Artificial Intelligence (AI) and Multienterprise MDM

The previous post on this blog was called Machine Learning, Artificial Intelligence and Data Quality. In here the it was examined how Artificial Intelligence (AI) is impacted by data quality and how data quality can impact AI.

Master Data Management (MDM) will play a crucial role in sustaining the needed data quality for AI and with the rise of digital transformation encompassing business ecosystems we will also see an increasing need for ecosystem wide MDM – also called multienterprise MDM.

Right now, I am working with a service called Product Data Lake where we strive to utilize AI including using Machine Learning (ML) to understand and map data standards and exchange formats used within product information exchange between trading partners.

The challenge in this area is that we have many different classification systems in play as told in the post Five Product Classification Standards. Besides the industry and cross sector standards we still have many homegrown standards as well.

Some of these standards (as eClass and ETIM) also covers standards for the attributes needed for a given product classification, but still, we have plenty of homegrown standards (at no standards) for attribute requirements as well.

Add to that the different preferences for exchange methods and we got a chaotic system where human intervention makes Sisyphus look like a lucky man. Therefore, we have great expectations about introducing machine learning and artificial intelligence in this space.

AI ML PDL

Next week, I will elaborate on the multienterprise MDM and artificial theme on the Master Data Management Summit Europe in London.

Toward the Third Generation of MDM

The Forrester Wave™: Master Data Management, Q1 2019 is out. The subtitle of the report is “Toward the Third Generation of Master Data Management.”

This resonates very well with my view as for example expressed is the post Three Stages of MDM Maturity.

The Forrester Report has this saying on that theme: “The internet of things has led to systems of automation and systems of design, which introduce new MDM usage scenarios to support co-design and the exchange of information on customers, products, and assets within ecosystems”.

Else, the report of course ranks the best selling MDM solutions as seen below:

Forrester MDM Wave 2019

You can get a free copy of the report from Riversand here or from Reltio here.

Multi Enterprise MDM

The title of this post is also the title of my presentation at the Master Data Management Summit Europe 2019. This conference is co-located with the Data Governance Conference Europe 2019.

The session will go through these topics:

  • Why business ecosystem wide MDM will be on the future agenda as elaborated in a post on this blog. The post is called Ecosystem Wide MDM.
  • What exactly is multienterprise MDM as examined in a post on The Disruptive Master Data Management Solutions List.
  • How does it apply to party master data and what about data privacy and data protection?
  • How can multienterprise MDM be used within product MDM and what is the link to IoT (Internet of Things).
  • Learn from a concrete use case encompassing product information and AI (Artificial Intelligence) as mentioned in the post It is time to apply AI to MDM and PIM.

You can have a look at the entire agenda at the MDMDG Summit Europe 2019 here.

MDM Ecosystem

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

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.

Shareconomy and MDM

The Master Data Management (MDM) discipline is something that belongs in the backbone of digitalization and enterprise architecture and therefore new ways of doing things always have a hard time in this realm. Fore sure there have been talk about big data and MDM for years, but actual implementations are few compared to ongoing traditional system of record implementations. The same will be the case with Artificial Intelligence (AI) and MDM. We will still see a lot of clerking around MDM for years.

So, I am stretching it far when working with yet a new must do thing for MDM (besides working with MDM, big data and AI).

But I have no doubt about that shareconomy (or sharing economy) will affect the way we work with MDM in the future. A few others are on the same path as for example the Swiss consultancy CDQ as presented on their page about Shareconomy for Customer and Supplier Data and The Corporate Data League (CDL).

Master Data ShareDoing Master Data Management (MDM) enterprise wide is hard enough. The ability to control master data across your organization is essential to enable digitalization initiatives and ensure the competitiveness of your organization in the future.

But it does not stop there. Increasingly every organization will be an integrated part of a business ecosystem where collaboration with business partners and through market places will be a part of digitalization and thus, we will have a need for working on the same foundation around master data.

This new aspect of MDM is also called multienterprise MDM. It will take years to be widespread. But you better start thinking about how this will be a part of your MDM strategy. Because in the long run you must Share or be left out of business.

MDM Hype Cycle, GDSN, Data Quality, Multienterprise MDM and Product Data Syndication

Gartner, the analyst firm, has a hype cycle for Information Governance and Master Data Management.

Back in 2012 there was a hype cycle for just Master Data Management. It looked like this:

Hype cycle MDM 2012
Source: Gartner

I have made a red circle around the two rightmost terms: “Data Quality Tools” and “Information Exchange and Global Data Synchronization”.

Now, 6 years later, the terms included in the cycle are the below:

Hype Cycle MDM 2018
Source: Gartner

The two terms “Data Quality Tools” and “Information Exchange and Global Data Synchronization” are not mentioned here. I do not think it is because the they ever fulfilled their purpose. I think they are being supplemented by something new. One of these terms that have emerged since 2012 is, in red circle, Multienterprise MDM.

