have always been here and will always be, the move from platforms to ecosystems is a current opportunity worth a closer look.

Here data sharing, according to Gartner, is essential. Some recommended actions are to

  • Consider adopting data fabric design to enable a single architecture for data sharing across heterogeneous internal and external data sources.
  • Brand data reusability and resharing as a positive for business value, including ESG (Environmental, Social and Governance) efforts.

Data Fabric is the Gartner buzzword that resembles the competing non-Gartner buzzword Data Mesh. According to Gartner, organizations use data fabrics to capture data assets, infer new relationships in datasets and automate actions on data.

Data sharing can be internal and external.

In my mind there are two pillars in internal data sharing:

  • MDM (Master Data Management) with the aim of sharing harmonized core data assets as for example business partner records and product records across multiple lines of business, geographies, and organizational disciplines.
  • Knowledge graph approaches where MDM is supplemented by modern capabilities in detecting many more relationships (and entities) than before as explained in the post MDM and Knowledge Graph.

In external data sharing we see solutions for:

External data sharing is on the rise, however at a much slower pace than I had anticipated. The main obstacle seems to be that the internal data sharing is still not mature in many organizations and that external data sharing require interaction between at least two data mature organizations.

4 Concepts in the Gartner Hype Cycle for Digital Business Capabilities that will Shape MDM

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Some months ago, Gartner published the latest Hype Cycle for Digital Business Capabilities.

The hype cycle includes 4 concepts that in my mind will shape the future of Master Data Management (MDM) and data management in all. These are:

  • Industrie 4.0
  • Business Ecosystems
  • Digital Twin
  • Machine Customer

Industrie 4.0

You will find Industrie 4.0 near the trough of disillusionment almost ready to climb the slope of enlighten. Several of the recent MDM blue prints I have worked with have Industrie 4.0 as an overarching theme.

Industrie 4.0 is about using intelligent devices in manufacturing and thus closely connected to the term Industrial Internet of Things (IIoT). The impact of industry 4.0 is across the whole supply chain encompassing not only product manufacturing companies but also for example product merchants and product service providers.

With intelligent devices in the supply chain product MDM will evolve from handling data about product models to handle data about each instance of a product.

Business Ecosystems

The concept of business ecosystems has just passed the peak of inflated expectations.

In a modern business environment, no organization can do everything – or even most things – themselves. Therefore, any enterprise needs to partner with other organizations when working on new digital powered business models.

This also calls for increasing sharing of data, including master data, with business partners. This leads to the rise of interenterprise MDM, which by the way is at about the same position in the Hype Cycle for Data Analytics and MDM.

An example of interenterprise data sharing is Product Data Syndication.

Digital Twin

On the climbing side of the peak of inflated expectations we find the concept of a digital twin.

A digital twin is a virtual representation of a real-world entity such as an asset, person, organization, or process. This fits somehow with what MDM is doing which traditionally has been providing virtual descriptions of customers, suppliers, and products.

With the digital twin flavour, you can sharpen and extend MDM in two ways:

  • Have a more real-world on customers and suppliers by looking at those has roles of business partners along with handling many other external and internal organizational entities
  • Putting more asset types than direct products under the MDM umbrella with improved data governance as a result

Machine Customers

A bit further down the climbing side of the peak you will see the concept of the machine customer.

The expectation is that more and more buying tasks will be automated so there will be no human interaction in the bulk of purchasing processes.

This will only be possible if the products involved at those who sell them are digitally described in sufficient details and categorized the same way on the selling and buying side.

This seems like a job for Master Data Management and the adjacent Product Information Management (PIM) discipline where the buying side needs the right capabilities not only for direct trading products but also indirect supplies.

Also, the concept of augmented MDM will play a role here by applying Artificial Intelligence (AI) to the MDM and PIM side of enabling the machine customer.

The Full Report

You can download the full hype cycle report including the complete visual cycle from the parsionate website: Gartner Hype Cycle for Digital Business Capabilities.

How MDM Inflates

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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 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.

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

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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

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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.

2021 Data Management Mind Map

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Disciplines come and go in the data management world. Here is a mind map of the disciplines on top of my mind today. Some of the disciplines goes back to the emerge of IT in the previous millennium and some have risen during the latest years.

MDM Terms in Use in the Gartner Hype Cycle

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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

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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

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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.