B2B2C in Data Management

The Business-to-Business-to-Consumer (B2B2C) scenario is increasingly important 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 DQMIn 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.

TCO, ROI and Business Case for Your MDM / PIM / DQM Solution

Any implementation of a Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) solution will need a business case to tell if the intended solution has a positive business outcome.

Prior to the solution selection you will typically have:

  • Identified the vision and mission for the intended solution
  • Nailed the pain points the solution is going to solve
  • Framed the scope in terms of the organizational coverage and the data domain coverage
  • Gathered the high-level requirements for a possible solution
  • Estimated the financial results achieved if the solution removes the pain points within the scope and adhering to the requirements

The solution selection (jump-starting with the Disruptive MDM / PIM / DQM Select Your Solution service) will then inform you about the Total Cost of Ownership (TCO) of the best fit solution(s).

From here you can, put very simple, calculate the Return of Investment (ROI) by withdrawing the TCO from the estimated financial results.

MDM PIM DQM TCO ROI Business Case

You can check out more inspiration about ROI and other business case considerations on The Disruptive MDM / PIM /DQM Resource List.

Collaborative Product Data Syndication vs Data Pools and Marketplaces

The previous post on this blog was called Inbound and Outbound Product Data Syndication.

As touched in this post there are two kinds of Product Data Syndication (PDS):

  • The public kind where everyone shares the same product information. The prominent examples are marketplaces and data pools.
  • The collaborative kind where you can exchange the same product information with all your accepted trading partners but also supplement with one-to-one product information that allows the merchant to stand out from the crowd.

When you syndicate to marketplaces everyone can easily watch and get inspired. A creepy kind of inspiration is the one surfacing at the moment where Amazon is believed to copy product data in order to make a physical twin as examined in the Wall Street Journal article telling that Amazon Scooped Up Data From Its Own Sellers to Launch Competing Products.

When syndicating – or synchronizing – through data pools you are limited to the consensus on the range of data elements, structure and format enforced by those who control the data pool – which can be you and your competitors.

With a collaborative PDS solution you can get the best of two worlds. You can have the market standard that makes you not falling behind your competitors. However, you can also have unique content coming through that puts you ahead of your competitors.

Collaborative PDS Data pools and Marketplaces

Right now, I am working with a collaborative PDS solution. This solution welcomes other (collaborative) PDS solutions as part of the product information flow. The solution will also encompass data pools in a reservoir concept. This PDS solution is called Product Data Lake.

Inbound and Outbound Product Data Syndication

If you google for the term Product Data Syndication you will get the explanation in a post on the sister site to this blog. The Disruptive MDM / PIM / DQM list blog post is called What is Product Data Syndication (PDS).

Inbound and Outbound Scenarios

Digging further into this subject one can divide the Product Data Syndication (PDS) scenarios as seen from the individual organization within a supply chain into inbound and outbound product data syndication.

As a merchant/retailer/dealer being downstream in the supply chain you will have these main scenarios:

  • Outbound product data syndication to marketplaces. This is the scenario covered by most solutions that are marketed as PDS solutions. The challenge here is that there are hundreds of marketplaces both internationally and nationally. These marketplaces have each their way of getting the product information. The advantage of such a PDS solution is that you as a merchant only need one downstream feed to (in theory) all marketplaces.
  • Inbound product data syndication from suppliers either directly from the manufacturer or through distributors. There are many ways this is done today stretching exchanging spreadsheets, getting the product information in your supplier portal, fetching the product information from each of the manufacturers customer portal, through data pools and, still in the emerging stage, utilizing a collaborative PDS solution (see further down).
  • Outbound product data syndication to large end users often being manufacturers utilizing MRO (Maintenance, Repair and Operation) parts.

As a manufacturer/brand owner being upstream in the supply chain you will have these main scenarios:

  • Outbound product data syndication to marketplaces, which most often only covers a fraction of the revenue.
  • Outbound product data syndication of product information for finished products to distributors and/or merchants. There are many ways this is done today stretching exchanging spreadsheets, putting the product information in each of the distributors/merchant’s supplier portal, exhibiting the product information in your customer portal, through data pools and, still in the emerging stage, utilizing a collaborative PDS solution (see further down).
  • Inbound product data syndication of product information for raw materials and MRO (Maintenance, Repair and Operation) parts from suppliers being other manufacturers, distributors and/or merchants. There are many ways this is done today stretching exchanging spreadsheets, through data pools and, still in the emerging stage, utilizing a collaborative PDS solution (see further down).

