Constellation Research MDM Shortlist Q1 2021

There is a new MDM market report with vendor assessment out. It is the Constellation ShortList™ Master Data Management Q1 2021.

The report highlights a shortlist of the solutions you have to know. This one has 6 solutions:

Compared to the previous shortlist, Stibo Systems has been dropped. The explanation is: “This Q1 2021 update removes Stibo Systems from this ShortList due to what Constellation sees as slow progress on cloud deployment options.”

I find this a bit peculiar.

While cloud MDM is an important theme and Stibo Systems has not been a front runner in this game, it is by far not the only important theme, which strangely also is stated in the reports threshold criteria.

In my work with selecting a longlist/shortlist/PoC candidate for actual MDM considerations at 250 organizations per year via The Disruptive MDM/PIM/DQM List, Stibo Systems is part of many shortlists and is the best fit in some cases.

Also, Stibo Systems is a front runner in some other important MDM themes. One example is Interenterprise MDM through Product Data Syndication.

Interenterprise MDM Will be Hot

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.

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:

  • 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.
  • 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.

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, Operation and Repair). 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.   

Movements in the Gartner MDM MQ 2021

This is the fourth and final blog post on the main take away from the fresh published Gartner Magic Quadrant for Master Data Management Solutions 2021.

The first post here touched on the quadrant advancements being the vendors that have moved between the 4 quadrants.

Unfortunately, Gartner has not, as in previous years, stated the revenue for all the vendors, so that you can determine the growth directly. Gartner though mentions, that Semarchy, Reltio and Ataccama had 2-digit revenue growth and that IBM had shrinking MDM revenue – again. We may then assume that the other recurring vendors had 1-digit revenue growth. However, it is mentioned that Riversand had a 10m USD revenue growth, which could indicate a 2-digit revenue growth for them too.

Combining quadrant advancements and revenue growth statements results in this movement overview:

Based on statements in the Gartner MDM MQ

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.

The Quasimodo Quadrant

The 2021 Magic Quadrant for Master Data Management (MDM) Solutions went public yesterday as reported here.

Quasimodo is the main protagonist of the novel The Hunchback of Notre-Dame. Somehow the plot of vendors in this year’s MDM quadrant looks like (a caricature of) a hunchback. The vendors are in general better in “Ability to Execute” than in “Completeness of Vision”.

So, MDM vendors in general may lack something in market understanding, marketing strategy, product strategy, innovation and more.

This does resonate with me. As also stated in the quadrant some vendors are too invisible in the market buzz. There are heaps of emerging MDM use cases where it is not that easy to find a suitable solution not to say finding one well-fit solution for a range of use cases in a given organization with a given IT landscape.

Gartner Reports 28 % Increase in Client Inquiries for MDM

The new Gartner Magic Quadrant for Master Data Management Solutions 2021 is out.

There is as usual two main pieces of take away:

  • The inclusion and positioning of the vendors
  • The message about where the market is heading

The first one is here:

Some noteworthy movements from the previous quadrant are:

  • Semarchy and Riversand have advanced to being leaders
  • Contentserv, Reltio and Ataccama have moved up as challengers
  • Syniti and PiLog are new inclusions in the report
  • Propecta MDO has emerged into the honourable mentions part of the report

The market is probably also heading up. As stated in the report: “From March 2020 — when COVID-19 became a pandemic and global crisis — until December 2020, Gartner had a 28% increase in client inquiries compared with the same period in 2019”.

You can, against a small set of your Personally Identifiable Information, get a free copy of the report at the Semarchy site here.

Stay tuned for more pieces of take away from the quadrant report in the coming days.

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.

What to Expect from the 2021 Gartner MDM Magic Quadrant?

Today the 11th January 2021 we should, according to the Gartner publishing schedule, expect a refreshed Gartner Magic Quadrant for Master Data Management (MDM) Solutions.

Historically these quadrants have been delayed possibly due to fighting with vendors objecting to the results herein.

An observation is that the thorough process applied by Gartner makes the results in here a bit behind what is currently happening on the market as touched in the post Why are Analyst Rankings Behind the MDM Market Dynamics? If say the information used in a fresh published quadrant is between a half to a full year old, the latest quadrant to be used in a given tool assessment can be founded on up to 2 years old data.

The last Magic Quadrant for MDM was mentioned in this post.

As touched in the post the two advancing vendors in here were Informatica, who extended their lead, and Semarchy, who became top challenger.

With Informatica it is hard to confirm their position in other analyst reports. Informatica has a dysfunctional relationship with Forrester, so they are not included in their latest MDM reports. Information Difference did not assess Informatica that favourable in their ranking as seen in the post Who is in the MDM Landscape Q2 2020? Will be interesting to see if Gartner keeps having a view on Informatica MDM which is different from most other sources.

Semarchy seems to keep up their momentum from what I hear from the market. Let us see if Gartner reflects that too.

The fastest growing vendor last time was Reltio as reported in the post What has Changed with the Gartner MDM Magic Quadrant? I hope Gartner keeps publishing these revenue estimates, so we can see who has grown the most and who has grown not so much or even shrunk as it happened with IBM and Riversand in the previous check.

Stay tuned for a summary of and link to first free reprints of the refreshed MDM Magic Quadrant.