Category Archives: Trends

Environmental Data Management

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As examined in the post The Intersection Between MDM, PIM and ESG, environmental data management is becoming an important aspect of the offerings provided in solutions for Master Data Management (MDM) and Product Information Management (PIM). Consequentially, this will also apply to the Data Quality Management (DQM) capabilities that are either offered as part of these solutions or as standalone solutions for data quality.

This site has an interactive Select Your Solution service for potential buyers of MDM / PIM / DQM solutions. The service has a questionnaire and an undelaying model for creating a longlist, shortlist or direct PoC suggestion for the best candidate(s) according to the context, scope, and requirements of an intended solution.

In line with the rise of the Environmental, Social and Governance (ESG) theme, the questionnaire and the underlying selection model must in the first place be enhanced with environmental data management aspects, as the environmental part of ESG is the one with the currently most frequently and comprehensive experienced data management challenges.

There is already established a good basis for this.

However, if you as either one from a solution end user organization with environmental data management challenges or a solution provider have input to environmental data management aspects to be covered, you are more than welcome to make a comment here on the blog or use the below contact form:

Digital Twins in the MDM Product Domain / PIM

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When working with the product domain in Master Data Management (MDM) and with Product Information Management (PIM) we have traditionally been working with the product model meaning that we manage data about how a product that can be produced many times in exactly the same way and resulting in having exactly the same features. In other words, we are creating a digital twin of the product model.

As told in the post Spectre vs James Bond and the Unique Product Identifier the next level in product data management is working with each product instance meaning each produced thing that have a set of data attached that is unique to that thing. Such data can be:

  • Serial number or other identification as for example the Unique Device Identification (UDI) known in healthcare
  • Manufacturing date and time
  • Specific configuration
  • Current and historical position
  • Current and historical owner
  • Current and historical installer, maintainer and other caretaker
  • Produced sensor data if it is a smart device / machine

There is a substantial business potential in being better than your competitor in managing product instances. This boils down to that data is power – if you use the data.

When managing this data, we are building a digital twin of the product instance.

Maintaining that digital twin is a collaborative effort involving the manufacturer, the logistic service provider, the owner, the caretaker, and other roles. For that you need some degree of Interenterprise MDM.

Unified Customer MDM and Supplier MDM

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When blueprinting a Master Data Management (MDM) solution one aspect is if – or in what degree – you should unify customer MDM and supplier MDM.

In theory, you should unify the concept for these two master domains in some degree. The reasons are:

  • There is always an overlap of the real-world entities that has both a customer and a supplier role to your organization. The overlap is often bigger than you think not at least if you include the overlap of company family trees that have members in one of these roles.
  • The basic master data for these master data domains are the same: Identification numbers, names, addresses, means of communication and more.
  • The third-party enrichment opportunities are the same. The most predominant possibilities are integration with business directories (as Dun & Bradstreet and national registries) and address validation (as Loqate and national postal services).

In practice, the problem is that the business case for customer MDM and supplier MDM may not be realized at the same time. So, one domain will typically be implemented before the other depending on your organization’s business model.

Solution Considerations

Most MDM solutions must coexist with an – or several – ERP solutions. Many popular enterprise grade ERP solutions have adapted the business partner view with a common data model for basic customer and supplier data. This is the case with SAP S/4HANA and for example the address book in Microsoft Dynamics AX and Oracle JD Edwards.

MDM solutions themselves does also provide for a common structure. If you model one domain before the other, it is imperative that you consider all business partner roles in that model.

Data Governance Considerations

A data governance framework may typically be rolled out one master data domain at the time or in parallel. It is here essential that the data policies, data standards and business glossary for basic customer master data and basic supplier master data is coordinated.

Business Case Considerations

The business case for customer MDM will be stronger if the joint advantages with supplier MDM is incorporated – and vice versa.

This includes improvement in customer/supplier engagement and the derived supply/value chain effectiveness, cost sharing of third-party data enrichment service expenses and shared gains in risk assessment.  

Available Solutions

Check the list of innovative solutions in the MDM space here.

The Rise of Interenterprise MDM

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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.

MDM Terms In and Out of The Gartner 2020 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 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.