As a distributor/wholesaler being midstream in the supply chain you share the outbound PDS scenarios at manufactures and the inbound PDS scenarios at merchants.

In some cases, a marketplace can act as a data pool too.

Collaborative PDS

A Collaborative PDS Solution

In my eyes a collaborative PDS solution have these capabilities:

  • Catering for a win-win scenario between trading partners by allowing one uniform way of outbound push of product information from upstream trading partners (manufacturers, distributors) and one uniform way of inbound pull of product information at downstream trading partners (distributors, merchants).
  • Ability to work with all in-house Product Information Management (PIM) solutions and/or other in-house applications where product information is managed both for outbound push and inbound pull.
  • Can encompass outbound push to and pull from data pools and even other PDS solutions as elements in the total product information flow embracing both market standard product information and flow of individual product information that makes the merchant stand out from the crowd.

Right now, I am working with a collaborative PDS solution. This solution welcomes other (collaboratve) PDS solutions as part of the product information flow. And of course, also every in-house Product Information Management (PIM) solution out there. This PDS solution is called Product Data Lake.

Get Your Free Bespoke MDM / PIM / DQM Solution Ranking

The Disruptive MDM / PIM / DQM List has an interactive service that can help you jumpstart in your tool selection for a Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) solution.

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.

MDM / PIM / DQM Resources

Last week The Resource List went live on The Disruptive MDM / PIM / DQM List.

The rationale behind the scaling up of this site is explained in the article Preaching Beyond the Choir in the MDM / PIM / DQM Space.

These months are in general a yearly peak for conferences, written content and webinars from solution and service providers in the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space. With the covid-19 crises conferences are postponed and therefore the content providers are now ramping up the online channel.

As one who would like to read, listen to and/or watch relevant content, it is hard to follow the stream of content being pushed from the individual providers every day.

The Resource List on this site is a compilation of white papers, ebooks, reports, podcasts, webinars and other content from potentially all the registered tool vendors and service providers on The Solution List and coming service list. The Resource List is divided into sections of topics. Here you can get a quick overview of the content available within the themes that matters to you right now.

The list of content and topics is growing.

Check out The Resource List here.

MDM PIM DQM resourcesPS: The next feature on the site is planned to be The Case Story List. Stay tuned.

How the Covid-19 Outbreak Can Change Data Management

From sitting at home these are my thoughts about how data management can be changed due to the current outbreak of the Covid-19 (Corona) virus and the longer-term behaviour impact after the pandemic hopefully will be over.

Ecommerce Will Grow Faster

Both households and organizations are buying more online and this trend is increasing due to the urge of keeping a distance between humans. The data management discipline that underpins well executed ecommerce is Product Information Management (PIM). We will see more organizations implementing PIM solutions and we must see more effective and less time-consuming ways of implementing PIM solutions.

Data Governance Should Mature Faster

The data governance discipline has until now been quite immature and data governance activities have been characterized by an endless row of offline meetings. As data governance is an imperative in PIM and any other data management quest, we must shape data governance frameworks that are more ready to use, and we must have online learning resources available for both professionals and participating knowledge workers with various roles.

Data Sharing Could Develop Faster

People, organizations and countries initially act in a selfish manner during a crisis, but we must realize that collaboration including data sharing is the only way forward. Hopefully we will see more widespread data sharing enterprise wide as this will ease remote working. Also, we could see increasing interenterprise (business ecosystem wide) data sharing which in particular will ease PIM implementations through automated Product Data Syndication (PDS).

Covid Data Management

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 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 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 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. 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.

It Is Black Friday and Cyber Monday All the Time at the Disruptive MDM / PIM / DQM List

The upcoming Black Friday and Cyber Monday are synonymous with good deals.

At the Disruptive MDM / PIM / DQM List there are good deals all the days.

As a potential buyer on the look for a solution covering your Master Data Management (MDM), Product Information Management (PIM) and/or Data Quality Management (DQM) needs you can use the free service that based on your context, scope and requirement selects the best fit solution(s). You can start here.

Black Friday

As a solution provider you can against a very modest fee register your solution here.

Happy Black Friday and Cyber Monday.

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)?