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.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.
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.
The report also measures how happy the end customers are with the vendors: “The happiest customers based on this survey were those of Datactics, followed by those of Syncsort and Active Prime, closely followed by those of Innovative Systems and Melissa Data, then Experian. Congratulations to those vendors.”
Also, this time it strikes again that the mega vendors (IBM, SAP, Informatica) are not in this crowd.
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 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.
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.
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.
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.
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.
The 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.
The 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
These 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.
The 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.
One of the major players on the data quality market, Experian, do a yearly survey of the current data management trends. This year is no exception and I just had the chance to read through the 2020 report.
This year’s report revolves around trusted data, data debt and the skills gap in the light of data literacy. As always, the report holds some good percentage take away you can use in your data quality evangelism.
My favourite this year is a bit tricky:
I think this one shows a challenging side of data quality evangelism. While operational efficiency is a bit ahead of other reasons to improve data quality, there are many good reasons to improve data quality. And advocating for every kind of goodness is often harder than being able to pinpoint one absolutely good reason.
A recent Gartner report states that: “Organizations that fail to understand their use cases, desired business outcomes and customer data governance requirements have difficulty choosing between CDPs and MDM solutions, because of overlapping capabilities.”
Gartner compare the two breeds of solutions this way:
CDPs are marketing-managed tools designed for the creation, segmentation and activation of customer profiles. … These platforms have less governance functionality than MDM solutions and tend to focus on delivering a complete view through the amalgamation of data generated by digital customer interactions.
MDM solutions are more mature technology that also enable customer 360 insights by creating and managing a central, persisted system or index of record for master customer records. They enable governance and management of the core data that uniquely identifies one customer as distinct from another. They were built to support enterprisewide sources and applications of customer data.
CDP platforms (via CRM applications) seems to hit from outside in without getting to the core of customer centrecity. MDM solutions are hitting the bullseye and some of the MDM solutions are moving inside out in the direction of extended MDM, where all customer data, not just customer master data, is encompassed under the same data governance umbrella.
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.
Multi-domain Master Data Management is usually perceived as the union of Customer MDM, Supplier MDM and Product MDM. It is. And it is much more than that.
Customer MDM is typically about federating the accounts receivable in the ERP system(s) and the direct and prospective accounts in the CRM system(s). Golden records are formed through deduplication of multiple representations of the same real-world entity.
Supplier (or vendor) MDM is typically about federating the accounts payable in the ERP system(s) and the existing and prospective accounts in the SRM system(s). A main focus is on the golden records and the company family tree they are in.
Product MDM has a buy-side and a sell-side.
On the buy-side MDM is taking care of trading data for products to resell, in manufacturing environments also the trading data for raw materials and in some cases also for parts to be used in Maintenance, Repair and Operation (MRO). The additional long tail of product specifications may in resell scenarios be onboarded in an embedded/supplementary Product Information Management (PIM) solution.
On the sell-side the trading data are handled for resell products and in manufacturing environments the finished products. The additional long tail of product specifications may be handled in an embedded/supplementary Product Information Management (PIM) solution.
Multidomain MDM does this in a single solution / suite of solutions. And much more as for example:
Supplier contacts can be handled in a generic party master data structure.
Customer contacts can be handled in a generic party master data structure
Besides the direct accounts in CRM the indirect accounts and contacts can in the party master data structure too. Examples of such parties are:
Influencers in the form of heath care professionals in life science.
Influencers in the form of architects and other construction professionals in building material manufacturing.
End consumers in many supply chain B2B2C scenarios.
Employee records can be handled in a generic party master data structure. The roles of sales representatives and their relation to customers, influencers, product hierarchies and location hierarchies can be handled as well as purchase responsibles and their relation to suppliers, influencers, product hierarchies and location hierarchies can be handled.
The relation between suppliers and product hierarchies and location hierarchies cand be handled.
The relation between customers and end consumers and the product hierarchies and location hierarchies can be handled.
Inbound product information feeds from suppliers can be organized and optimized through Product Data Syndication (PDS) solutions.