Tag Archives: PIM

Environmental Data Management

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As examined in the post The Intersection Between MDM, PIM and ESG, environmental data management is becoming an important aspect of the offerings provided in solutions for Master Data Management (MDM) and Product Information Management (PIM). Consequentially, this will also apply to the Data Quality Management (DQM) capabilities that are either offered as part of these solutions or as standalone solutions for data quality.

This site has an interactive Select Your Solution service for potential buyers of MDM / PIM / DQM solutions. The service has a questionnaire and an undelaying model for creating a longlist, shortlist or direct PoC suggestion for the best candidate(s) according to the context, scope, and requirements of an intended solution.

In line with the rise of the Environmental, Social and Governance (ESG) theme, the questionnaire and the underlying selection model must in the first place be enhanced with environmental data management aspects, as the environmental part of ESG is the one with the currently most frequently and comprehensive experienced data management challenges.

There is already established a good basis for this.

However, if you as either one from a solution end user organization with environmental data management challenges or a solution provider have input to environmental data management aspects to be covered, you are more than welcome to make a comment here on the blog or use the below contact form:

Nine Essential Master Data Processes in The Data Supply Chain

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Master Data Management (MDM) and the overlapping Product Information Management (PIM) discipline is the centre of which the end-to-end data supply chain revolves around in enterprises that produce and/or sell goods.

Nine essential master data processes are:

1: Onboard Customer Data

It starts and ends with the King: The Customer. Your organization will probably have several touchpoints where customer data is captured. MDM was born out of the Customer Data Integration (CDI) discipline and a main reason of being for MDM is still to be a place where all customer data is gathered as exemplified in the post Direct Customers vs Indirect Customers.

2: Onboard Vendor Data

Every organization has vendors/suppliers who delivers direct and indirect products as office supplies, Maintenance, Repair and Operation (MRO) parts, raw materials, packing materials, resell products and services as well. As told in a post on this blog, you have to Know Your Supplier.

3: Enrich Party Data

There are good options for not having to collect all data about your customers and vendors yourself, as there are 3rd party sources available for enriching these data preferable as close to capture as possible. This topic was examined in the post Third-Party Data and MDM.

4: Onboard Product Data

While a small portion of product data for a small portion of product groups can be obtained via product data pools, the predominant way is to have product data coming in as second party data from each vendor/supplier. This process is elaborated in the post 4 Supplier Product Data Onboarding Scenarios.

5: Transform Product Data

As your organization probably do not use the same standard, taxonomy, and structure for product data as all your suppliers, you have to transform the data into your standard, taxonomy, and structure. You may do the onboarding and transformation in one go as pondered in the post The Role of Product Data Syndication in Interenterprise MDM.

6: Consolidate Product Data

If your organization produce products or you combine external and internal products and services in other ways you must consolidate the data describing your finished products and services.

7: Enrich Product Data

Besides the hard facts about the products and services you sell you must also apply competitive descriptions of the products and services that makes you stand out from the crowd and ensure that the customer will buy from you when looking for products and services for a given purpose of use.

8: Customize Product Data

Product data will optimally have to be tailored for a given geography, market and/or channel. This includes language and culture considerations and adhering to relevant regulations.

9: Personalize Product Data

Personalization is one step deeper than market and channel customization. Here you at point-of-sale seek to deliver the right Customer Experience (CX) by exercising Product eXperience Management (PXM). Here you combine customer data and product data. This quest was touched in the post What is Contextual MDM?

Digital Twins in the MDM Product Domain / PIM

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When working with the product domain in Master Data Management (MDM) and with Product Information Management (PIM) we have traditionally been working with the product model meaning that we manage data about how a product that can be produced many times in exactly the same way and resulting in having exactly the same features. In other words, we are creating a digital twin of the product model.

As told in the post Spectre vs James Bond and the Unique Product Identifier the next level in product data management is working with each product instance meaning each produced thing that have a set of data attached that is unique to that thing. Such data can be:

  • Serial number or other identification as for example the Unique Device Identification (UDI) known in healthcare
  • Manufacturing date and time
  • Specific configuration
  • Current and historical position
  • Current and historical owner
  • Current and historical installer, maintainer and other caretaker
  • Produced sensor data if it is a smart device / machine

There is a substantial business potential in being better than your competitor in managing product instances. This boils down to that data is power – if you use the data.

When managing this data, we are building a digital twin of the product instance.

Maintaining that digital twin is a collaborative effort involving the manufacturer, the logistic service provider, the owner, the caretaker, and other roles. For that you need some degree of Interenterprise MDM.

Product Information Elements in Customization and Personalization

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When working with Product Information Management (PIM) you can divide the different kinds of information to be managed into some levels and groups as elaborated in the post 5 Product Data Levels to Consider.

The 10 groups of product information in this 5-level scheme are all relevant for customization and personalization of product information in the following way:

  1. A (prospective) customer may have some preferred brands which are recognized either by collection of preferences or identified through previous behaviour.
  2. The shopping context may dictate that some product codes like GTIN/UPC/EAN and industry specific product codes are relevant as part of the product presentation or if these codes will only be noise.
  3. The shopping context may guide the use of variant product descriptions as touched in the post What’s in a Product Name?
  4. The shopping context may guide the use of various product image styles.
  5. The shopping context may guide the range of product features (attributes) to be presented typically either on a primary product presentation screen and on a detailed specification screen.
  6. The shopping context and occasion may decide the additional product description assets (as certificates, line drawings, installation guides and more) to be presented.
  7. The shopping occasion may decide the product story to be told.
  8. The shopping occasion may decide the supplementary products as accessories and spare parts to be presented along with the product in focus.
  9. The shopping occasion may decide the complementary products as x-sell and up-sell candidates to be presented along with the product in focus.
  10. The shopping occasion may decide the advanced digital assets as brochures and videos to be presented.   

Personalization of product information is a component of a Product eXperience Management (PxM) solution. You can learn more about this discipline in the post What is PxM?

What is MDM? – and the Adjacent Disciplines?

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This site is list of solutions for MDM and the disciplines adjacent to MDM. As always, it is good to have a definition of what we are talking about. So, here are some definitions of MDM and an Introduction to 9 adjacent disciplines:

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 not 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 positioning PIM solutions away from the MDM flavour and more towards digital experience / customer experience themes.

In PXM the focus is on personalization of product information, Search Engine 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. Product data syndication is often the first wave of encompassing interenterprise data sharing. You can get the details in the post What is Product Data Syndication (PDS)?