The Rise of Interenterprise MDM

The recent Gartner Magic Quadrant for Master Data Management Solutions has this strategic planning assumption:

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

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.

Contextual MDM vs Enterprise-Wide, Global, Multidomain MDM

The term “contextual Master Data Management” has been floating around in a couple of years. We can see contextual MDM as smaller pieces of MDM with a given flavour as for example focussing on sub/overlapping disciplines as:

The focus can also be at:

  • A given locality
  • A given master data domain as customer, supplier, employee, other/all party, product (beyond PIM), location or asset
  • A given business unit

You must eat an elephant one bite at a time. Therefore, contextual MDM makes a good concept for getting achievable wins.   

However, in an organization with high level of data management maturity the range of contextual MDM use cases, and the solutions for them, will be encompassed by a common enterprise-wide, global, multidomain MDM framework – either as one solution or a well-orchestrated set of solutions.

One example with dependencies is when working with personalization as part of Product Experience Management (PXM). Here you need customer personas. The elephant in the room, so to speak, is that you have to get the actual personas from Customer MDM and/or the Customer Data Platform (CDP).

The list of solutions on this site covers both one-stop-shopping options for all contextual MDM use cases and specialised solutions for a given contextual MDM use case. Check the growing list here.

MDM Terms In and Out of The Gartner 2020 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 MDM terms in motion:

  • Single domain MDM represented by the two most common domains being MDM of Product Data and MDM of Customer Data. Doing Customer MDM and Product MDM is according to Gartner still going up the slope of enslightment towards the plateau of productivity.
  • Multidomain MDM solutions as examined here on this blog in the post What is Multidomain MDM?.According to Gartner there are still desillusions to be made for these solutions.
  • Cloud MDM as for example pondered in a guest blog post on this blog. The post is called Cloud multi-domain MDM as the foundation for Digital Transformation. There is still a long downhill journey for cloud MDM in the eyes of the Gartner folks.
  • Data Hub Strategy which my also be coined Extended MDM as a data hub covers more data than master data as reported in the post Master Data, Product Information, Reference Data and Other Data. This trend is trailing cloud MDM on the Gartner Hype Cycle.
  • Interenterprise MDM, which before was coined Multienterprise MDM by Gartner and I like to coin Ecosystem Wide MDM. An example of a kind of solution with this theme will be PDS as explained in the post What is Product Data Syndication (PDS)? This trend has, estimated by Gartner, just passed the peak and have more than 5 years before reaching the plateau of productivity.

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 related to- or opposed to– MDM and that is almost only used by Gartner is Application Data Management (ADM). That term is still in there making the under most radars progress near the final uphill climb.

Learn more about how solution providers cover these terms on The Resource List.

What is a Golden Record within Data Management?

The term golden record is a core concept within Master Data Management (MDM) and Data Quality Management (DQM). A golden record is a representation of a real world entity. This representation may be compiled from multiple different representations of that entity in a single or in multiple different databases within the enterprise system landscape.

A golden record is optimized towards meeting data quality dimensions as:

  • Being a unique representation of the real world entity described
  • Having a complete description of that entity covering all purposes of use in the enterprise
  • Holding the most current and accurate data values for the entity described

In Multidomain MDM we work with a range of different entity types as party (with customer, supplier, employee and other roles), location, product and asset. The golden record concept applies to all of these entity types, but in slightly different ways.

Party Golden Record

Having a golden record that facilitates a single view of a customer is probably the most known example of using the golden record concept. Managing customer records and dealing with duplicates of those is the most frequent data quality issue around.

If you are not able to prevent duplicate records from entering your MDM world, which is the best approach, then you have to apply data matching capabilities. When identifying a duplicate you must be able to intelligently merge any conflicting views into a golden record as examined in the post Three Master Data Survivorship Approaches.

In lesser degree we see the same challenges in getting a single view of suppliers and you ultimately will want to have a single view on any business partner, also where the same real world entity have both customer, supplier and other roles to your organization.

There are party identification systems available. Most countries have national ID systems for both citizens (however in most countries mostly restricted to public administration) and organizations. There is Legal Entity Identifier (LEI) concept slowly penetrating in financial services. Also, there are commercial organization identifiers as the Duns Number available.

Location Golden Record

Having the same location only represented once in a golden record and applying any party, product and asset record, and ultimately golden record, to that record may be seen as quite academic. Nevertheless, striving for that concept will solve many data quality conundrums.

Location management have different meanings and importance for different industries. One example is that a brewery makes business with the legal entity (party) that owns a bar, café, restaurant. However, even though the owner of that place changes, which happens a lot, the brewery is still interested in being the brand served at that place. Also, the brewery wants to keep records of logistics around that place and the historic volumes delivered to that place. Utility and insurance are other examples of industries where the location golden record (should) matter a lot.

