Welcome EnterWorks as a Featured Solution on The Disruptive MDM / PIM / DQM List – and to Europe

One of the rising stars on the Master Data Management (MDM) and Product Information Management (PIM) scene is EnterWorks.

Enterworks Europe LaunchThe EnterWorks solution has during the latest years, as a small crowd of other solutions on the market, grown from being a PIM solution to be a Multi-domain MDM solution. But they have not stopped there. EnterWorks is also a Multi-enterprise MDM solution and is thus covering the needs of sharing master data and product information within business ecosystems. This is, as stated by Gartner, a particularly interesting value proposition in the context of digital ecosystems.

Last year EnterWorks joined forces with WinShuttle, a major player in the data management realm.

This has led to that I, besides welcoming EnterWorks as a featured solution on the list, also is able to welcome EnterWorks to Europe. The European launch should have taken place on The Gartner Data & Analytics Summit in March. However, this event was as all other events at the moment postponed. But the launch is not. Read about the perspectives of this move in the press release on that Winshuttle Announces European Launch of EnterWorks® MDM/PIM Platform.

Scaling Up The Disruptive MDM / PIM / DQM List

The Disruptive MDM / PIM / DQM List was launched in the late 2017.

Here the first innovative Master Data Management (MDM) and Product Information Management (PIM) tool vendors joined the list with a presentation page showcasing the unique capabilities offered to the market.

The blog was launched at the same time. Since then, a lot of blog posts – including guest blog posts – have been posted. The topics covered have been about the list, the analysts and their market reports as well as the capabilities that are essential in solutions and their implementation.

In 2019 the MDM and PIM tool vendors were joined by some of the forward-looking best-of-breed Data Quality Management (DQM) tool vendors.

The Select Your Solution service was launched at the same time. Here organizations – and their consultants – who are on the look for a MDM / PIM / DQM solution can jumpstart the selection process by getting a list of the best solutions based on their individual context, scope and requirements. More than 100 hundred end user organizations or their consultants have received such a list.

MDMlist timeline

Going into the 20es the list is ready to be scaled up. The new sections being launched are:

  • The Service List: In parallel with the solution providers it is possible for service providers – like implementation partners – to register on The Service List. This list will run besides The Solution List. For an organization on the look for an MDM / PIM / DQM solution it is equally important to select the right solution and the right implementation partner.
  • The Resource List: This is a list – going live soon – with white papers, webinars and other content from potentially all the registered tool vendors and service providers divided into sections of topics. Here end user organizations can get a quick overview of the content available within the themes that matters right now.
  • The Case Study List: The next planned list is a list of case studies from potentially all the registered tool vendors and service providers. The list will be divided into industry sectors. Here end user organizations can get a quick overview of studies from similar organizations.

If you have questions and/or suggestions for valuable online content on the list, make a comment or get in contact here:

Analyst MDM / PIM / DQM Solution Reports Update March 2020

Analyst firms occasionally publish market reports with solution overview for Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM).

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.

Here is an overview of the latest major reports:

Stay tuned on this blog to get the latest on analyst reports and news on market movements.

MDM PIM DQM analysts and solutions

Looking for the Right MDM / PIM / DQM Implementation Partner

The Disruptive MDM / PIM / DQM List has been running for a couple of years and is now a list of 15 of some of the most innovative and forward looking solutions on the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) market.

The list can be seen as an online MDM / PIM / DQM conference:

The Online MDM PIM DQM Conference

In these times when offline MDM / PIM / DQM conferences are cancelled, I thought it might be useful to uncover if there is an interest among implementation partners in this space to join the list on an adjacent list on the site covering these services, as it besides choosing the right solution is equally important to choose the right implementation partner.

So, if you as an implementation partner are interested, you can register here.

Alternatively, give me a shout here:

No One MDM Solution Can Fully Satisfy All Current and Future Use Cases

The title of this post is taken from the Gartner Critical Capabilities for Master Data Management Solutions.

One implication of this observation is that you when selecting your solution will not be able to use a generic analyst ranking of solutions as examined in the post Generic Ranking of Vendors versus an Individual Selection Service.

Selection Model

This is the reason of being for The Disruptive MDM / PIM / DQM List.

Another implication is that even the best fit MDM solution will not necessarily cover all your needs.

One example is within data matching, where I have found that the embedded solutions in MDM tools often only have limited capabilities. To solve this case, there are best of breed data matching solutions on the market able to supplement the MDM solutions.

Another example close to me is within multienterprise (business ecosystem wide) MDM, as MDM solutions are focused on each given organization. Here your interaction with a trading partner, and the interaction by the trading partner with you, can be streamlined with a solution like Product Data Lake.

