MDM Use Case Status

As reported in the post Counting MDM Licenses there is movement in the MDM landscape when it comes to the offerings for the various use cases we have been working with the last 15 years and those we will be working with in the future.

Borrowing from the Gartner lingo, we can sketch the MDM use case overview this way:

  • Party MDM, meaning handling master data about persons and companies interacting with your company. Their role may be as employee, partner, supplier/vendor and customer. With the customer role we can make a distinction between:
    • MDM of B2C (Business-to-Consumer) customer data, meaning handling master data about persons in their private roles as consumers, citizens, patients, students and more. This may also cover how persons are part of a household.
    • MDM of B2B (Business-to-Business) customer data, meaning handling master data about organizations with a customer role in your company. This may also cover the hierarchy these organizations form (typically company family trees) and the persons who are your contacts at these organizations.
  • Product MDM, meaning handling data about product models and their item variants as well as each instance of a product as an asset. This can be divided into:
    • MDM of buy-side product data covering the procurement and Supply Chain Management (SCM) view of products going into your company from suppliers.
    • MDM of sell-side product data covering the sales and marketing view of products being sold directly to customers or through partners.
  • Multidomain MDM being combining product and party master data possibly with other domains as locations, general ledger accounts and specific master data domains in your industry.
  • Multivector MDM being a special Gartner term meaning use case split into multiple domains (as mentioned above), multiple industries, operational/analytical usage scenarios, organizational structures and implementation styles (registry, consolidation, coexistence, centralized).
  • Multienterprise MDM being handling master data in collaboration with your business partners as told in this post about Multienterprise MDM.

In the latest Gartner MDM quadrant, the status of the use cases is:

  • Customer MDM and Product MDM continue to climb the Slope of Enlightenment toward the Plateau of Productivity in Gartner’s Hype Cycle for Information Governance and Master Data Management.
  • Multidomain MDM solutions are sliding toward the bottom of the Trough of Disillusionment, while Multivector MDM solutions continue their climb toward the Peak of Inflated Expectations in the Hype Cycle.
  • Multienterprise MDM is near the Peak of Inflated Expectations on the Hype Cycle as well as mentioned in the post MDM Hype Cycle, GDSN, Data Quality, Multienterprise MDM and Product Data Syndication. In the Gartner MDM quadrant 2018 multienterprise MDM was only mentioned as a strength at one of the included vendors – Enterworks.

Stay tuned on this blog for more news about Multienterprise MDM as how it looks like from standing on the peak.

mdm hype cycle
Based on Gartner sources

1,000 Blog Posts and More to Come

number_1000I just realized that this post will be number 1,000 published on this blog. So, let me not say something new but just recap a little bit on what it has been all about in the last nearly 10 years of running a blog on some nerdy stuff.

Data quality has been the main theme. When writing about data quality one will not avoid touching Master Data Management (MDM). In fact, the most applied category used here on this site, with 464 and counting entries, is Master Data.

The second most applied category on this blog is, with 219 entries, Data Architecture.

The most applied data quality activity around is data matching. As this is also where I started my data quality venture, there has been 192 posts about Data Matching.

The newest category relates to Product Information Management (PIM) and is, with 20 posts at the moment, about Product Data Syndication.

Even though that data quality is a serious subject, you must not forget to have fun. 66 posts, including a yearly April Fools post, has been categorized as Supposed to be a Joke.

Thanks to all who are reading this blog and not least to all who from time to time takes time to make a comment, like and share.

Supply Chain Participants and PIM Requirements

A recent post on this blog was called B2C vs B2B in Product Information Management. This post was my take on the differences, if any, between doing Product Information Management (PIM) in a Business-to-Consumer (B2C) scenario versus in a Business-to-Business (B2B) scenario.

In a comment in the Product Information Management Professional Association group on LinkedIn Hans de Gier of SyncForce replied:

“For many of our B2B customers information plays a bigger role in the Market-to-Order process than for consumer products. But most of our customers (Consumer & Professional Packaged Goods Manufacturers) serve both retail and professional/wholesale channels, which have different information needs, even regarding the same products. So, any manufacturer targeted solution should be able to serve both channels with the right content via the right data channels. In our vision a more relevant question is: What is your take on the differences on doing PIM in Manufacturing versus Wholesale / Retail.”

