4 Vendor Paths to Multidomain MDM

In short, multidomain Master Data Management (MDM) is about handling all core business entities under a single regime. This encompasses customer MDM, vendor MDM – or better unified party / business partner MDM, product MDM, location MDM, asset MDM and the relations between those domains.

MDM VennAs a user organization you may choose the buy these technologies separately or buy a multidomain MDM solution from a given vendor.

There are a range of available multidomain solutions on the market. These solutions have been assembled in different ways. Below are 4 paths various vendors have taken to offer a multidomain MDM solution:

Acquisition

Mega-vendors as IBM, Oracle and Informatica have bought best-of-breed solutions and wrapped those under the brand. Taking advantage of this offering will typically mean also choosing other technologies from there whole application stack and the technologies that handles the various domains will not be exactly the same.

ADM

ADM stands for Application Data Management. Some offerings are an extension to an ERP suite. The predominant example is SAP MDG. With SAP MDG you get a close integration and a data model fit with the popular SAP ECC solution. However, if you want to include other sources, you may have a hard time doing that.

PIM

Some former traditional Product Information Management (PIM) vendors have successfully transformed their solution into a true multidomain MDM solution. This includes (all from this list) Enterworks, Riversand and Stibo Systems. Recently Stibo Systems announced that their last year’s impressive growth was led by increased demand for multidomain solutions.

Natural born

Other solutions are built up from scratch and thought through as a multidomain MDM solution. Examples includes Orchestra Networks, Reltio and the first forward looking vendor who joined this list: Semarchy.

8 Forms of Master Data Management

8 forms MDM

Master Data Management (MDM) can take many forms. In the following I will shortly introduce 8 forms of MDM. A given MDM implementation will typically be focused on one of these forms with some elements of the other forms and a given piece of technology will have an origin in one of these forms and in more or less degree encompass some more forms:

1.      The traditional MDM platform: A traditional MDM solution is a hub for master data aiming at delivering a single source of truth (or trust) for master data within a given organization either enterprise wide or within a portion of an enterprise. The first MDM solutions were aimed at Customer Data Integration (CDI), because having multiple and inconsistent data stores for customer data with varying data quality is a well-known pain point almost everywhere. Besides that, similar pain points exist around vendor data and other party roles, product data, assets, locations and other master data domains and dedicated solutions for that are available.

2.      Product Information Management (PIM): Special breed of solutions for Product Information Management aimed at having consistent product specifications across the enterprise to be published in multiple sales channels have been around for years and we have seen a continuously integration of the market for such solutions into the traditional MDM space as many of these solutions have morphed into being a kind of MDM solution.

3.      Digital Asset Management (DAM): Not at least in relation to PIM we have a distinct discipline around handling digital assets as text documents, audio files, video and other rich media data that are different from the structured and granular data we can manage in data models in common database technologies. A post on this blog examines How MDM, PIM and DAM Stick Together.

4.      Big Data Integration: The rise of big data is having a considerable influence on how MDM solutions will look like in the future. You may handle big data directly inside MDM og link to big data outside MDM as told in the post about The Intersection of MDM and Big Data.

5.      Application Data Management (ADM): Another area where you have to decide where master data stops and handling other data starts is when it comes to transactional data and other forms data handled in dedicated applications as ERP, CRM, PLM (Product Lifecycle Management) and plenty of other industry specific applications. This conundrum was touched in a recent post called MDM vs ADM.

6.      Multi-Domain MDM: Many MDM implementations focus on a single master data domain as customer, vendor or product or you see MDM programs that have a multi-domain vision, overall project management but quite separate tracks for each domain. We have though seen many technology vendors preparing for the multi-domain future.

7.      MDM in the cloud: MDM follows the source applications up into the cloud. New MDM solutions naturally come as a cloud solution. The traditional vendors introduce cloud alternatives to or based on their proven on-promise solutions. There is only one direction here: More and more cloud MDM – also as customer as business partner engagement will take place in the cloud.

8.      Ecosystem wide MDM: Doing MDM enterprise wide is hard enough. 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 as reported in the post Ecosystem Wide MDM.

PS: If you are a vendor on the MDM, PIM or DAM market you can promote your solution here on the list and emphasize on the forms you support. Registration can be done here.

MDM Fact or Fiction: Who Knows?

Over at the IRM connects blog Aaron Zornes wrote about MDM & Data Governance Market 2018-19: Facts vs. Beliefs.

It is always fun to be entertained by analysts bashing each other and Aaron Zornes makes no exception in his blog post when pointing out that it took Gartner 12 years to realize that there are not two MDM markets anymore (Customer MDM / CDI vs Product MDM /PIM) but only one.

Also, the recent vitalized notion of Application Data Management (ADM) by Gartner gets a few words about who is lagging and who is leading in market analysis.

Else the two entertaining trends the coming years will according to Aaron Zornes be Graph databases and machine learning.

MDM ADMSo, a lot of questions for buyers and vendors are piling up:

  • Should your solution be multi-domain MDM or will there still be room for specialized CDI (Customer Data Integration) and PIM (Product Information Management) solutions?
  • Does ADM (Application Data Management) solutions, meaning building your master data capabilities inside ERP, CRM and so make sense?
  • Will graph databases really brake through in MDM soon?
  • Can ML (Machine Learning) help delivering business benefits in MDM – and when will that happen?

