Popular Entries on The Resource List

This site has a list of white papers, ebooks, reports and webinars from solution and service providers.

The aim is to give inspiration for organizations having the quest to implement or upgrade their Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) capability.

The list has now been online in a month and it is time to look at which entries that until now have been the most popular in terms of click through. These are:

ROI of MDM, PIM and DQM

Exploring The ROI of PIM and MDMHave you ever wondered how to effectively evaluate the return on investment (ROI) of a Product Information Management (PIM) and Master Data Management (MDM) implementation? Then, take a look at some real-life examples. Download the Enterworks ebook on Exploring The ROI of PIM and MDM.

MDM, PIM and DQM market overview

The State of Product Information Management 2020Get an overview of why PIM solutions are implemented in more and more organizations, which capabilities a 2020 PIM solution needs to cover, where the market is heading and who the PIM vendors in the market are and how this affect your purchase of PIM. Download the Dynamicweb PIM white paper The State of Product Information Management 2020. 

MDM, PIM and DQM implementation

virtual-conference-webcast-revConferences cancelled? Stuck working from home? Bring the conferences to you with an virtual MDM conference. Don’t miss this must see 6 week live webcast series and hear what other companies are doing in the world of MDM along with best practices and workshops by industry experts.. Register for this Enterworks webcast series at the Everything Master Data Management (MDM) Virtual Conference.

Extended MDM

Intelligent Data Hub - Taking MDM to the Next LevelMDM solutions have been instrumental in solving core data quality issues in a traditional way, focusing primarily on simple master data entities such as customer or product. Organizations now face new challenges with broader and deeper data requirements to succeed in their digital transformation. Help your organization through a successful digital transformation while taking your MDM initiative to the next level. Download the Semarchy white paper Intelligent Data Hub – Taking MDM to the Next Level.

Data Quality

4 Keys to Unlocking Data Quality with MDMBusinesses today face a rapidly growing mountain of content and data. Mastering this content can unlock a whole new level of Business Intelligence for your organization and impact a range of data analytics. It’s also crucial for operational excellence and digital transformation. Download the 1WorldSync and Enterworks ebook on 4 Keys to Unlocking Data Quality with MDM.

Next To Come

More resources from solution and service vendors are on the way. Additionally, there will also be a Case Story List with success stories from various industries. Stay tuned.

If you have comments, suggestions and/or entries to be posted (yes, there is a very modest fee), then get in touch here:

 

B2B2C in MDM, PIM and DQM

The Business-to-Business-to-Consumer (B2B2C) scenario is becoming of increasing importance in Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM).

This scenario is usually seen in manufacturing including pharmaceuticals as examined in the post Six MDMographic Stereotypes.

One challenge here is how to extend the capabilities in MDM / PIM / DQM solutions that are build for Business-to-Business (B2B) and Business-to-Consumer (B2C) use cases. Doing B2B2C requires a Multidomain MDM approach with solid PIM and DQM elements either as one solution, a suite of solutions or as a wisely assembled set of best-of-breed solutions.

B2B2C MDM PIM DQM

In the MDM sphere a key challenge with B2B2C is that you probably must encompass more surrounding applications and ensure a 360-degree view of party, location and product entities as they have varying roles with varying purposes at varying times tracked by these applications. You will also need to cover a broader range of data types that goes beyond what is traditionally seen as master data.

In DQM you need data matching capabilities that can identify and compare both real-world persons, organizations and the grey zone of persons in professional roles. You need DQM of a deep hierarchy of location data and you need to profile product data completeness for both professional use cases and consumer use cases.

In PIM the content must be suitable for both the professional audience and the end consumers. The issues in achieving this stretch over having a flexible in-house PIM solution and a comprehensive outbound Product Data Syndication (PDS) setup.

As the middle B in B2B2C supply chains you must have a strategic partnership with your suppliers/vendors with a comprehensive inbound Product Data Syndication (PDS) setup and increasingly also a framework for sharing customer master data taking into account the privacy and confidentiality aspects of this.

This emerging MDM / PIM / DQM scope is also referred to as Multienterprise MDM.

The Free Bespoke MDM / PIM / DQM Solution Ranking Service

This site has an interactive service to help you jumpstart in your tool selection for a solution for Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM).

MDM PIM DQM Context, Scope and RequirementsThe selection model is based on the context, scope and requirements for your solution.

The context includes the geographical reach and the industry where your organization operates.

The scope includes the number of entities as for example consumers (B2C customers), companies (B2B customers, suppliers and other business partners), products and digital assets as well as the organizational reach.

The requirements are those that differentiate the MDM / PIM / DQM solutions on the market.

MDM PIM DQM Vendor capabilitiesThe solution capabilities considered in the selection process are those of who are:

  • On this Disruptive MDM / PIM / DQM Solutions List or
  • Gartner MDM Magic Quadrant or
  • Forrester MDM Wave or Forrester PIM Wave or
  • Information Difference MDM Landscape

MDM PIM DQM AI EngineThese two sets of information are compared in a continuously supervised learning algorithm – also known in marketing as machine learning and artificial intelligence (AI).

Filling in the information usually takes less than 15 minutes. You will get your solution list within 1 to 48 hours.

MDM PIM DQM Ranking OutcomeThe outcome is:

  • The best fit solution for a Proof of Concept
  • Two more solutions to be in a shortlist
  • Four more solutions to be in a longlist
  • If fit, a couple of more solutions to be considered as alternatives or supplements

During the half year this service has been online, more than 100 end user organizations or their consultants have received their solution list.

This service is free. No information is shared with anyone unless requested. Are you ready? Start with step 1 here.

An MDM / PIM / DQM Easter Egg

It is high season for painting Easter eggs now.MDM PIM DQM Easter EggThis egg is featuring:

  • Master Data Management (MDM),
  • Product Information Management (PIM) and/or
  • Data Quality Management (DQM)

as well as:

  • Application Data Management (ADM),
  • Customer Data Integration (CDI),
  • Customer Data Platform (CDP),
  • Digital Asset Management (DAM),
  • Product Data Syndication (PDS),
  • Product experience Management (PXM) and
  • Reference Data Management (RDM)

Check out the 10 data management TLAs on this list here.

What’s in a Product Name?

Within Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) one of the critical data elements that needs to be governed is the product name (product description).

Usually the product name is held in two forms:

  • A short form as it appears internally in an organization and is held in an ERP application. Everyone working with SAP knows about the challenge of keeping the product description in SAP within 40 characters. The short form is typically governed within the MDM discipline and is kept in the single one official language in the organization.
  • A long and customer friendly form as it is used when presenting the product in sales channels as for example on a web shop. The long form is typically governed within the PIM discipline and is kept in all languages in use in the organization.

The data quality dimensions that applies to the product name are namely:

  • Uniqueness, meaning that:
    • The product name must be the same (in short or long language form) across data stores in order to avoid duplicates.
    • Each product most have a distinct name, so you are able to distinguish one from the other.
  • Consistency, meaning that you must build the product name by sub elements that are sequenced the same way and have a common vocabulary.

The most common sub elements in a product name are:

  • Brand name or product line
  • Model number
  • Kind of product
  • Size
  • Color
  • Product classification (group) specific characteristics
  • Country

What is in a product name

It is a challenge to implement data governance around product names within a single organization. Even more it is a challenge to rely on / be depended on your trading partners when governing product names in business ecosystems as told in the post Toilet Seats and Data Quality.