What is a Golden Record?

The term golden record is a core concept within Master Data Management (MDM) and Data Quality Management (DQM). A golden record is a representation of a real world entity that may be compiled from multiple different representations of that entity in a single or in multiple different databases within the enterprise system landscape.

A golden record is optimized towards meeting data quality dimensions as:

  • Being a unique representation of the real world entity described
  • Having a complete description of that entity covering all purposes of use in the enterprise
  • Holding the most current and accurate data values for the entity described

In Multidomain MDM we work with a range of different entity types as party (with customer, supplier, employee and other roles), location, product and asset. The golden record concept applies to all of these entity types, but in slightly different ways.

Party Golden Record

Having a golden record that facilitates a single view of customer is probably the most known example of using the golden record concept. Managing customer records and dealing with duplicates of those is the most frequent data quality issue around.

If you are not able to prevent duplicate records from entering your MDM world, which is the best approach, then you have to apply data matching capabilities. When identifying a duplicate you must be able to intelligently merge any conflicting views into a golden record as examined in the post Three Master Data Survivorship Approaches.

In lesser degree we see the same challenges in getting a single view of suppliers and, which is one of my favourite subjects, you ultimately will want to have a single view on any business partner, also where the same real world entity have both customer, supplier and other roles to your organization.

Location Golden Record

Having the same location only represented once in a golden record and applying any party, product and asset record, and ultimately golden record, to that record may be seen as quite academic. Nevertheless, striving for that concept will solve many data quality conundrums.

Location management have different meanings and importance for different industries. One example is that a brewery makes business with the legal entity (party) that owns a bar, café, restaurant. However, even though the owner of that place changes, which happens a lot, the brewery is still interested in being the brand served at that place. Also, the brewery wants to keep records of logistics around that place and the historic volumes delivered to that place. Utility and insurance are other examples of industries where the location golden record (should) matter a lot.

Knowing the properties of a location also supports the party deduplication process. For example, if you have two records with the name “John Smith” on the same address, the probability of that being the same real world entity is dependent on whether that location is a single-family house or a nursing home.

Golden RecordsProduct Golden Record

Product Information Management (PIM) solutions became popular with the raise of multi-channel where having the same representation of a product in offline and online channels is essential. The self-service approach in online sales also drew the requirements of managing a lot more product attributes than seen before, which again points to a solution of handling the product entity centralized.

In large organizations that have many business units around the world you struggle with having a local view and a global view of products. A given product may be a finished product to one unit but a raw material to another unit. Even a global SAP rollout will usually not clarify this – rather the contrary.

While third party reference data helps a lot with handling golden records for party and location, this is lesser the case for product master data. Classification systems and data pools do exist, but will certainly not take you all the way. With product master data we must, in my eyes, rely more on second party master data meaning sharing product master data within the business ecosystems where you operate.

Asset (or Thing) Golden Record

In asset master data management you also have different purposes where having a single view of a real world asset helps a lot. There are namely financial purposes and logistic purposes that have to aligned, but also a lot of others purposes depending on the industry and the type of asset.

With the raise of the Internet of Things (IoT) we will have to manage a lot more assets (or things) than we usually have considered. When a thing (a machine, a vehicle, an appliance) becomes intelligent and now produces big data, master data management and indeed multi-domain master data management becomes imperative.

You will want to know a lot about the product model of the thing in order to make sense of the produced big data. For that, you need the product (model) golden record. You will want to have deep knowledge of the location in time of the thing. You cannot do that without the location golden records. You will want to know the different party roles in time related to the thing. The owner, the operator, the maintainer. If you want to avoid chaos, you need party golden records.

The Latest Disruptive MDM Entry: Unidata

Gartner has restarted mentioning some of the Master Data Management (MDM) solutions not (yet) meeting the threshold criteria for the MDM Magic Quadrant as reported in the post What has Changed with the Gartner MDM Magic Quadrant?

One of the solutions in this tiny crowd is Unidata.

The sister site to this blog is The Disruptive MDM / PIM / DQM List. So very timely it is good to be able to present Unidata on this site as well – and providing some more information than Gartner do.

An interesting fact about Unidata is that the core of its data management platform is under an open license which is “part of the company’s strategy and mission to form a community of experts in the field of data management and is aimed at developing the industry as a whole”.

Learn more about Unidata here.Unidata image

Data Matching and Deduplication

The two terms data matching and deduplication are often used synonymously.

In the data quality world deduplication is used to describe a process where two or more data records, that describes the same real-world entity, are merged into one golden record. This can be executed in different ways as told in the post Three Master Data Survivorship Approaches.