As the analysts disagree between each other, you can have your say here on the list by commenting on this blog post, under the registered solutions or as a not yet listed vendor registering your disruptive solution.

How MDM Solutions are Changing

When Gartner, the analyst firm, today evaluates MDM solutions they measure their strengths within these use cases:

  • MDM of B2C Customer Data, which is about handling master data related to individuals within households acting as buyers (and users) of the products offered by an organisation
  • MDM of B2B Customer Data, which is about handling master data related to other organizations acting as buyers (and users) of the products offered by an organisation.
  • MDM of Buy-Side Product Data, which is about handling product master data as they are received from other organisations.
  • MDM of Sell-Side Product Data, which is about handling product master data as they are provided to other organisations and individuals.
  • Multidomain MDM, where all the above master data are handled in conjunction with other party roles than customer (eg supplier) and further core objects as locations, assets and more.
  • Multivector MDM, where Gartner adds industries, scenarios, structures and styles to the lingo.

QuadrantThe core party and product picture could look like examined in the post An Alternative Multi-Domain MDM Quadrant. Compared to the Gartner Magic Quadrant lingo (and the underlying critical capabilities) this picture is different because:

  • The distinction between B2B and B2C in customer MDM is diminishing and does not today make any significant differentiation between the solutions on the market.
  • Handling customer as one of several party roles will be the norm as told in the post Gravitational Waves in the MDM World.
  • We need (at least) one good MDMish solution to connect the buy-sides and the sell-sides in business ecosystems as pondered in the post Gravitational Collapse in the PIM Space.

3 Reasons MDM No Longer Delivers a Customer 360

Today’s guest blog post is from David Corrigan, CEO at AllSight

When Master Data Management (MDM) and Customer Data Integration (CDI) were designed over 15 years ago, they were touted as the answer to “Customer 360”.  But the art of mastering data and the art of creating a complete view of a customer are two very different things.  MDM is focused on managing a much smaller, core data set and aims to very deeply and truly master it.  Customer 360 solutions focus on “all data about the customer” to get the complete picture.  When it comes to a 360-degree view of the customer, master data is only part of the story.  Additional data has to be part of the 360 in order to have a full understanding of the customer – whether that be an individual or an organization. Additional data sources and data types required for today’s Customer 360 include transactions, interactions, events, unstructured content, analytics and intelligence – all of which are not managed in MDM.

Today, leading organizations are looking beyond MDM to a new era of Customer 360 technology to deliver the elusive complete view of the customer.  Here are 3 reasons why

  1. Customer 360 needs all data; MDM only stores partial data.  MDM focuses on core master data attributes, matching data elements and improving data quality.  Customer 360 has rapidly evolved requiring big data sets such as transactions and interactions, as well as unstructured big data like emails, call center transcriptions, and web chat interactions not to mention social media mentions, images and video.
  2. Customer 360 must serve analytical and operational needs; MDM only supports operational processing.  The original intent of MDM was to provide ‘good’ data to CRM and transactional systems.  While a branch of MDM evolved for ‘analytical MDM’ use cases, it was really a staging area for quality and governance to occur before data was loaded into warehouse for analysis and reporting.  A Customer 360 is meant to be analyzed and used by marketing analysts, data scientists as well as customer care and sales staff – it powers many different personas with different perspectives of the customer.
  3. Customer 360 is about improving the customer experience; Master data (core data) is used during a customer experience. Master Data is required during a customer interaction to understand key facts about a customer including name, contact info, account info, etc.  But the Customer 360 needs to blend all interactions, transactions and events into a comprehensive customer journey in order to analyze and personalize customer experiences.

But why does a Customer 360 now require all this information and capabilities beyond traditional MDM?  It is because the expectations of customers and the demands they make on businesses have changed.  Customers want personalized service and they want it now.  And they want a consistent experience across all channels – online, via phone, and in store.  They don’t want to have to repeat themselves or their preferences every time they interact with a business.  This requires companies to know more about their customers and to anticipate their next move in order to retain their business and loyalty.  Because, not only are customers more demanding than ever, but it is also easier for them to switch brands with little to no cost.

In order to meet these demands, many organizations assume they need to build these capabilities on their own using new technologies such as Apache Hadoop and Graph data stores.  These technologies can join together silos of master data, transactional data, raw data lake data, and experience/journey analytics.  However, a new class of software is emerging that bridges data, analytics and action and is based on these modern technologies. Customer Intelligence Platforms manage all customer information and synthesize it into an intelligent Customer 360.  Synthesizing all of those data sources is no easy task and that is where many organizations stall out.  What’s required is a machine-learning contextual matching engine that automates the process of linking customer data and evaluates data confidence.

Organizations such as Dell are seeing this shift first hand and have recognized that legacy MDM apps alone aren’t cutting it.  Deotis Harris, Senior Director, MDM at Dell EMC said “We saw an opportunity to leverage AllSight’s modern technology (Customer Intelligence), coupled with our legacy systems such as Master Data Management (MDM), to provide the insight required to enable our sellers, marketers and customer service reps to create better experiences for our customers.”

If you are like Dell and so many other organizations, a Customer 360 is high on your priority list.  A Customer Intelligence Platform might just be your next step.

MDM not 360