Making Your MDM Vendor Longlist and Shortlist

Various analyst firms are making more or less periodic reports with vendor rankings and recommendations for the Master Data Management (MDM) market.

The latest one from Constellation Research is their Constellation ShortList™ Master Data Management authored by R “Ray” Wang.

The public part of the Q3 2018 report reveals 6 shortlisted vendors among over 15 evaluated ones.

Shortlist

Some viable solutions among there indeed.

PS: If you would prefer not to start with a generic shortlist, you can compile your relevant longlist and weighted shortlist based on The Disruptive Master Data Management Solutions List.

MDM Hype Cycle, GDSN, Data Quality, Multienterprise MDM and Product Data Syndication

Gartner, the analyst firm, has a hype cycle for Information Governance and Master Data Management.

Back in 2012 there was a hype cycle for just Master Data Management. It looked like this:

Hype cycle MDM 2012
Source: Gartner

I have made a red circle around the two rightmost terms: “Data Quality Tools” and “Information Exchange and Global Data Synchronization”.

Now, 6 years later, the terms included in the cycle are the below:

Hype Cycle MDM 2018
Source: Gartner

The two terms “Data Quality Tools” and “Information Exchange and Global Data Synchronization” are not mentioned here. I do not think it is because the they ever fulfilled their purpose. I think they are being supplemented by something new. One of these terms that have emerged since 2012 is, in red circle, Multienterprise MDM.

As touched in the post Product Data Quality we have seen data quality tools in action for years when it comes to customer (or party) master data, but not that much when it comes to product master data.

Global Data Synchronization has been around the GS1 concept of GDSN (Global Data Synchronization Network) and exchange of product data between trading partners. However, after 40 years in play this concept only covers a fraction of the products traded worldwide and only for very basic product master data. Product data syndication between trading partners for a lot of product information and related digital assets must still be handled otherwise today.

In my eyes Multienterprise MDM comes to the rescue. This concept was examined in the post Ecosystem Wide MDM. You can gain business benefits from extending enterprise wide product master data management to be multienterprise wide. This includes:

  • Working with the same product classifications or being able to continuously map between different classifications used by trading partners
  • Utilizing the same attribute definitions (metadata around products) or being able to continuously map between different attribute taxonomies in use by trading partners
  • Sharing data on product relationships (available accessories, relevant spare parts, updated succession for products, cross-sell information and up-sell opportunities)
  • Having shared access to latest versions of digital assets (text, audio, video) associated with products.

This is what we work for at Product Data Lake – including Machine Learning Enabled Data Quality, Data Classification, Cloud MDM Hub Service and Multienterprise Metadata Management.

MDM Vendor Landscape 2018

The Information Difference MDM Landscape Q2 2018 is out.

The report confirms the trend of increasing uptake of cloud Master Data Management solutions as examined in the recent post called The Rise of Cloud MDM.

According to the report the coexistence of big data and master data is another trend and more and more MDM vendors are embracing all master data domains while though as stated “most vendors have their roots in either customer or product data, and their particular functionality and track record of deployment is usually deeper where the software had its roots”.

The plot of vendors looks like this:MDM Landscape Q2 2018You can read the full report here.

PS: Many of the vendors – and some more – are presented in depth on The Disruptive MDM List.

PPS: If you represent a vendor not on The Disruptive MDM List, you can register here.

The Rise of Cloud MDM

Cloud as a deployment method for Master Data Management (MDM) solutions is on the rise.

In the latest MDM vendor selection activities I am involved in cloud is not an absolute must but certainly the preferred deployment method.

The MDM vendor market is responding to that trend. Some of the new players offers purely cloud based solutions. In a recent post on this blog I wrote about Three Remarkable Observations about Reltio. The fourth will be that this is a cloud-based MDM (and more) solution – called Reltio Cloud.

Another example of going the cloud path is Riversand. Their new release is put forward as a cloud-native suite of Master Data Management solutions as told in an interview by Katie Fabiszak with CEO & Founder Upen Varanasi of Riversand. The interview is posted as a guest blog post on The Disruptive MDM List. The post is called Cloud multi-domain MDM as the foundation for Digital Transformation.

