Another implication is that even the best fit MDM solution will not necessarily cover all your needs.
One example is within data matching, where I have found that the embedded solutions in MDM tools often only have limited capabilities. To solve this case, there are best of breed data matching solutions on the market able to supplement the MDM solutions.
Another example close to me is within multienterprise (business ecosystem wide) MDM, as MDM solutions are focused on each given organization. Here your interaction with a trading partner, and the interaction by the trading partner with you, can be streamlined with a solution like Product Data Lake.
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.
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.
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.”
This way of looking at MDM as something that goes beyond each organization and evolves to be ecosystem wide is also called Multienterprise MDM.
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.
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.
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.”
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.
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.
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:
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.