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.

A Tricky Thing with Data Quality Evangelism

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:

Experian 2020 Data Survey
Source: Experian

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.

Well, see for yourself. Get the 2020 Global data management research from Experian Data Quality here.

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

Take Part in State of Data 2020

KDR Recruitment is a data management recruitment company and one of those rare recruitment agencies that genuinely express an interest in the disciplines covered.

This is manifested in among other things a yearly survey and report about the state of data that also was touched on this blog five years ago in the post Integration Matters.

This year the surveyed topics include for example how to use data analysis, new skills needed and the most effective ways to improve data quality. You can participate with your experience and observations here at State of Data 2020.

KDR state of data 2020

The Two Data Quality Definitions

If you search on Google for “data quality” you will find the ever-recurring discussion on how we can define data quality.

This is also true for the top ranked none sponsored articles as the Wikipedia page on data quality and an article from Profisee called Data Quality – What, Why, How, 10 Best Practices & More!

The two predominant definitions are that data is of high quality if the data:

  • Is fit for the intended purpose of use.
  • Correctly represent the real-world construct that the data describes.

Personally, I think it is a balance.

Data Quality Definition

In theory I am on the right side. This is probably because I most often work with master data, where the same data have multiple purposes.

However, as a consultant helping organizations with getting the funding in place and getting the data quality improvement done within time and budget I do end up on the other side.

What about you? Where do you stand in this question?

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)?

Movements in the Constellation Research MDM Shortlist

One of the not so often mentioned analyst MDM market reports is the Constellation Shortlist™ Master Data Management.

The Q3 2018 version was mentioned here on the blog in the post Making Your MDM Vendor Longlist and Shortlist.

The Q3 2019 version has these changes compared to the shortlist a year ago:

  • Orchestra Networks is renamed to Tibco EBX
  • Oracle CDM Cloud is joined by Oracle Product MDM
  • Stibo Systems is a new entry

Constellation MDM ShortlistTwo observations:

  • At analyst firms Gartner and Forrester, Oracle is not considered as a (major) MDM market player anymore.
  • SAP MDG is the only megavendor solution not reaching this generic shortlist

PS: If you need a shortlist tailored to your context, scope and requirements, you can get it on The Disruptive MDM list here.

When Vendors Decline to Participate in Analyst Research

In the Master Data Management (MDM) and Product Information Management (PIM) space there are some analyst market reports with vendor rankings used by organizations when doing a tool selection project.

These reports are based on the analyst’s survey at their customers and perhaps other end user organizations as well as the analysts research in corporation with the solution vendor. However, sometimes the latter part does not happen.

One example was the Gartner late 2017 Magic Quadrant for MDM solutions where IBM declined to participate as reported in the post Why IBM Declined to Participate in The Gartner MDM Magic Quadrant.

Another example is the Forrester and Informatica dysfunctional relationship. In the Forrester 2019 MDM Wave it is stated that “Informatica declined to participate in our research. This was also apparent in the Forrester 2018 PIM Wave where Forrester’s placement of Informatica as a Germany-based vendor didn’t reflect movements (and perhaps achievements) since 2012 as told in the post MDM Alternative Facts.

Both Gartner and Forrester have though positioned IBM and Informatica in their plot with the note that the research did not include interaction with the vendor.

Analyst Relationship

Information Difference has taken another approach and does not include nonparticipating vendors as discussed in the post Movements in the MDM Vendor Landscape 2019.

This challenge is a bit close to me as I am running a list of MDM / PIM / DQM vendors where there now also is a ranking service based on individual context, scope and requirements. Here I have chosen to include vendor solutions on the three above analyst reports and the list itself as noted in select your solution step 4.