No One MDM Solution Can Fully Satisfy All Current and Future Use Cases

The title of this post is taken from the Gartner Critical Capabilities for Master Data Management Solutions.

One implication of this observation is that you when selecting your solution will not be able to use a generic analyst ranking of solutions as examined in the post Generic Ranking of Vendors versus an Individual Selection Service.

Selection Model

This is the reason of being for The Disruptive MDM / PIM / DQM List.

Another implication is that even the best fit MDM solution will not necessarily cover all your needs.

One example is within data matching, where I have found that the embedded solutions in MDM tools often only have limited capabilities. To solve this case, there are best of breed data matching solutions on the market able to supplement the MDM solutions.

Another example close to me is within multienterprise (business ecosystem wide) MDM, as MDM solutions are focused on each given organization. Here your interaction with a trading partner, and the interaction by the trading partner with you, can be streamlined with a solution like Product Data Lake.

What is a Golden Record?

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 that 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 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, which is one of my favourite subjects, 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.

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 being the same real world entity is dependent on whether that location is a single-family house or a nursing home.

Golden RecordsProduct 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 we must, in my eyes, rely more on second party master data meaning sharing product master data within the business ecosystems where you operate.

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.

The Latest Constellation Research MDM Shortlist

The new Constellation Research generic shortlist for Master Data Management (MDM) is out.

Compared to the previous list Semarchy is a new entry which follows up their up-right move in The Latest Gartner MDM Magic Quadrant. So, another acknowledgement of the Semarchy Intelligent Data Hub concept. It is good to see that someone I started to blog about 8 years ago is now going to the top of the market.

Else IBM, Informatica, Reltio, Riversand, Stibo Systems and Tibco EBX stays on the list.

Constellation Research has now realized, like Gartner also did some while ago, that Oracle has left the MDM market. Thus the Oracle expansion on the previous shortlist is now followed up by a goodbye.

Fun fact: The guys who started Semarchy left Oracle a decade ago with the aim to build a better MDM solution as told by FX Nicolas of Semarchy in this interview.

Check out the Constellation Shortlist(tm) Master Data Management here.

Constellation MDM Shortlist 2020 Q1
Source: Constellation Research

MDM License Distribution

Some of the hard facts presented in the Gartner Magic Quadrant for Master Data Management (MDM) Solutions is how the vendor licenses are distributed between the various master data domains. You can find these figures from the previous quadrant in the post Counting MDM Licenses.

The Latest MDM Magic Quadrant also includes these numbers. In order to highlight how the vendors have different profiles, let us concentrate on the innovative solutions registered on The Disruptive MDM / PIM / DQM List.

MDM License Distribution
Source: Gartner

The above figure shows the three domains where the vendor has sold the most licenses and how many customers who are handling multiple domains.

Contentserv is coming from a strong position in the Product Information Management (PIM) market and still have the vast part of licenses attached to product master data.

Enterworks is also coming from the PIM space and are with their ecosystem wide (or interenterprise as Gartner says) approach building up the multidomain grip through encompassing supplier master data.

Informatica is covering all domains with their suite of 360 solutions and have a good portion of customers doing multidomain MDM.

Reltio does cover all domains but are increasingly focusing on the customer domain with their connected customer 360 offering that encompasses all customer data.

Riversand is another vendor coming from the PIM space that is now growing into the multidomain MDM sphere with their new cloud-native platform.

Semarchy is with their Intelligent Data Hub concept going beyond multidomain MDM into handling more kinds of data as reference data and critical application data.

This diversity means that you cannot just use a generic ranking as presented in the magic quadrant when selecting the best fit solution for your intended solution. You must make a tailored selection.

Growth on the MDM Market

As reported yesterday a new Gartner MDM Magic Quadrant is out.

MDM Growth
MDM license and maintenance revenue in M USD and growth %. Source: Gartner.

While the change in positions is limited the change in market share is more significant, if we look at the license and maintenance revenue estimates provided by Gartner. The numbers included in the new quadrant published in January 2020 are 2018 estimates. The figure here compares those numbers to the 2017 numbers included in the previous quadrant published in December 2018.

The trend is that the largest providers are not growing as fast as some of the midsize providers, where Reltio, Ataccama and Semarchy are going in the fast lane.

If any of the solution providers have some 2019 updates, please comment here.

The Latest MDM Magic Quadrant

With some delay the latest Gartner Magic Quadrant for Master Data Management Solutions has now been published and has began surfacing on the vendor websites.