Indeed, there are several ways to slice the PIM space and the supply chain position of a company as a supply chain delegate is for sure very relevant. Exchanging product information between trading partners in upstream and downstream (and midstream) positions must be very flexible and one size fits all thinking will not work.

The different positions of a company as they are in my mind is illustrated below:

supply chain

The possible combinations when exchanging product information between supply chain delegates are plentiful. To mention a few channels:

  • Manufacturer to wholesaler to retailer to end private consumer
  • Manufacturer to distributor to dealer to end business customer
  • Manufacturer to distributor to dealer to manufacturer as raw material
  • Manufacturer to merchant to marketplace to end customer
  • Manufacturer to marketplace to end customer
  • Manufacturer to/from brand owner to any midstream/downstream delegate

This variety is why the means of exchanging product information (product data syndication) between trading partners is essential in almost any PIM solution.

At Product Data Lake we offer the remedy to this challenge and in combination with any PIM solution or other application where in-house product information is managed.

Counting MDM Licenses

The Gartner Magic Quadrant for Master Data Management (MDM) Solutions 2018 was published last month.

Some of the numbers in the market that were revealed in the report was the number and distribution of MDM licenses from the included vendors. These covered their top-three master data domains and estimated license counts as well as the number of customers managing multiple domains:

mdm licenses

One should of course be aware of the data quality issues related to comparing these numbers, as they in some degree are estimates based on different perceptions at the included vendors. So, let me just highlight these observations:

  • The overall number of MDM licenses and unique MDM customers (at the included vendors) is not high. Under 10,000 organizations world-wide is running such a solution. The potential new market out there for the salesforce at the MDM vendors is huge.
  • If you find an existing MDM solution user organization, they probably have a solution from SAP or Informatica – or maybe IBM. To be complete, Oracle has been dropped from the MDM quadrant, they practically do not promote their MDM solutions anymore, but there are still existing solutions operating out there.
  • The reign of Customer MDM is over. Product MDM is selling and multidomain is becoming the norm. Several MDM vendors are making their way into the quadrant from a Product Information Management (PIM) base as reported in the post The Road from PIM to Multidomain MDM.

PS: If you, as an end customer organization or a MDM and PIM vendor, want to work with me on the consequences for MDM solutions, here are some Popular Offerings for you.

Governing Product Information

The title of this blog post is also the title of a presentation I will do at the 2019 Data Governance and Information Quality Conference in San Diego, US in June.

There is a little difference between how we can exercise data governance and information quality management when we are handling data about products versus handling the most common data domain being party data (customer, vendor/supplier, employee and other roles).

Multi-Domain MDM and Data Quality DimensionsThis topic was touched here on the blog in the post called Data Quality for the Product Domain vs the Party Domain.

The conference session will go through these topics:

  • Product master data vs. product information
  • How Master Data Management (MDM), Product Information Management (PIM) and Digital Asset Management (DAM) stick together
  • The roles of 1st party data, 2nd party data and 3rd party data in MDM, PIM and DAM
  • Business ecosystem wide product data management
  • Cross company data governance and information quality alignment

You can have a look at the full agenda for the DGIQ 2019 Conference here.

dgiq 2019

The relation between CX and MDM

The title of this blog post is also the title of a webinar I will be presenting on the 28th February 2019. The webinar is hosted by the visionary Multidomain MDM and PIM solution provider Riversand.

Customer experience (CX) and Master Data Management (MDM) must go hand in hand. Both themes involve multiple business units and digital environments within your enterprise and in the wider business ecosystem, where your enterprise operates. Master data is the glue that brings the data you hold about your customers together as well as the glue that combines the data you share about your product offering together.

To be successful within customer experience in the digital era you need classic master data outcomes as a 360-degree view of customers as well as complete and consistent product information. In other words, you need to maintain Golden Records in Multidomain MDM.

You also need to combine your customer data and your product data to get to the right level of personalization. Knowing about your customer, what he/she wants, and their buying behaviour is one side personalization. The other side is being able to match these data with relevant products that is described to a level that can provide reasonable logic against the behavioural data.

Furthermore, you need to be able to make sense of internal and external big data sources and relate those to your prospective and existing customers and the products they have an interest in. This quest stretches the boundaries of traditional MDM towards being a more generic data platform.

You can register to join the webinar here.

webinar data lake

Get Your Hands Dirty with Data

When working with data management – and not at least listening to and reading stuff about data management – there is in my experience too little work with the actual data going around out there.

