Master Data Management (MDM) continues to scale as explored in the post about how MDM inflates.
One inflating trend can be named Extended MDM, which is about taking other data domains than the traditional customer, supplier, and product domains under the MDM umbrella and thus creating a governed data hub.
Which entities that will be is industry specific. Examples from retail are warehouses, stores, and the equipment within those. Examples from pharma are own and affiliated plants, hospitals and other served medical facilities. Examples from manufacturing are plants, warehouses as well as the products, equipment and facilities where the produced products are used within.
Extended MDM can be seen as a twin discipline to a data hub strategy with the twist that the MDM approach adds governance to reflecting, integrating, and meshing critical application in a centralized fashion. This trend was touched in the post MDM Terms on the Move in the Gartner Hype Cycle with reference to the latest Hype Cycle for Data and Analytics Governance and Master Data Management.
Some months ago, Gartner published the latest Hype Cycle for Digital Business Capabilities.
The hype cycle includes 4 concepts that in my mind will shape the future of Master Data Management (MDM) and data management in all. These are:
Industrie 4.0
Business Ecosystems
Digital Twin
Machine Customer
Industrie 4.0
You will find Industrie 4.0 near the trough of disillusionment almost ready to climb the slope of enlighten. Several of the recent MDM blue prints I have worked with have Industrie 4.0 as an overarching theme.
Industrie 4.0 is about using intelligent devices in manufacturing and thus closely connected to the term Industrial Internet of Things (IIoT). The impact of industry 4.0 is across the whole supply chain encompassing not only product manufacturing companies but also for example product merchants and product service providers.
With intelligent devices in the supply chain product MDM will evolve from handling data about product models to handle data about each instance of a product.
Business Ecosystems
The concept of business ecosystems has just passed the peak of inflated expectations.
In a modern business environment, no organization can do everything – or even most things – themselves. Therefore, any enterprise needs to partner with other organizations when working on new digital powered business models.
This also calls for increasing sharing of data, including master data, with business partners. This leads to the rise of interenterprise MDM, which by the way is at about the same position in the Hype Cycle for Data Analytics and MDM.
On the climbing side of the peak of inflated expectations we find the concept of a digital twin.
A digital twin is a virtual representation of a real-world entity such as an asset, person, organization, or process. This fits somehow with what MDM is doing which traditionally has been providing virtual descriptions of customers, suppliers, and products.
With the digital twin flavour, you can sharpen and extend MDM in two ways:
Have a more real-world on customers and suppliers by looking at those has roles of business partners along with handling many other external and internal organizational entities
Putting more asset types than direct products under the MDM umbrella with improved data governance as a result
Machine Customers
A bit further down the climbing side of the peak you will see the concept of the machine customer.
The expectation is that more and more buying tasks will be automated so there will be no human interaction in the bulk of purchasing processes.
This will only be possible if the products involved at those who sell them are digitally described in sufficient details and categorized the same way on the selling and buying side.
This seems like a job for Master Data Management and the adjacent Product Information Management (PIM) discipline where the buying side needs the right capabilities not only for direct trading products but also indirect supplies.
Also, the concept of augmented MDM will play a role here by applying Artificial Intelligence (AI) to the MDM and PIM side of enabling the machine customer.
A digital twin is in short digital data representing a physical object.
Master Data Management (MDM) has since the discipline emerged in the 00’s been about managing data representing some very common physical objects like persons, products and locations though with a layer of context in between:
Persons are traditionally described with data aimed for a given role like a person being a customer, patient, student, contact, employee, and many more specific roles.
Products are traditionally described as a product model with data that are the same for a product being mass produced.
Locations are typically described as a postal address and/or a given geocode.
With the rise of digitalization and Internet of Things (IoT) / Industry 4.0 the need for having a more real-world view of persons, a broader view of products, and more useful views of locations arise together with the need of similar digital twins for other object types.
As Knowledge Graph and (extended) MDM can coexist very well, the same objectives are true for MDM as well.
Some of the use cases I have stumbled on are:
Manage generic data about a person and belonging organizations as a digital twin encompassing all historic, current, and sought roles related to your organization. Data privacy must be adhered to here, however issues as opt-in and opt-out must also be handled across roles.
Manage specific data about each instance of a product that is a smart device, which is true for more and more products models. Such a digital twin is described in the post 3 Old and 3 New Multi-Domain MDM Relationship Types.
Manage complex data about a location as boundaries, placements in geographic hierarchies, location names and property descriptions as a digital twin.
Manage data about plants, machines, vehicles, warehouses, stores as digital twins using an MDM approach.
Which digital twins have you stumbled upon where an MDM approach is useful?
Liliendahl on Data Quality 