Knowing the properties of a location also supports the party deduplication process. For example, if you have two records with the name “John Smith” on the same address, the probability of that John Smith being the same real world entity is dependent on whether that location is a single-family house or a nursing home.

Location identification concepts revolves around postal adresses, which are fluffy and varies in format by country, and geocoding systems as latitude/longitude, UTM coordinates, WGS coordinates and more.

Golden Records

Product Golden Record

Product Information Management (PIM) solutions became popular with the raise of multi-channel where having the same representation of a product in offline and online channels is essential. The self-service approach in online sales also drew the requirements of managing a lot more product attributes than seen before, which again points to a solution of handling the product entity centralized.

In large organizations that have many business units around the world you struggle with having a local view and a global view of products. A given product may be a finished product to one unit but a raw material to another unit. Even a global SAP rollout will usually not clarify this – rather the contrary.

While third party reference data helps a lot with handling golden records for party and location, this is lesser the case for product master data. Classification systems and data pools do exist, but will certainly not take you all the way. With product master data you must rely more on second party master data meaning sharing product master data within the business ecosystems where you operate.

The none-profit organization GS1 has done a lot in implementing the Global Trade Item Number (GTIN) based on the Universal Product Code (UPC) and the European Article Number (EAN) concept. However there are still some challenges in this concept around packaging levels and more.

Asset (or Thing) Golden Record

In asset master data management you also have different purposes where having a single view of a real world asset helps a lot. There are namely financial purposes and logistic purposes that have to aligned, but also a lot of others purposes depending on the industry and the type of asset.

With the raise of the Internet of Things (IoT) we will have to manage a lot more assets (or things) than we usually have considered. When a thing (a machine, a vehicle, an appliance) becomes intelligent and now produces big data, master data management and indeed multi-domain master data management becomes imperative.

You will want to know a lot about the product model of the thing in order to make sense of the produced big data. For that, you need the product (model) golden record. You will want to have deep knowledge of the location in time of the thing. You cannot do that without the location golden records. You will want to know the different party roles in time related to the thing. The owner, the operator, the maintainer. If you want to avoid chaos, you need party golden records.

Tools That Can Help

This site has a list of innovative MDM and DQM solution that can help you mastering golden records. Check out the list here.

What is Multi-Domain MDM?

Multi-domain 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.

Customer MDM is typically about federating the accounts receivable in the ERP system(s) and the direct and prospective accounts in the CRM system(s). Golden records are formed through deduplication of multiple representations of the same real-world entity.

Supplier (or vendor) MDM is typically about federating the accounts payable in the ERP system(s) and the existing and prospective accounts in the SRM system(s). A main focus is on the golden records and the company family tree they are in.

Product MDM has a buy-side and a sell-side.

On the buy-side MDM is taking care of trading data for products to resell, in manufacturing environments also the trading data for raw materials and in some cases also for parts to be used in Maintenance, Repair and Operation (MRO). The additional long tail of product specifications may in resell scenarios be onboarded in an embedded/supplementary Product Information Management (PIM) solution.

On the sell-side the trading data are handled for resell products and in manufacturing environments the finished products. The additional long tail of product specifications may be handled in an embedded/supplementary Product Information Management (PIM) solution.

What is multidomain MDM

Multidomain MDM does this in a single solution / suite of solutions. And much more as for example:

  • Supplier contacts can be handled in a generic party master data structure.
  • Customer contacts can be handled in a generic party master data structure
  • Besides the direct accounts in CRM the indirect accounts and contacts can in the party master data structure too. Examples of such parties are:
    • Influencers in the form of heath care professionals in life science.
    • Influencers in the form of architects and other construction professionals in building material manufacturing.
    • End consumers in many supply chain B2B2C scenarios.
  • Employee records can be handled in a generic party master data structure. The roles of sales representatives and their relation to customers, influencers, product hierarchies and location hierarchies can be handled as well as purchase responsibles and their relation to suppliers, influencers, product hierarchies and location hierarchies can be handled.
  • The relation between suppliers and product hierarchies and location hierarchies cand be handled.
  • The relation between customers and end consumers and the product hierarchies and location hierarchies can be handled.
  • Inbound product information feeds from suppliers can be organized and optimized through Product Data Syndication (PDS) solutions.
  • The relation between customer preferences and product information can be handled in Product eXperience Management (PXM) solutions.
  • Outbound product information feeds to resellers can be organized and optimized through Product Data Syndication (PDS) solutions.

This site has a list of the most innovative solutions that can either be your multi-domain solution or supplement other solutions as a best-of-breed component. Check the list here.