Cloud MDM

 

 

The Disruptive MDM List is Growing

The Disruptive MDM Solutions List is a list of available:

  • Master Data Management (MDM) solutions
  • Customer Data Integration (CDI) solutions
  • Product Information Management (PIM) solutions
  • Digital Asset Management (DAM) solutions.

The list will help you when making vendor longlists and shortlists for new implementations of these solutions.

The latest entry is Magnitude MDM. This solution was previously known as Kalido, one of the first MDM solutions to emerge on the nascent market for MDM technology back in the mid 00’s.

You can see the current list of MDM solutions here.

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Avoid Duplicates by Avoiding Peer-to-Peer Integrations

When working in Master Data Management (MDM) programs some of the main pain points always on the list are duplicates. As explained in the post Golden Records in Multi-Domain MDM this may be duplicates in party master data (customer, supplier and other roles) as well as duplicates in product master data, assets, locations and more.

Most of the data quality technology available to solve these problems revolves around identifying duplicates.  This is a very intriguing discipline where I have spent some of my best years. However, this is only a remedy to the symptoms of the problem and not a mean to eliminate the root cause as touched in the post The Good, Better and Best Way of Avoiding Duplicates.

The root causes are plentiful and as all challenges they involve technology, processes and people.

Having an IT landscape with multiple applications where master data are a created, updated and consumed is a basic problem and a remedy to that is the main reason of being for Master Data Management (MDM) solutions. The challenge is to implement MDM technology in a way that the MDM solution will not just become another silo of master data but instead be solution for sharing master data within the enterprise – and ultimately in the digital ecosystem around the enterprise.

blind-spot-take-careThe main enemy from a technology perspective is in my experience peer-to-peer system integration solutions. If you have chosen application X to support a business objective and application Y to support another business objective and you learn that there is an integration solution between X and Y available, this is very bad news. Because short term cost and timing considerations will make that option obvious. But in the long run it will cost you dearly if the master data involved are handled in other applications as well. Because then you will have blind spots all over the place where through duplicates will enter.

The only sustainable solution is to build a master data hub where through master data are integrated and thus shared with all applications inside the enterprise and around the enterprise. This hub must encompass a shared master data model and related metadata.

 

MDM Alternative Facts

When searching for information about Master Data Management (MDM) solutions you will stumble on a lot of alternative facts.

Here are three more or less grave examples:

The MDM news is filled with yet a new market research report at sale for a few thousand US dollars. These reports look at first hand to be very thorough and information rich. But usually with a closer look you will become suspicious. It may be the mention of key players where often some is missing and a few actually mentioned will be companies more known from other trades. And the structure and content, as in the below example, seems to be a copy paste from other trades. Hmmm… “Production”, “Gross Margin” …. Seems to be more about the global cement market.

Market Research MDM.png

The next example is from an article called The 4 Best Master Data Management (MDM) Software Tools to Consider. Oracle, Profisee, Talend and SAP are all viable candidates. But the below justification for Oracle seems to be very little about MDM.

Oracle MDM

Finally, on the pedantic side, even the recognized analyst firms can make a mistake (or a copy paste from earlier years). Forrester places Informatica as a German company. Well, it is the Product Information Management (PIM) wave and Informatica got into PIM (now Product 360 MDM) by buying the German PIM vendor Heiler.

Infa as German.pngNope, there is no such thing as a single version of the truth.

Learn from MDM Vendors

The Disruptive List of Master Data Management Solutions has a blog. On this blog some of the leading MDM vendors provides guest blog posts with their perspective on both emerging trends and good old fundamental prerequisites within the MDM discipline.

Earlier this year David Corrigan of AllSight wrote a thought provoking post about 3 Reasons MDM No Longer Delivers a Customer 360.    

Yesterday Shamanth Shankar of Riversand contributed with a timely post on Why next generation MDM and PIM solutions must be in the Cloud.

Today we have a well-founded post by Nils Erik Pedersen of Stibo Systems on the Five Steps to Guarantee a Successful Master Data Management Implementation.

Learn from MDM Vendors

The Three MDM Ages

Master Data Management (MDM) is relatively new discipline. The future will prove what is was, but standing here in mid-2018 I see that we already had 2 ages and are now slowly proceeding into a 3rd age. These ages can be coined as:

  • Pre MDM,
  • Middle MDM and
  • High MDM

Pre MDM

In these dark ages the term Master Data Management may have been used, but there were not any established discipline, methodologies, frameworks and technology solutions around that truly could count as MDM.

