About Henrik Gabs Liliendahl

Data Quality and Master Data Management professional

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.

Five Product Information Management (PIM) Essential Aspects

A Product Information Management (PIM) solution must encompass some core aspects of handling product data in a digitalized world where products are exchanged online in self-service scenarios. Here are five essential aspects:

Product Identification

PIM id

Usually a product is identified uniquely within each organization using a number or a code that follows the product in all the applications that handles product data. But as products are exchanged between trading partners, an external product identifier is essential in many business processes.

The most common external identifier of a product is a GTIN (Global Trade Identification Number) which has those three most common formats:

  • 12-digit UPC – Universal Product Code, which is popular in North America
  • 13- digit EAN – European/International Article Number, which is popular in Europe
  • 14-digit GTIN, which is meant to replace among others the two above

We know these numbers from the barcodes on goods in physical shops.

It is worth noticing that a GTIN is applied to each packing level for a product model. So, if we for example have a given model of a magic wand, there could be three GTINs applied:

  • One for a single magic wand
  • One for a box of 25 magic wands
  • One for a pallet of 50 boxes of magic wands

Also, the GTIN is applied to a specific variant of a product model. So, if we have a given model of a pair of trousers, there will be a GTIN for each size and colour variant.

This level of product is also referred to as a SKU – Stock Keeping Unit.

Besides the GTIN (UPC/EAN) system there are plenty of industry and national number and code systems in play.

Product Classification

PIM class

There are many reasons for why you need to classify your range of products. Therefore, there are also many ways of doing so. You can either use an external classification system or your homegrown classification tailored to your organizations view of the world.

Here are five examples of an external standard you can use in order to meet the classification standards required by your trading partners:

  • UNSPSC (United Nations Standard Products and Services Code) is managed by GS1 US™ for the UN Development Programme (UNDP). This is an open, global, multi-sector standard for classification of products and services. This standard is often used in public tenders and at some marketplaces.
  • GPC (Global Product Classification) is created by GS1 as a separate standard classification within its network synchronization called the Global Data Synchronization Network (GDSN).
  • Harmonized System (HS) codes are commodity codes lately being worldwide harmonized to represent the key classifier in international trade. They determine customs duties, import and export rules and restrictions as well as documentation requirements. National statistical bureaus may require these codes from businesses doing foreign trade.
  • eCl@ss is a cross-industry product data standard for classification and description of products and services emphasizing on being a ISO/IEC compliant industry standard nationally and internationally. The classification guides the eCl@ss standard for product attributes (in eClass called properties) that are needed for a product with a given classification.
  • ETIM develops and manages a worldwide uniform classification for technical products. This classification guides the ETIM standard for product attributes (in ETIM called features) that are needed for a product with a given classification.

Within each organization you can have one – and often several – homegrown classification schemes that exist besides the external ones relevant in each organization. One example is how you arrange your range of products on a webshop similar to how you would arrange the goods in aisles in a physical shop.

Specific product attributes

PIM attributes

When selling products in self-service scenarios a main challenge is that each classification of products needs a specific set of attributes (sometimes called properties and features) in order to provide the set of information needed to support a buying decision.

So, while some attributes are common for all products there will be a set of attributes needed to be populated to have data completeness for this product while these attributes are irrelevant for another product belonging to another classification.

External standards as eClass and ETIM includes a scheme that names and states the attributes needed for a product belonging to a certain classification and also the value lists that goes with it as for example which terms for colours that are valid and can be exchanged consistently between trading partners.

Related products

PIM relation

A core challenge in self-service selling is that you have to mimic what a salesman does: If you enter a shop to buy an intended product, the salesman will like you to walk away with a better (and more expensive) choice along with some other products you would need to fulfil the intended purpose of use.

A common trick in a webshop is to present what other users also bought or looked at. That is the crowdsourcing approach. But it does not stop there. You must also present precisely what accessories that goes with a given product model. You must be able to present a replacement if the intended product is not available anymore (or temporarily out of stock). You can present up-sell options based on the features in question. You can present x-sell options based on the intended purpose of use.

Digital Assets

PIM asset

When your prospective customer can’t see and feel a product online you must present product images of high quality that shows the product (and not a lot of other things too). It can be product images taken from a range of different angles. You can also provide video clips with the given product.

Besides that, there may be many other types of digital assets related to each product model. This can be installation guides, line drawings, certificates and more.

Tools That Can Help

This site has a list of innovative Master Data Management (MDM) and PIM solutions that can help you mastering the core aspects of products information management. Check out the 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 Data Matching and Deduplication?

The two terms data matching and deduplication are often used synonymously.

In the data quality world deduplication is used to describe a process where two or more data records, that describes the same real-world entity, are merged into one golden record. This can be executed in different ways as told in the post Three Master Data Survivorship Approaches.

Data matching can be seen as an overarching discipline to deduplication. Data matching is used to identify the duplicate candidates in deduplication. Data matching can also be used to identify matching data records between internal and external data sources as examined in the post Third-Party Data Enrichment in MDM and DQM.

As an end-user organization you can implement data matching / deduplication technology from either pure play Data Quality Management (DQM) solution providers or through data management suites and Master Data Management (MDM) solutions as reported in the post DQM Tools In and Around MDM Tools.

When matching internal data records against external sources one often used approach is utilizing the data matching capabilities at the third-party data provider. Such providers as Dun & Bradstreet (D&B), Experian and others offer this service in addition to offering the third-party data.

To close the circle, end-user organizations can use the external data matching result to improve the internal deduplication and more. One example is to apply a matched duns-numbers from D&B for company records as a strong deduplication candidate selection criterium. In addition, such data matching results may often result not in a deduplication, but in building hierarchies of master data.

Data Matching and Deduplication

This site has a list of the most innovative providers of data matching and deduplication tools stretching from best-of-breed solutions for Articficial Intelligence (AI) underpinned data matching and deduplication specialists to Master Data Management (MDM) solutions that include data matching and deduplication capabilities. Check the list here.