I will get back with more take away. As a starter, a short look at the movements in the quadrant, where it can be observed that:

  • Informatica is heading towards the top-right corner
  • TIBCO Software has defended the second place held by Orchestra Networks following the take over
  • Semarchy is up in third position following the high jump into the challengers quadrant last year

MDM MQ 18 to 19Bigger picture here.

Download the report from Semarchy here or Informatica here.

It Is Black Friday and Cyber Monday All the Time at the Disruptive MDM / PIM / DQM List

The upcoming Black Friday and Cyber Monday are synonymous with good deals.

At the Disruptive MDM / PIM / DQM List there are good deals all the days.

As a potential buyer on the look for a solution covering your Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) needs you can use the free service that based on your context, scope and requirement selects the best fit solution(s). You can start here.

Black Friday

As a solution provider you can against a very modest fee register your solution here.

Happy Black Friday and Cyber Monday.

Combining Data Matching and Multidomain MDM

Data Matching GroupTwo of the most addressed data management topics on this blog is data matching and multidomain Master Data Management (MDM). In addition, I have also founded two LinkedIn Groups for people interested in one of or both topics.

The Data Matching Group has close to 2,000 members. In here we discus nerdy stuff as deduplication, identity resolution, deterministic matching using match codes, algorithms, pattern recognition, fuzzy logic, probabilistic learning, false negatives and false positives.

Check out the LinkedIn Data Matching Group here.

Multidomain MDM GroupThe Multi-Domain MDM Group has close to 2,500 members. In here we exchange knowledge on how to encompass more than a single master data domain in an MDM initiative. In that way the group also covers the evolution of MDM as the discipline – and solutions – has emerged from Customer Data Integration (CDI) and Product Information Management (PIM).

Check out the LinkedIn Multi-Domain MDM Group here.

The result of combining data matching and multi-domain MDM is golden records. The golden records are the foundation of having a 360-degree / single view of parties, locations, products and assets as examined in The Disruptive MDM / PIM / DQM List blog post Golden Records in Multidomain MDM.

Why Multienterprise MDM will Underpin Digital Transformation

I read (and write) a lot about why Master Data Management (MDM) is a core capability you need to succeed in digital transformation.

Over at the Profisee blog there is a post about that, extending the capability to be multidomain MDM. The post is called The Role of Multi-Domain MDM in Digital Transformation.

Also, at the Reltio blog as part of the #ModernDataMasters series, Tony Saldanha, author of the book  Why Digital Transformations Fail, explains: “Look at master data in terms of the entire virtual company – the total supply chain including your clients and suppliers – and create an ecosystem to drive standards across that.”

Tony continues: “The investment in master data within ecosystems is going to increase dramatically. People are going to realise that most of the waste that happens is at the seams of large organisations – not having a common language between the accounts payable of one company and the accounts receivable of another company means both companies are wasting resources and money.”

Multienterprise MDM Digital Transformation

This way of looking at MDM as something that goes beyond each organization and evolves to be ecosystem wide is also called Multienterprise MDM.

In my eyes this is a very important aspect of using MDM within digital transformation. This theme is further examined in the post Why is Your Digital Ecosystem and MDM the Place to Begin in Digital Transformation?

Unifying Data Quality Management, MDM and Data Governance

During the end of last century data quality management started to gain traction as organizations realized that the many different applications and related data stores in operation needed some form of hygiene. Data cleansing and data matching (aka deduplication) tools were introduced.

In the 00’s Master Data Management (MDM) arised as a discipline encompassing the required processes and the technology platforms you need to have to ensure a sustainable level of data quality in the master data used across many applications and data stores. The first MDM implementations were focused on a single master data domain – typically customer or product. Then multidomain MDM (embracing customer and other party master data, location, product and assets) has become mainstream and we see multienterprise MDM in the horizon, where master data will be shared in business ecosystems.

MDM also have some side disciplines as Product Information Management (PIM), Digital Asset Management (DAM) and Reference Data Management (RDM). Sharing of product information and related digital assets in business ecosystems is here supported by Product Data Syndication.

Lately data governance has become a household term. We see multiple varying data governance frameworks addressing data stewardship, data policies, standards and business glossaries. In my eyes data governance and data governance frameworks is very much about adding the people side to the processes and technology we have matured in MDM and Data Quality Management (DQM). And we need to combine those themes, because It is not all about People or Processes or Technology. It is about unifying all this.

In my daily work I help both tool providers and end user organisations with all this as shown on the page Popular Offerings.

DG DQ and MDM