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.

Who are the ADM Solution Providers?

ADM MDMAs examined in the post MDM vs ADM there is a sister discipline to Master Data Management (MDM) called Application Data Management (ADM).

While there are plenty of analyst market reports on who are the MDM solution providers, there are no similar ADM solution market reports. Not even by Gartner, who has coined the ADM term.

So, let me try to present three (to seven) examples of who might be some of the leading ADM solutions:

Oracle (CDM Cloud and Product MDM Cloud)

Oracle was thrown out of the latest Gartner Magic Quadrant for MDM Solutions as their approach reflects an exclusively ADM approach to MDM, thus meeting the associated Gartner defined exclusion criteria.

This indicates that you can use Oracle technology to underpin data management encompassing master data and other critical application data as long as these data are managed in an Oracle application or brought from somewhere else into the Oracle application before the data management capabilities are applied.

SAP ECC, S/4HANA, MDG

A lot of master/application data management takes place inside SAP’s ERP application which was called ECC and is now being replaced by S/4HANA. As SAP ERP do not provide much help for master data management, there are third-party applications that helps with that. One example I have worked with is it.mds.

SAP has introduced their newest MDM solution called SAP MDG (Master Data Governance). While this MDM solution in theory may be a solution that embraces all master data within an enterprise, it is, as I see it, in practice used to govern master data that sits in SAP ECC or S/4HANA as the core advantage of SAP MDG is that it fits with the SAP ERP data model and technology set up.

Semarchy xDM

The Semarchy solution is called xDM, implying that x can be everything as M for MDM, R for RDM (Reference Data Management) and A for ADM. In this approach the data management capabilities as data governance, hierarchy management and workflow management are applied in their Intelligent Data Hub™ regardless of the brand of the source (and target) application.

xDM from Semarchy is one of the featured solutions on The Disruptive MDM / PIM / DQM List. Learn more her.

Data Quality Tool Market Musings

There is a market for data quality tools and most of the tools on this market have been operating since before year 2k either still by an independent data quality tool provider or now as part of a data management suite.

The prominent market reports telling about the market with generic ranking of the solutions are from Gartner and Information Difference (and earlier on from Forrester, who though have not published a report on data quality tools for years now).

The latest Gartner report from March 2019 was examined in the post Data Quality Tools are Vital for Digital Transformation. Information Difference has their report available here: The Data Quality Landscape – Q1 2019.

As told in the post DQM Tools In and Around MDM Tools there are three main ways of providing Data Quality Management (DQM) capabilities: As an independent data quality tool, as part of a data management suite or inside an MDM platform. The data quality tool market reports encompasses the first two of three options (while the MDM solution market reports encompasses the latter two of three).

DQ Tool Market

The data quality tools represented in the above-mentioned DQ markets reports are:

  • Informatica, who acquired the veteran DQ service SSA-Name3, Similarity Systems and other DQ services, now part of the Informatica data management suite
  • Experian, who acquired among others veteran DQ service QAS and also offers other solutions mainly related to credit check and fraud prevention
  • Syncsort, who acquired veteran DQ service Trillium, recently also Pitney Bowes and also have some other data management services
  • IBM, who acquired DQ services as Ascential now being part of a data management suite with several overlapping services
  • SAP, who bought several DQ services now being part of the huge ERP/data management suite
  • SAS Institute, who acquired Dataflux that now is part of the BI focused suite
  • Talend, as part of a data management suite
  • Oracle, who acquired veteran DQ service Datanomic and other DQ services now being part of the ERP/data management suite
  • Information Builders, as part of a data management and BI suite
  • Ataccama, together with MDM services
  • Melissa, a veteran company in DQ
  • Uniserv, a veteran company in DQ
  • Innovative, a veteran company in DQ
  • Datactics, a veteran company in DQ
  • MIQsoft
  • RedPoint Global
  • BackOffice Associates, as part of a data governance focused solution

However, there are a lot of other data quality tools on the market.

PS: If you represent a vendor providing DQM capabilities as an independent data quality tool, as part of a data management suite or inside an MDM / PIM solution, you are welcome to register your solution on The Disruptive MDM / DQM List here.

First Experiences with the MDM / PIM Solution Selection Service

As announced 3 weeks ago here on the blog I have started a service for MDM / PIM solution ranking based on your context, scope and requirements.

It has been good to see that the first 2-digit number of people have requested their solution list based on their specific requirements. A few have also provided the feedback, that they actually already had made a list. In these cases, I am happy that the responses were, that the result from the automated selection process corresponded very well with their traditional and (I guess) time and money consuming selection project.

The set of requirements I have processed have been very varying and thus the solution lists have also been somewhat different encompassing a lot of solution vendors both being on The Disruptive MDM / PIM List as well as those recognized by Gartner, Forrester and Information Difference.

Anyone else who would like to jumpstart a tool selection? Start here.

Select your solution process

How Data Discovery Makes a Data Hub More Valuable

Data discovery is emerging as an essential discipline in the data management space as explained in the post The Role of Data Discovery in Data Management.

In a data hub encompassing master data, reference data, critical application data and more, data discovery can play a significant role in the continuous improvement of data quality and how data is governed, managed and measured along with an ever evolving business model and new data driven services.

