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.
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.
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.
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.
I am running a service where organizations on the look for a Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) solution can get a list of the best fit solutions for their context, scope and requirements. The service is explained in more details in the post Get Your Free Bespoke MDM / PIM / DQM Solution Ranking.
2020 was a busy year for this service. There were 176 requests for a list. About half of them came, as far as I can tell, from end user organizations and the other half came from consultancies who are helping end user organizations with finding the right tool vendor. Requests came from all continents (except Antarctica) with North America and Europe as the big chunks. There were requests from most industries thus representing a huge span in context.
Also, there where requests from a variety in organization sizes which has given insights beyond what the prominent analyst firms obtain.
It has been a pleasure also to receive feedback from requesters which has helped calibrating the selection model and verifying the insights derived from the context, scope and requirements given.
The variety in context, scope and requirements resulted in having 8 different vendor logos in top-right position and 25 different logos in all included in the 7 to 9 sized best fit extended longlists in the dispatched Your Solution Lists during 2020.
If you are on the look for a solution, you can use the service here.
If you are a vendor in the MDM / PIM / DQM space, you can register your solution here.
At this time of the year, it is custom to make a foreseeing about what will happen next year usually within a specific area – as for example data management.
After 2020 one should think that making any qualified guess about next year should be regarded within a huge amount of uncertainty.
Well, let us have a go anyway.
The horrible year of the outbreak of the pandemic has also affected the data management scene. One often mentioned theme is the accelerated digitalization, which all the bad things about the pandemic aside, seen in isolation (so to speak), is a positive development.
Digitalization also push globalization. Now you do not have to work with data management partners who is within a 5 miles reach – 5,000 kilometres will be the same.
In fact, the outlook for the data management industry is not bad at all. Digital transformation initiatives will require investments in data management consultancy, data management services and data management technology. The competition will intensify with many partners available at a global range. This will be an opportunity for smaller consultancies with broad visions, nimble service providers with scalable offerings and forward-looking tool vendors with doable solutions.
The chances for gaining market shares in a developing market are good for those of you who sell data management stuff.
The chances for getting the best help are good for those of you who buy data management stuff.
A Merry Christmas to you who celebrate this and a Happy Calendar New Year to all of you.
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).
In having that common MDM solution/framework there are some challenges to be solved in order to cater for all the contextual MDM use cases. One such challenge, being context-aware customer views, was touched upon in the post There is No Single Customer 360 View.
As said in here: “When the story of Information Quality Management is written, the first sentence of the first paragraph will include the name Larry English”.
Larry pioneered the data quality – or information quality as he preferred to coin it – discipline.
He was an inspiration to many data and information quality practitioners back in the 90’s and 00’s, including me, and he paved the way for bringing this topic to the level of awareness that it has today.
In his teaching Larry emphasized on the simple but powerful concepts which are the foundation of data quality and information quality methodologies:
Quantify the costs and lost opportunities of bad information quality
Always look for the root cause of bad information quality
Observe the plan-do-check-act circle when solving the information quality issues
Let us roll up our sleeves and continue what Larry started.
The terms “Single Customer View” (SCV) and “360 View of Customer” have been commonly used within the field of Master Data Management (MDM) since things started with the very first Customer Data Integration (CDI) solutions.
The theory is simple: A customer MDM solution creates golden records that uniquely identify any person or business who is a customer of your organization. The solution then builds out a complete description of those persons and businesses which serves as the single source of truth.
In practice, this is very hard. Compiling a concept for a view that suits all scenarios across all business units is often too daunting; the challenges involved in this effort often kill off the customer MDM implementation before completion. This is sad, because it is also hard to succeed in digital transformation and launch new digital services when you have unconnected customer views scattered across the application landscape within your organization.
Therefore, building context-aware customer views is a very useful concept when you want to deliver successful customer MDM implementations and digital transformation projects.
From time to time, analyst firms publish market reports that include their opinion and ranking of the vendors/solution providers in a specific market, such as Master Data Management (MDM).
Reading such reports, it strikes me that the rankings often do not seem to be in line with what is going in the market, especially when you consider market positioning, demand and technological developments.
Gartner’s Magic Quadrant reports are generally the most popular; their rankings often appear in corporate PowerPoint decks when businesses want to evaluate and select the right MDM solution that fits their needs. And yet, I would argue that Gartner is more conservative in its approach. For example, it took Gartner a long time to abandon the notion that there was a separate customer MDM and product MDM enterprise-level market, as I examined in my post “Who will become Future Leaders in the Gartner Multidomain MDM Magic Quadrant?”
You’ll notice that the magic quadrant from 2017 had a very limited number of market players on it; it excluded several vendors who offer MDM via cloud subscription models who are now recognised as key players and who, in hindsight, should have been included on many shortlists back then.
So, when the next Gartner Magic Quadrant for MDM is published (currently scheduled for the end of November 2020, though I hear it may be pushed to January 2021), I would always recommend you take a look at who is not included as well as those who are, and ask yourself what information has led Gartner to rank the vendors the way they have.
In that sense, Gartner’s thoroughness can often work against them as a lot of the data used in the upcoming report will be from 2019. Also, you should be aware that customer feedback is given by those who made the decision to implement a specific solution; I often hear a number of differing opinions from people across a business when they evaluate MDM solutions.
It’s also interesting to note how analyst firms differ between them. Examples from the world of MDM include a dysfunctional relationship between Forrester and Informatica as well as between Gartner and IBM, and how Forrester, opposed to Gartner, has a much more favourable assessment of a new kind of MDM provider like Reltio.