As touched in the post Product Data Quality we have seen data quality tools in action for years when it comes to customer (or party) master data, but not that much when it comes to product master data.

Global Data Synchronization has been around the GS1 concept of GDSN (Global Data Synchronization Network) and exchange of product data between trading partners. However, after 40 years in play this concept only covers a fraction of the products traded worldwide and only for very basic product master data. Product data syndication between trading partners for a lot of product information and related digital assets must still be handled otherwise today.

In my eyes Multienterprise MDM comes to the rescue. This concept was examined in the post Ecosystem Wide MDM. You can gain business benefits from extending enterprise wide product master data management to be multienterprise wide. This includes:

  • Working with the same product classifications or being able to continuously map between different classifications used by trading partners
  • Utilizing the same attribute definitions (metadata around products) or being able to continuously map between different attribute taxonomies in use by trading partners
  • Sharing data on product relationships (available accessories, relevant spare parts, updated succession for products, cross-sell information and up-sell opportunities)
  • Having shared access to latest versions of digital assets (text, audio, video) associated with products.

This is what we work for at Product Data Lake – including Machine Learning Enabled Data Quality, Data Classification, Cloud MDM Hub Service and Multienterprise Metadata Management.

The Three MDM Ages

Master Data Management (MDM) is relatively new discipline. The future will prove what is was, but standing here in mid-2018 I see that we already had 2 ages and are now slowly proceeding into a 3rd age. These ages can be coined as:

  • Pre MDM,
  • Middle MDM and
  • High MDM

Pre MDM

In these dark ages the term Master Data Management may have been used, but there were not any established discipline, methodologies, frameworks and technology solutions around that truly could count as MDM.

We had Customer Data Integration (CDI) around, we had Product Information Management (PIM) in the making and some of us were talking Data Quality Management – and that in practice being namely deduplication / data matching.

Middle MDM

MDM as Three Letter Acronym (TLA) emerged in the mid 00’s as told in the post Happy 10 Years Birthday MDM Solutions.

It was at that time Aaron Zornes changed his stage name from The Customer Data Integration Institute to The MDM Institute.

During this age many MDM solutions slowly but steadily have developed into multi-domain MDM solutions as reported over at the Disruptive MDM List in the blog post called 4 Vendor Paths to Multidomain MDM covering the road travelled by 10 vendors on the MDM market.

Most MDM solutions in the Middle MDM Age have been deployed on-premise

High MDM

We are now cruising into the High MDM Age. First and foremost a lot more organizations are now implementing MDM. Many new deployments are cloud based. New ways are tried out like encompassing more than master data in the same platform.

The jury is of course still out about what will be some main trends of the High MDM Age. My money is placed on what Gartner, the analyst firm, calls Multienterprise MDM as elaborated in the post Ecosystem Wide MDM.

MDM Ages.png

Ecosystem Wide Product Information Management

The concept of doing Master Data Management (MDM) not only enterprise wide but ecosystem wide was examined in the post Ecosystem Wide MDM.

As mentioned, product master data is an obvious domain where business outcomes may occur first when stretching your digital transformation to encompass business ecosystems.

The figure below shows the core delegates in the ecosystem wide Product Information Management (PIM) landscape we support at Product Data Lake:

Ecosystem Wide PIM.png

Your enterprise is in the centre. You may have or need an in-house PIM solution where you manipulate and make product information more competitive as elaborated in the post Using Internal and External Product Information to Win.

At Product Data Lake we collaborate with providers of Artificial Intelligence (AI) capabilities and similar technologies in order to improve data quality and analyse product information.

As shown in the top, there may be a relevant data pool with a consensus structure for your industry available, where you exchange some of product information with trading partners. At Product Data Lake we embrace that scenario with our reservoir concept.

Else, you will need to make partnerships with individual trading partners. At Product Data Lake we make that happen with a win-win approach. This means, that providers can push their product information in a uniform way with the structure and with the taxonomy they have. Receivers can pull the product information in a uniform way with the structure and with the taxonomy they have. This product data syndication concept is outlined in the post Sell more. Reduce costs.

What is Interenterprise Data Sharing?

The term “Interenterprise Data Sharing” has been used a couple of times by Gartner, the analyst firm, during the last two decades.

Lately it has been part of the picturing in conjunction with a recent research document with the title Fundamentals for Data Integration Initiatives.

Data Integration.png
Source: Gartner Inc with red ovals added

The term was also used back in 2001 in the piece about that Data Ownership Extends Outside the Enterprise. Here on the blog it was included in the title of the post about Interenterprise Data Sharing and the 2016 Data Quality Magic Quadrant.

In my eyes interenterprise data sharing is closely related to how you can achieve business benefits from taking part in the ecosystem flavor of a digital business platform. Some of the data types where we will see such business ecosystem platform flourish will be around sharing product model master data and data about and coming from things related to the Internet of Things (IoT) theme. This is further explained in the blog page about Master Data Share.