Data discovery serves as the weapon used when exploring the as-is data landscape at your organization with the aim of building a data hub that reflects your data model and data portfolio. As the data maturity is continuously improved reflected in step-by-step maturing to-be states, data discovery can be used when increasing the data hub scope by encompassing more data sources, when new data driven services are introduced and the business model is enhanced as part of a digital transformation.

Data Discovery Outcome

In that way data discovery is an indispensable node in maturing the data supply chain and the continuously data quality improvement cycle that must underpin your digital transformation course.

Learn more about the data discovery capability in a data hub context in the Semarchy whitepaper authored by me and titled Intelligent Data Hub: -Taking MDM to the Next Level.

The People Behind the MDM / PIM Tools

Over at the sister site, The Disruptive MDM / PIM List, there are some blog posts that are interviews with some of the people behind some of the most successful Master Data Management (MDM) and Product Information Management (PIM) tools.

People behind MDM tools

CEO & Founder Upen Varanasi of Riversand Technologies provided some insights about Riversand’s vision of the future and how the bold decisions he had made several years ago led to the company’s own transformational journey and a new MDM solution. Read more in the post Cloud multi-domain MDM as the foundation for Digital Transformation.

In a recent interview FX Nicolas, VP of Products at Semarchy, tells about his MDM journey and explains how the Semarchy Intelligent Data Hub™:

  • Extends the scope of data available via the data hub beyond core master data
  • Takes an end-to-end approach for the data management initiative
  • Transparently opens the initiative to the whole enterprise

Read the full interview here.

I hope to be able to present more people behind successful solutions on The Disruptive MDM / PIM List Blog.

The Pain of Getting Product Information from Your Suppliers

If you are a merchant (retailer or a B2B dealer) of tangible goods a huge challenge in today’s data driven world is the get complete product information from your suppliers being the importers, brand owners and/or manufacturers of the products.

There are plenty of bad ways of trying to do that:

  • Send them a spreadsheet to be filled in
  • Build a supplier portal where they can do the work
  • Get the data from a data pool
  • Outsource the collection process to someone far away

Sean Sinclair sums this up nicely in the LinkedIn article called Feeding the Monster – Product Data Onboarding for ‘Hundreds and Thousands’…

Coincidentally Sean and I at the same time worked with these challenges at two different major competing UK distributors/dealers of building materials up in the West Midlands.

The only solution will be to create a win-win situation for both manufacturers and merchants as explained in the post Merchants vs Manufacturers in the Information Age.

Standoff both sides narrow

Movements in the Constellation Research MDM Shortlist

One of the not so often mentioned analyst MDM market reports is the Constellation Shortlist™ Master Data Management.

The Q3 2018 version was mentioned here on the blog in the post Making Your MDM Vendor Longlist and Shortlist.

The Q3 2019 version has these changes compared to the shortlist a year ago:

  • Orchestra Networks is renamed to Tibco EBX
  • Oracle CDM Cloud is joined by Oracle Product MDM
  • Stibo Systems is a new entry

Constellation MDM ShortlistTwo observations:

  • At analyst firms Gartner and Forrester, Oracle is not considered as a (major) MDM market player anymore.
  • SAP MDG is the only megavendor solution not reaching this generic shortlist

PS: If you need a shortlist tailored to your context, scope and requirements, you can get it on The Disruptive MDM list here.

When Vendors Decline to Participate in Analyst Research

In the Master Data Management (MDM) and Product Information Management (PIM) space there are some analyst market reports with vendor rankings used by organizations when doing a tool selection project.

These reports are based on the analyst’s survey at their customers and perhaps other end user organizations as well as the analysts research in corporation with the solution vendor. However, sometimes the latter part does not happen.

One example was the Gartner late 2017 Magic Quadrant for MDM solutions where IBM declined to participate as reported in the post Why IBM Declined to Participate in The Gartner MDM Magic Quadrant.

Another example is the Forrester and Informatica dysfunctional relationship. In the Forrester 2019 MDM Wave it is stated that “Informatica declined to participate in our research. This was also apparent in the Forrester 2018 PIM Wave where Forrester’s placement of Informatica as a Germany-based vendor didn’t reflect movements (and perhaps achievements) since 2012 as told in the post MDM Alternative Facts.

Both Gartner and Forrester have though positioned IBM and Informatica in their plot with the note that the research did not include interaction with the vendor.

Analyst Relationship

Information Difference has taken another approach and does not include nonparticipating vendors as discussed in the post Movements in the MDM Vendor Landscape 2019.

This challenge is a bit close to me as I am running a list of MDM / PIM / DQM vendors where there now also is a ranking service based on individual context, scope and requirements. Here I have chosen to include vendor solutions on the three above analyst reports and the list itself as noted in select your solution step 4.

Longlist, Shortlist and Proof of Concept

When selecting a tool for a Master Data Management (MDM) / Product Information Management (PIM) / Data Quality Management (DQM) solution you can:

  • Select a longlist of 5 to 10 solutions that you after some research narrow down to a shortlist and after some more thorough research you will from this select a solution for a PoC / contract.
  • Select a shortlist of 3 to 5 solutions and after some research select a solution for a PoC / contract.
  • Directly select a solution for a Proof of Concept (PoC) and Business Case.

How would you – or did you – select a tool?

 

By the way: There are also some different approaches to get the work done:

Longlist shortlist PoC