I know this from my own work. Most often presentations, studies and other decision support in the data management realm is based on random anecdotes about the data rather than looking at the data. And don’t get me wrong. I know that data must be seen as information in context, that the processes around data is crucial, that the people working with data is key to achieving better data quality and much more cleverness not about the data as is.

data management wordsBut time and again I always realize that you get the best understanding about the data when getting your hands dirty with working with the data from various organizations. For me that have been when doing a deduplication of party master data, when calibrating a data matching engine for party master data against third party reference data, when grouping and linking product information held by trading partners, when relating other master data to location reference data and all these activities we do in order to raise data quality and get a grip on Master Data Management (MDM) and Product Information Management (PIM).

Well, perhaps it is just me and because I never liked real dirt and gardening.

B2C vs B2B in Product Information Management

The difference between doing Business-to-Consumer (B2C) or Business-to-Business (B2B) reflects itself in many IT enabled disciplines.

Yin and yangWhen it comes to Product Information Management (PIM) this is true as well. As PIM has become essential with the rise of eCommerce, some of the differences are inherited from the eCommerce discipline. There is a discussion on this in a post on the Shopify blog by Ross Simmonds. The post is called B2B vs B2C Ecommerce: What’s The Difference?

Some significant observations to go into the PIM realm is that for B2B, compared to B2C:

  • The audience is (on average) narrower
  • The price is (on average) higher
  • The decision process is (on average) more thoughtful

How these circumstances affect the difference for PIM was exemplified here on the blog in the post Work Clothes versus Fashion: A Product Information Perspective.

To sum up the differences I would say that some of the technology you need, for example PIM solutions, is basically the same but the data to go into these solutions must be more elaborate and stringent for B2B. This means that for B2B, compared to B2C, you (on average) need:

  • More complete and more consistent attributes (specifications, features, properties) for each product and these should be more tailored to each product group.
  • More complete and consistent product relations (accessories, replacements, spare parts) for each product.
  • More complete and consistent digital assets (images, line drawings, certificates) for each product.

How to achieve that involves deep collaboration in the supply chains of manufacturers, distributors and merchants. The solutions for that was examined in the post The Long Tail of Product Data Synchronization.

The Road Ahead for MDM

Even though that Master Data Management (MDM) has been around as a discipline for about 15 years now, there is still a lot of road to be covered for many organizations and for the discipline as a whole.

vestre kirkegaardSome of the topics I find to be the most promising visit points on this journey are cloud deployment of MDM solutions, inclusion of Artificial Intelligence (AI) in MDM and multienterprise MDM.

Cloud deployment of MDM has increased slowly but steadily over the recent years. Quite naturally the implementation of MDM in the cloud will follow the general adoption of cloud solutions deployed in each organization as master data is the glue between the data held in each application. Doing MDM in the cloud or not is, as with most things in life, not a simple question with a yes or no answer, as there are different deployment styles as examined in the post MDM, Cloud, SaaS, PaaS, IaaS and DaaS.

Inclusion of Artificial Intelligence (AI) and Machine Learning (ML) in the MDM discipline will, in my eyes, be one of the hottest topics in the years to come. MDM is not the easiest IT enabled discipline in which AI and ML can be applied. Handling master data has many manual processes today because it is highly interactive, and the needed day-to-day decisions requires much knowledge input. But we will get there step by step and we must start now as told in the post It is time to apply AI to MDM and PIM.

Multienterprise MDM is emerging as a necessity following the rise of digitalization. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners will be a part of digitalization and thus, we will have a need for working on the same foundation around master data. This theme was pondered in the post Share or be left out of business.

Flying by Ultima Thule and Data Management

Ultima Thule is a name for a distant place beyond the known world and the nickname of the most distant object in the solar system closely observed by a man-made object today the 1st January 2019. Before the flyby scientists were unsure if it was two objects, a peanut formed object or another shape. The images probing what it is will be downloaded during the next couple of months.

You can make many analogies between exploring space and data management. On this blog the journey has passed the similarity between Neutron Star Collision and Data Quality. The Gravitational Waves in the MDM World has been observed and so has the Gravitational Collapse in the PIM Space. The notion of A Product Information Management (PIM) Solar System has also been suggested.

Happy New Year and wishing you all well in the data management journey beyond Ultima Thule.

Ultima Thule
Source: Nasa via BBC