Multienterprise MDM

So, you have the algorithm! But do you have the data?

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In the game of winning in business by using Artificial Intelligence (AI) there are two main weapons you can use: Algorithms and data. In a recent blog post Andrew White of Gartner, the analyst firm, says that It’s all about the data – not the algorithm.

AI iconIn the Master Data Management (MDM) space the equipment of solutions with AI capabilities has been going on for some time as reported in the post Artificial Intelligence (AI) and Master Data Management (MDM).

So, next thing is how to provide the data? It is questionable if every single organization has the sufficient (and well managed) master data to make a winning formula. Most organizations must, for many use cases, look beyond the enterprise firewall to get the training data or better the data fuelled algorithms to win the battles and the whole game.

An example of such a scenario is examined in the post Artificial Intelligence (AI) and Multienterprise MDM.

Why Multienterprise MDM will Underpin Digital Transformation

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I read (and write) a lot about why Master Data Management (MDM) is a core capability you need to succeed in digital transformation.

Over at the Profisee blog there is a post about that, extending the capability to be multidomain MDM. The post is called The Role of Multi-Domain MDM in Digital Transformation.

Also, at the Reltio blog as part of the #ModernDataMasters series, Tony Saldanha, author of the book  Why Digital Transformations Fail, explains: “Look at master data in terms of the entire virtual company – the total supply chain including your clients and suppliers – and create an ecosystem to drive standards across that.”

Tony continues: “The investment in master data within ecosystems is going to increase dramatically. People are going to realise that most of the waste that happens is at the seams of large organisations – not having a common language between the accounts payable of one company and the accounts receivable of another company means both companies are wasting resources and money.”

Multienterprise MDM Digital Transformation

This way of looking at MDM as something that goes beyond each organization and evolves to be ecosystem wide is also called Multienterprise MDM.

In my eyes this is a very important aspect of using MDM within digital transformation. This theme is further examined in the post Why is Your Digital Ecosystem and MDM the Place to Begin in Digital Transformation?

Unifying Data Quality Management, MDM and Data Governance

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During the end of last century data quality management started to gain traction as organizations realized that the many different applications and related data stores in operation needed some form of hygiene. Data cleansing and data matching (aka deduplication) tools were introduced.

In the 00’s Master Data Management (MDM) arised as a discipline encompassing the required processes and the technology platforms you need to have to ensure a sustainable level of data quality in the master data used across many applications and data stores. The first MDM implementations were focused on a single master data domain – typically customer or product. Then multidomain MDM (embracing customer and other party master data, location, product and assets) has become mainstream and we see multienterprise MDM in the horizon, where master data will be shared in business ecosystems.

MDM also have some side disciplines as Product Information Management (PIM), Digital Asset Management (DAM) and Reference Data Management (RDM). Sharing of product information and related digital assets in business ecosystems is here supported by Product Data Syndication.

Lately data governance has become a household term. We see multiple varying data governance frameworks addressing data stewardship, data policies, standards and business glossaries. In my eyes data governance and data governance frameworks is very much about adding the people side to the processes and technology we have matured in MDM and Data Quality Management (DQM). And we need to combine those themes, because It is not all about People or Processes or Technology. It is about unifying all this.

In my daily work I help both tool providers and end user organisations with all this as shown on the page Popular Offerings.

DG DQ and MDM

 

Why is Your Digital Ecosystem and MDM the Place to Begin in Digital Transformation?

Henrik Gabs LiliendahlLeave a comment

The question “Why is Your Digital Ecosystem the Place to Begin?” was asked by Frank Diana of Tata Consultancy Services in the article Why an ecosystem strategy is where digital transformations begin.

As said by Frank Diana: “Whatever can be digitized is being digitized, and that means it’s available to be shared with other, digitally-enabled companies.”

This is true for master data as well. The role of Master Data Management (MDM) in making digital transformation a success was examined in the Disruptive MDM solution list post Digital Transformation Success Rely on MDM / PIM Success.

The concepts mentioned were:

  • Providing a 360-degree view of master data entities
  • Enabling happy self-service scenarios
  • Underpinning the best customer experience
  • Encompassing Internet of Things (IoT)

Providing a 360-degree view of master data entities through Golden Records in Multidomain MDM will be much easier by sharing master data that is already digitalised as third-party reference data and/or at business partners.

Enabling happy self-service scenarios can be done much more effectively by opening up the master data onboarding to business partners and customers them selves and by letting product data flow easily between trading partners as pondered in the post Linked Product Data Quality.

Underpinning the best customer experience will require that you utilize data from and about the whole business ecosystem where your company is a participant.

Encompassing Internet of Things (IoT) means that you must share master within the business ecosystem as touched in the post IoT and MDM.Digital Transformation MDM and business ecosystems

IoT and Business Ecosystem Wide MDM

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Two of the disruptive trends in Master Data Management (MDM) are the intersection of Internet of Things (IoT) and MDM and business ecosystem wide MDM (aka multienterprise MDM).

These two trends will go hand in hand.

IoT and Ecosystem Wide MDM

The latest MDM market report from Forrester (the other analyst firm) was mentioned in the post Toward the Third Generation of MDM.

In here Forrester says: “As first-generation MDM technologies become outdated and less effective, improved second generation and third-generation features will dictate which providers lead the pack. Vendors that can provide internet-of-things (IoT) capabilities, ecosystem capabilities, and data context position themselves to successfully deliver added business value to their customers.”

This saying is close to me in my current job as co-founder and CTO at Product Data Lake as told in the post Adding Things to Product Data Lake.

In business ecosystem wide MDM business partners collaborate around master data. This is a prerequisite for handling asset master data involved in IoT as there are many parties involved included manufacturers of smart devices, operators of these devices, maintainers of the devices, owners of the devices and the data subjects these devices gather data about.

In the same way forward looking solution providers involved with MDM must collaborate as pondered in the post Linked Product Data Quality.

Artificial Intelligence (AI) and Multienterprise MDM

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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

Henrik Gabs Liliendahl2 Comments

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

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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

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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

Henrik Gabs LiliendahlLeave a comment

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.