What Will the Twenty-Twenties Bring to MDM?

We are now entering into a new decade: The twenty-twenties. Last time it was the twenties, back in the nineteen-twenties, this decade was defined as the Golden Age Twenties, the Roaring Twenties the Jazz Age or in French “Années folles” meaning the Crazy Years.

If we look into Master Data Management (MDM) the next decade could be the golden age of MDM. The MDM discipline has been around for 15 years or so now. The number of organizations that have implemented an MDM solution is not a big number – perhaps around 10,000 world-wide as examined in the post Counting MDM Licenses. This number may be more than 10-fold higher at the end of the decade and thus making MDM – and what is beyond – a common part of enterprise IT landscapes.

The next MDM decade will probably also be roaring, jazzy and even crazy. A lot is happening to MDM solutions and there are many questions to be answered about how the market will develop, as for example:

  • Will Customer Data Platforms(CDP) be part of CRM or MDM?
  • Will the MDM market and the Product Information Market (PIM) market be a big union or two separate markets?
  • Besides customer master data and product master data, which other master data, reference data and application data will commonly be included in MDM hubs?

You could also argue about if MDM will survive as a discipline!

What is your prediction about the next MDM decade?

MDM 20s

Major Generic MDM / PIM / DQM Solution Rankings

Analyst firms occasionally publish market reports with a generic ranking of solutions for MDM, PIM and DQM.

The latest such ones include:

The publication schedule from the analyst firms can be unpredictable.

Information Difference is an exception. There have during the years every year been a Data Quality landscape named Q1 and published shortly after that quarter and an MDM landscape named Q2 and published shortly after that quarter. However, these reports are relying on participation from relevant vendors and not all vendors prioritize this scheme.

Forrester is quite unpredictable both with timing and which market segments (MDM, PIM, DQM) to be covered.

Gartner is a bit steadier. However, for example the MDM solution reports have been coming in varying intervals during the latest years. Let us see when the next ones are published and what news they bring.

MDM PIM DQM Solutions

PS: You can check out many of the included solutions on This Disruptive MDM / PIM / DQM List.

PPS: You can get a free ranking based on your context, scope and requirements here.

Jumpstart Your MDM / PIM / DQM Solution Selection

The solution selection service on this site started 3 months ago as told here.

Since then Master Data Management (MDM) and Product Information Management (PIM) solutions have been joined by Data Quality Management (DQM) solutions, where some of the most innovative DQM solutions have joined the listing on this site.

More than 50 requesters have provided information about the context, scope and requirements of their intended solution and based on that received a report telling:

  • Your solution listWhich solution that is the best fit for a direct proof of concept
  • Which 3 solutions that are the best fit for a shortlist of solutions
  • Which 7 solutions that are the best fit for a longlist of solutions

Depending on your organization’s rules and the circumstances of your solution selection this report is aimed to jumpstart your selection process using one of the above selections.

The requesters of this report that have given feedback have provided positive responses as told in the post about the First Experiences with the MDM / PIM Solution Selection Service.

The service is still free. Start here.

It Is Black Friday and Cyber Monday All the Time at the 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.

Six AI and ML Use Cases Within MDM

One of the hottest trends in the Master Data Management (MDM) world today is how to exploit Artificial Intelligence (AI) and ignite that with Machine Learning (ML).

This aspiration is not new. It has been something that have been going on for years and you may argue about when computerized decision support and automation goes from being applying advanced algorithms to being AI. However, the AI and ML theme is getting traction today as part of digital transformation and whatever we call it, there are substantial business outcomes to pursue.

As told in the post Machine Learning, Artificial Intelligence and Data Quality perhaps all use cases for applying AI is dependent on data quality and MDM is playing a crucial role in sustaining data quality efforts.

Here are six use cases that are commonly being addressed by AI and ML capabilities:

AI MDM DQ Use Cases

  • Translating between taxonomies: As reported in the post Artificial Intelligence (AI) and Multienterprise MDM emerging technologies can help in translating between the taxonomies in use when digital transformation sets a new bar for utilizing master data in business ecosystems.
  • Transforming unstructured to structured: A lot of data is kept in an unstructured way and to in order to systematically exploit these data in AI supported business process we need make data more structured. AI and ML can help with that too.
  • Data quality issue prevention: Simple rules for checking integrity and validating data is good – but unfortunately not good enough for ensuring data quality. AI is a way to exploit statistical methods and complex relationships.
  • Categorizing data: Digital transformation, spiced up with increasing compliance requirements, has made data categorization a must and AI and ML can be an effective way to solve this task that usually is not possible for humans to cover across an enterprise.
  • Data matching: Establishing a link between multiple descriptions of the same real-world entity across an enterprise and out to third party reference data has always been a pain. AI and ML can help as examined in the post The Art in Data Matching.
  • Improving insight: The scope of MDM can be enlarged to Extended MDM Platforms where other data as transactions and big data is used to build a 360-degree view of the master data entities. AI and ML is a prerequisite to do that.