Maturing RDM, MDM and ADM With Collaborative Data Governance

Data Governance and Master Data Management (MDM) are overlapping disciplines. When embarking on a data governance initiative you may encounter some difficulties in what belongs to the data governance side and what belongs to the master data side. One of the challenges is that data governance should also encompass other data than master data. The most common examples are reference data and other critical application data than master data.

So, while you may get coverage for setting up data stewardship, processes and the data platform for master data in a traditional MDM tool, other important aspects as the data governance related to Reference Data Management (RDM) and Application Data Management (ADM) may have to be implemented separately.

This calls for taking the MDM solution to the next level by encompassing reference data and application data as well. In that way essential data governance definition components as a business glossary, data policies and data standards as well as the enforcement components through data stewardship can be implemented in a collaborative way:

RDM MDM ADM

In this case the MDM platform will be extended to be an intelligent data hub. In collaboration with FX Nicolas I will be presenting such a solution in a webinar hosted by Semarchy. The webinar goes live Wednesday 13th November at 5pm CET / 11am ET. Register here on Intelligent Data Hub: MDM and Beyond.

MDM, PIM, DAM and 7 More Data Management TLAs

What is CDI (in a data management context)? What does PDS stand for? And what about RDM?

Well, here is an under 2 minutes silent video going through what 10 common Three Letter Acronyms in the data management world is:

Read more about the Three Letter Acronyms in the post 10 Data Management TLAs You Should Know.

Learn more about some of the best solutions in this space on The Disruptive MDM / PIM / DQM List.

What MDMographic Stereotype is Your Organization?

In marketing we use the term demographic stereotype for segmenting individual persons according to known data elements as age and where we live. There is also a lesser used term called firmographic stereotypes, where companies are segmented according to industry sector, size and other data elements.

Solutions for Master Data Management (MDM) and related disciplines are often presented by industry sector. In my work with tool selection – either as a thorough engagement or a quick select your solution report – I have identified some MDMographic stereotypes, where we have the same requirements based on the distribution of party (customer and supplier/vendor) entities and product entities:

MDMographic Stereotypes and Venn

These stereotypes are further explained in the post Six MDMographic Stereotypes.

MDM / PIM / DQM List September 2019 Achievements

September 2019 were in many ways a record month for the sister site – The Disruptive MDM / PIM / DQM List.

The number of visitors has doubled compared to last month and increased three times compared to last year’s average – and so have the page views.

Number of LinkedIn page followers has increased 27 % since last month.

The new Select your solution service has got a good debut with 16 requests for a tailored list of solutions fit for your context, scope and requirements.

A new solution has entered the list: Check out Reifier here.

MDMlist stats 2019 09

Thanks to all visitors, followers, solution selectors and registrants.

10 Data Management TLAs You Should Know

TLA stands for Three Letter Acronym. The world is full of TLAs. The IT world is full of TLAs. The Data Management world is full of TLAs. Here are 10 TLAs from the data management world that have been mentioned a lot of times on this blog and the sister blog over at The Disruptive MDM / PIM / DQM List:

MDM = Master Data Management can be defined as a comprehensive method of enabling an enterprise to link all of its critical data to a common point of reference. When properly done, MDM improves data quality, while streamlining data sharing across personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications. You can find the source of this definition and 3 other – somewhat similar – definitions in the post 4 MDM Definitions: Which One is the Best?

PIM = Product Information Management is a discipline that overlaps MDM. In PIM you focus on product master data and a long tail of specific product information related to each given classification of products. This data is used in omni-channel scenarios to ensure that the products you sell are presented with consistent, complete and accurate data. Learn more in the post Five Product Information Management Core Aspects.

DAM = Digital Asset Management is about handling rich media files often related to master data and especially product information. The digital assets can be photos of people and places, product images, line drawings, brochures, videos and much more. You can learn more about how these first 3 mentioned TLAs are connected in the post How MDM, PIM and DAM Stick Together.

DQM = Data Quality Management is dealing with assessing and improving the quality of data in order to make your business more competitive. It is about making data fit for the intended (multiple) purpose(s) of use which most often is best to achieved by real-world alignment. It is about people, processes and technology. When it comes to technology there are different implementations as told in the post DQM Tools In and Around MDM Tools.

RDM = Reference Data Management encompass those typically smaller lists of data records that are referenced by master data and transaction data. These lists do not change often. They tend to be externally defined but can also be internally defined within each organization. Learn more in the post What is Reference Data Management (RDM)?

10 TLA show

CDI = Customer Data Integration, which is considered as the predecessor to MDM, as the first MDMish solutions focussed on federating customer master data handled in multiple applications across the IT landscape within an enterprise. You may ask: What Happened to CDI?

CDP = Customer Data Platform is an emerging kind of solution that provides a centralized registry of all data related to parties regarded as (prospective) customers at an enterprise. Right now, we see such solutions coming both from MDM solution vendors and CRM vendors as reported in the post CDP: Is that part of CRM or MDM?

ADM = Application Data Management, which is about not just master data, but all critical data however limited to a single (suite of) application(s) at the time. ADM is an emerging term and we still do not have a well-defined market as examined in the post Who are the ADM Solution Providers?

PXM = Product eXperience Management is another emerging term that describes a trend to distance some PIM solutions from the MDM flavour and more towards digital experience / customer experience themes. Read more about it in the post What is PxM?

PDS = Product Data Syndication, which connects MDM, PIM (and other) solutions at each trading partner with each other within business ecosystems. As this is an area where we can expect future growth along with the digital transformation theme, you can get the details in the post What is Product Data Syndication (PDS)?

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.