Extended MDM Platforms

There is a tendency on the Master Data Management (MDM) market that solutions providers aim to deliver an extended MDM platform to underpin customer experience efforts. Such a platform will not only handle traditional master data, but also reference data, big data (as data lakes) either directly or by linking to the data in there as well as linking to transactions.

The recent acquisition of AllSight by Informatica is an example hereof.

In this context traditional MDM will, supplemented with Reference Data Management (RDM), enable the handling of:

  • Customer, supplier and product identity
  • Customer, supplier and product hierarchies
  • Customer, supplier and product locations

Additionally, the data lake concept can be used for:

Extended MDM Platforms

What is your view: Should MDM solution providers stick to traditional master data or should they strive to encompass other kinds of data too?

Artificial Intelligence (AI) and Master Data Management (MDM)

One of the hottest topics in the Master Data Management (MDM) realm right now is how Artificial Intelligence (AI), Machine Learning (ML) and Master Data Management stick together.

The MDM platform vendors are thus also busy with promoting the AI and ML capabilities within their offerings.

Some examples are:

Reltio: The Reltio page on AI, ML and Deep Learning (DL) focusses on two main scenarios:

  • Continuously improving consistency, accuracy, and manageability for better data quality (DQ), uncovering patterns, anomaly detection and streamlining data-driven jobs.
  • Using data-driven applications to enable a seamless foundation for the generation of relevant insights and contextual recommended actions.

Riversand: In a post called Cloud multi-domain MDM as the foundation for Digital Transformation Riversand CEO Upen Veranasi emphasized that enterprises want to better understand their customers (utilizing customer insights through AI), create/sell products and services that meet and exceed customer expectations (product positioning through AI), meet their customers across all touch points (channel management), secure their customer’s information (privacy), and interact with trading partners to creating cutting edge product differentiation (cloud and AI).

Enterworks: In a recent Enterworks product announcement it was stressed that “future proofing” investments mean that core data management hubs can provide the data to harness the advances in other technologies, such as AI and machine learning.

Artificial Intelligence is also a frequent topic on MDM conferences around. The MDM Summit Europe 2019 in London is no exception with multiple sessions touching on the connection between AI and MDM – with the presentation from yours truly as an example. You can view the entire agenda for this event here.

AI

Multienterprise MDM

One of the terms on the move on the Gartner Hype Cycle for Information Governance and Master Data Management is Multienterprise MDM.

Doing 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 will be a part of digitalization and thus we will have a need for working on the same foundation around master data.

The different master data domains will have different roles to play in such endeavors. Party master will be shared in some degree but there are both competitive factors, data protection and privacy factors to be observed as well.

MDM Ecosystem

Product master data – or product information if you like – is an obvious master data domain where you can gain business benefits from extending master data management to be ecosystem 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 access to latest versions of digital assets (text, audio, video) associated with products

The concept of ecosystem wide Multi-Domain MDM is explored further is the article about Master Data Share.

Golden Records in Multidomain MDM

The term golden record is a core concept within Master Data Management (MDM). A golden record is a representation of a real world entity that may be compiled from multiple different representations of that entity in a single or in multiple different databases within the enterprise system landscape.

GoldIn Multidomain MDM we work with a range of different entity types as party (with customer, supplier, employee and other roles), location, product and asset. The golden record concept applies to all of these entity types, but in slightly different ways.

Party Golden Records

Having a golden record that facilitates a single view of customer is probably the most known example of using the golden record concept. Managing customer records and dealing with duplicates of those is the most frequent data quality issue around.

If you are not able to prevent duplicate records from entering your MDM world, which is the best approach, then you have to apply data matching capabilities. When identifying a duplicate you must be able to intelligently merge any conflicting views into a golden record.

In lesser degree we see the same challenges in getting a single view of suppliers and, which is one of my favourite subjects, you ultimately will want to have a single view on any business partner, also where the same real world entity have both customer, supplier and other roles to your organization.

Location Golden Records

Having the same location only represented once in a golden record and applying any party, product and asset record, and ultimately golden record, to that record may be seen as quite academic. Nevertheless, striving for that concept will solve many data quality conundrums.

GoldLocation management have different meanings and importance for different industries. One example is that a brewery makes business with the legal entity (party) that owns a bar, café, restaurant. However, even though the owner of that place changes, which happens a lot, the brewery is still interested in being the brand served at that place. Also, the brewery wants to keep records of logistics around that place and the historic volumes delivered to that place. Utility and insurance is other examples of industries where the location golden record (should) matter a lot.

Knowing the properties of a location also supports the party deduplication process. For example, if you have two records with the name “John Smith” on the same address, the probability of that being the same real world entity is dependent on whether that location is a single-family house or a nursing home.

Product Golden Record

Product Information Management (PIM) solutions became popular with the raise of multi-channel where having the same representation of a product in offline and online channels is essential. The self-service approach in online sales also drew the requirements of managing a lot more product attributes than seen before, which again points to a solution of handling the product entity centralized.

In large organizations that have many business units around the world you struggle with having a local view and a global view of products. A given product may be a finished product to one unit but a raw material to another unit. Even a global SAP rollout will usually not clarify this – rather the contrary.

GoldWhile third party reference data helps a lot with handling golden records for party and location, this is lesser the case for product master data. Classification systems and data pools do exist, but will certainly not take you all the way. With product master data we must, in my eyes, rely more on second party master data meaning sharing product master data within the business ecosystems where you are present.

Asset (or Thing) Golden Records

In asset master data management you also have different purposes where having a single view of a real world asset helps a lot. There are namely financial purposes and logistic purposes that have to aligned, but also a lot of others purposes depending on the industry and the type of asset.

With the raise of the Internet of Things (IoT) we will have to manage a lot more assets (or things) than we usually have considered. When a thing (a machine, a vehicle, an appliance) becomes intelligent and now produces big data, master data management and indeed multi-domain master data management becomes imperative.

You will want to know a lot about the product model of the thing in order to make sense of the produced big data. For that, you need the product (model) golden record. You will want to have deep knowledge of the location in time of the thing. You cannot do that without the location golden records. You will want to know the different party roles in time related to the thing. The owner, the operator, the maintainer. If you want to avoid chaos, you need party golden records.

Product Something Management

This Disruptive MDM Solutions List encompasses what usually is called solutions for Product Information Management (PIM).

But besides PIM there are some other closely related Three Letter Acronym (TLA) coined solutions in play when it comes to the management of product data and product information. These are:

  • PCM which can stand for both Product Content Management and Product Catalog Management. There are various explanations for the difference between the two PCMs and PIM. The consensus seems to be that both representations of PCM is focussed on channel specific and formatting issues and may be relying on other data management disciplines as for example Master Data Management (MDM) and product data syndication services.
  • PDM being Product Data Management in whatever way that is different from Product Information Management. You might consider PDM techier than PIM.
  • PLM being Product Lifecycle Management. Solutions for PLM typically are different from other data management solutions, but then Stibo Systems recently have combined PLM, PIM and Multidomain MDM in a single platform and PLM has been an element from the start in the SyncForce Product Success Platform.
  • PxM is Product experience Management. In a recent post Gartner analyst Simon Walker explains how PCM and PIM for some vendors have merged into PxM in this piece.

Anyway, providers of all kinds of Product Something Management solutions are welcomed to register on this site.

PxM