We had Customer Data Integration (CDI) around, we had Product Information Management (PIM) in the making and some of us were talking Data Quality Management – and that in practice being namely deduplication / data matching.

Middle MDM

MDM as Three Letter Acronym (TLA) emerged in the mid 00’s as told in the post Happy 10 Years Birthday MDM Solutions.

It was at that time Aaron Zornes changed his stage name from The Customer Data Integration Institute to The MDM Institute.

During this age many MDM solutions slowly but steadily have developed into multi-domain MDM solutions as reported over at the Disruptive MDM List in the blog post called 4 Vendor Paths to Multidomain MDM covering the road travelled by 10 vendors on the MDM market.

Most MDM solutions in the Middle MDM Age have been deployed on-premise

High MDM

We are now cruising into the High MDM Age. First and foremost a lot more organizations are now implementing MDM. Many new deployments are cloud based. New ways are tried out like encompassing more than master data in the same platform.

The jury is of course still out about what will be some main trends of the High MDM Age. My money is placed on what Gartner, the analyst firm, calls Multienterprise MDM as elaborated in the post Ecosystem Wide MDM.

MDM Ages.png

Three Remarkable Observations about Reltio

The latest entry on The Disruptive Master Data Management Solutions List is Reltio. I have been following Reltio for more than 5 years and have had the chance to do some hands on lately.

In doing that, I think there are three observations that makes the Reltio Cloud solution a remarkable MDM offering.

More than Master Data

While the Reltio solution emphasizes on master data the platform can include the data that revolves around master data as well. That means you can bring transactions and big data streams to the platform and apply analytics, machine learning, artificial intelligence and those shiny new things in order to go from a purely analytical world for these disciplines to exploit these data and capabilities in the operational world.

The thinking behind this approach is that you can not get a 360-degree on customer, vendor and other party roles as well as 360-degree on products by only having a snapshot compound description of the entity in question. You also need the raw history, the relationships between entities and access to details for various use cases.

In fact, Reltio provides not just operational MDM, but through a module called Reltio IQ also brings continuously mastered data, correlated transactions into an Apache Spark environment for analytics and Machine Learning. This eliminates the traditional friction of synchronizing data models between MDM and analytical environments. It also allows for aggregated results to be synchronized back into the MDM profiles, by storing them as analytical attributes. These attributes are now available for use in operational context, such as marketing segmentation, sales recommendations, GDPR exposure and more.

Multiple Storing Capabilities

There is an ongoing debate in the MDM community these days about if you should use relational database technology or NoSQL technology or graph technology? Reltio utilizes all three of them for the purposes where each approach makes the most sense.

Reference data are handled as relational data. The entities are kept using a wide column store, which is a technique encompassing scalability known from pure column stores but with some of the structure known from relational databases. Finally, the relationships are handled using graph techniques, which has been a recurring subject on this blog.

Reltio calls this multi-model polyglot persistence, and they embrace the latest technologies from multiple clouds such as AWS and Google Cloud Platform (GCP) under the covers.

Survival of the Fit Enough

One thing that MDM solutions do is making a golden record from different systems of records where the same real-world entity is described in many ways and therefore are considered duplicate records. Identifying those records is hard enough. But then comes the task of merging the conflicting values together, so the most accurate values survive in the golden record.

Reltio does that very elegantly by actually not doing it. Survivorship rules can be set up based on all the needed parameters as recency, provenance and more and you may also allow more than one value to survive as touched in the post about the principle of Survival of the Fit Enough.

In Reltio there is no purge of the immediately not surviving values. The golden record is not stored physically. Instead Reltio keeps one (or even more than one) virtual golden record(s) by letting the original source records stay. Therefore, you can easily rollback or update the single view of the truth.

The Reltio platform allows survivorship rules to be customized in rulesets for an unlimited number of roles and personas. In effect supporting multiple personalized versions of the truth. In an operational MDM context this allows sales, marketing, compliance, and other teams to see the data values that they care about most, while collaborating continuously in what Reltio calls the Self-Learning Enterprise.

Going beyond operational MDM