Data matching can be seen as an overarching discipline to deduplication. Data matching is used to identify the duplicate candidates in deduplication. Data matching can also be used to identify matching data records between internal and external data sources as examined in the post Third-Party Data Enrichment in MDM and DQM.

As an end-user organization you can implement data matching / deduplication technology from either pure play Data Quality Management (DQM) solution providers or through data management suites and Master Data Management (MDM) solutions as reported in the post DQM Tools In and Around MDM Tools.

When matching internal data records against external sources one often used approach is utilizing the data matching capabilities at the third-party data provider. Such providers as Dun & Bradstreet (D&B), Experian and others offer this service in addition to offering the third-party data.

To close the circle, end-user organizations can use the external data matching result to improve the internal deduplication and more. One example is to apply a matched duns-numbers from D&B for company records as a strong deduplication candidate selection criterium. In addition, such data matching results may often result not in a deduplication, but in building hierarchies of master data.

Data Matching and Deduplication

 

The Latest Constellation Research MDM Shortlist

The new Constellation Research generic shortlist for Master Data Management (MDM) is out.

Compared to the previous list Semarchy is a new entry which follows up their up-right move in The Latest Gartner MDM Magic Quadrant. So, another acknowledgement of the Semarchy Intelligent Data Hub concept. It is good to see that someone I started to blog about 8 years ago is now going to the top of the market.

Else IBM, Informatica, Reltio, Riversand, Stibo Systems and Tibco EBX stays on the list.

Constellation Research has now realized, like Gartner also did some while ago, that Oracle has left the MDM market. Thus the Oracle expansion on the previous shortlist is now followed up by a goodbye.

Fun fact: The guys who started Semarchy left Oracle a decade ago with the aim to build a better MDM solution as told by FX Nicolas of Semarchy in this interview.

Check out the Constellation Shortlist(tm) Master Data Management here.

Constellation MDM Shortlist 2020 Q1
Source: Constellation Research

10 MDMish TLAs You Should Know

TLA stands for Three Letter Acronym. The world is full of TLAs. The IT world is indeed full of TLAs. The Data Management world is also full of TLAs. Here are 10 TLAs from the data management space that surrounds Master Data Management:

Def MDM

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?

The most addressed master data domains are parties encompassing customer, supplier and employee roles, things as products and assets as well as location.

Def PIM

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 – often called attributes – that is needed for a given classification of products.

Furthermore, PIM deals with how products are related as for example accessories, replacements and spare parts as well as the cross-sell and up-sell opportunities there are between products.

PIM also handles how products have digital assets attached.

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.

Def DAM

DAM: Digital Asset Management is about handling extended features of digital assets often related to master data and especially product information. The digital assets can be photos of people and places, product images, line drawings, certificates, brochures, videos and much more.

Within DAM you are able to apply tags to digital assets, you can convert between the various file formats and you can keep track of the different format variants – like sizes – of a digital asset.

You can learn more about how these first 3 mentioned TLAs are connected in the post How MDM, PIM and DAM Stick Together.

Def DQM

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.

The most used technologies in data quality management are data profiling, that measures what the data stored looks like, and data matching, that links data records that do have the same values, but describes the same real world entity.

Def RDM

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.

Examples of reference data are hierarchies of location references as countries, states/provinces and postal codes, different industry code systems and how they map and the many product classification systems to choose from.

Learn more in the post What is Reference Data Management (RDM)?

Def CDI

CDI: Customer Data Integration is considered as the predecessor to MDM, as the first MDMish solutions focused on federating customer master data handled in multiple applications across the IT landscape within an enterprise.

The most addressed sources with customer master data are CRM applications and ERP applications, however most enterprises have several of other applications where customer master data are captured.

You may ask: What Happened to CDI?

Def CDP

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.

In that way CDP goes far beyond customer master data by encompassing traditional transaction data related to customers and the emerging big data sources too.

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?

Def ADM

ADM: Application Data Management is about not just master data, but all critical data that is somehow shared between personel and departments. In that sense MDM covers all master within an organization and ADM covers all (critical) data in a given application and the intersection is looking at master data in a given application.

ADM is an emerging term and we still do not have a well-defined market – if there ever will be one – as examined in the post Who are the ADM Solution Providers?

Def PXM

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.

In PXM the focus is on personalization of product information, Search Ingine Optimization and exploiting Artificial Intelligence (AI) in those quests.

Read more about it in the post What is PxM?

Def PDS

PDS: Product Data Syndication 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)?

One example of a PDS service is the Product Data Lake solution I have been working with during the last couple of year. Learn why this PDS service is needed here.