Not at least Gartner, the analyst firm, has touted this as one of two Disruptive Forces in MDM Land. However, Gartner is not always your friend when it comes to short, crisp and easy digestible definitions and explanations of the terms they promote.
In my mind the two terms MDM and ADM relates as seen below:
So, ADM takes care of a lot of data that we do not usually consider being master data within a given application while MDM takes care of master data across multiple applications.
The big question is how we handle the intersection (and sum of intersections in the IT landscape) when it comes to applying technology.
If you have an IT landscape with a dominant application like for example SAP ECC you are tempted to handle the master data within that application as your master data hub or using a vendor provided tightly integrated tool as for example SAP MDG. For specific master data domains, you might for example regard your CRM application as your customer master data hub. Here MDM and ADM melts into one process and technology platform.
If you have an IT landscape with multiple applications, you should consider implementing a specific MDM platform that receives master data from and provides master data to applications that takes care of all the other data used for specific business objectives. Here MDM and ADM will be in separated processes using best-of-breed technology.
Every organization needs Master Data Management (MDM). But does every organization need a MDM tool?
In many ways the MDM tools we see on the market resembles common database tools. But there are some things the MDM tools do better than a common database management tool. The post called The Database versus the Hub outlines three such features being:
Controlling hierarchical completeness
Achieving a Single Business Partner View
Exploiting Real World Awareness
Controlling hierarchical completeness and achieving a single business partner view is closely related to the two things data quality tools do better than common database systems as explained in the post Data Quality Tools Revealed. These two features are:
Data profiling and
Specialized data profiling tools are very good at providing out-of-the-box functionality for statistical summaries and frequency distributions for the unique values and formats found within the fields of your data sources in order to measure data quality and find critical areas that may harm your business. These capabilities are often better and easier to use than what you find inside a MDM tool. However, in order to measure the improvement in a business context and fix the problems not just in a one-off you need a solid MDM environment.
When it comes to data matching we also still see specialized solutions that are more effective and easier to use than what is typically delivered inside MDM solutions. Besides that, we also see business scenarios where it is better to do the data matching outside the MDM platform as examined in the post The Place for Data Matching in and around MDM.
Looking at the single MDM domains we also see alternatives. Customer Relation Management (CRM) systems are popular as a choice for managing customer master data. But as explained in the post CRM systems and Customer MDM: CRM systems are said to deliver a Single Customer View but usually they don’t. The way CRM systems are built, used and integrated is a certain track to create duplicates. Some remedies for that are touched in the post The Good, Better and Best Way of Avoiding Duplicates.
With product master data we also have Product Information Management (PIM) solutions. From what I have seen PIM solutions has one key capability that is essentially different from a common database solution and how many MDM solutions, that are built with party master data in mind, has. That is a flexible and super user angled way of building hierarchies and assigning attributes to entities – in this case particularly products. If you offer customer self-service, like in eCommerce, with products that have varying attributes you need PIM functionality. If you want to do this smart, you need a collaboration environment for supplier self-service as well as pondered in the post Chinese Whispers and Data Quality.
All in all the necessary components and combinations for a suitable MDM toolbox are plentiful and can be obtained by one-stop-shopping or by putting some best-of-breed solutions together.
If you haven’t yet implemented a Master Data Management (MDM) solution you typically holds master data in dedicated solutions for Supply Chain Management (SCM), Enterprise Resource Planning (ERP), Customer Relation Management (CRM) and heaps of other solutions aimed at taking care of some part of your business depending on your particular industry.
In this first stage some master data flows into these solutions from business partners in different ways, flows around between the solutions inside your IT landscape and flows out to business partners directly from the various solutions.
The big pain in this stage is that a given real world entity may be described very different when coming in, when used inside your IT landscape and when presented by you to the outside. Additionally it is hard to measure and improve data quality and there may be several different business processes doing the same thing in an alternative way.
The answer today is to implement a Master Data Management (MDM) solution. When doing that you in some degree may rearrange the way master data flows into your IT landscape, you move the emphasis on master data management from the SCM, ERP, CRM and other solutions to the MDM platform and orchestrate the internal flows differently and you are most often able to present a given real world entity in a consistent way to the outside.
In this second stage you have cured the pain of inconsistent presentation of a given real world entity and as a result of that you are in a much better position to measure and control data quality. But typically you haven’t gained much in operational efficiency.
You need to enter a third stage. MDM 3.0 so to speak. In this stage you extend your MDM solution to your business partners and take much more advantage of third party data providers.
The master data kept by any organization is in a large degree a description of real world entities that also is digitalized by business partners and third party data providers. Therefore there are huge opportunities for reengineering your business processes for master data collection and interactive sharing of master data with mutual benefits for you and your business partners. These opportunities are touched in the post MDM 3.0 Musings.
Last week I had some fun making a blog post called The True Leader in Product MDM. This post was about how product Master Data Management still in most places is executed by having heaps of MS Excel spreadsheets flowing around within the enterprise and between business partners, as I have seen it.
When it comes to customer Master Data Management MS Excel may not be so dominant. Instead we have MS CRM and the competing offerings as Salesforce.com and a lot of other similar Customer Relationship Management solutions.
CRM systems are said to deliver a Single Customer View. Usually they don’t. One of the reasons is explained in the post Leads, Accounts, Contacts and Data Quality. The way CRM systems are built, used and integrated is a certain track to create duplicates.
Some remedies out there includes periodic duplicate checks within CRM databases or creating a federated Customer Master Data Hub with entities coming from CRM systems and other databases with customer master data. This is good, but not good enough as told in the post The Good, Better and Best Way of Avoiding Duplicates.
During the last couple of years I have been working with the instant Data Quality service. This MDM service sits within or besides CRM systems and/or Master Data Hubs in order to achieve the only sustainable way of having a Single Customer View, which is an instant Single Customer View.
Much of the talking and doing related to Master Data Management (MDM) today revolves around the master data repository being the central data store for information about customers, suppliers and other parties, products, locations, assets and what else are regarded as master data entities.
The difficulties in MDM implementations are often experienced because master data are born, maintained and consumed in a range of applications as ERP systems, CRM solutions and heaps of specialized applications.
It would be nice if these applications were MDM aware. But usually they are not.
As discussed in the post Service Oriented Data Quality the concepts of Service Oriented Architecture (SOA) makes a lot of sense in deploying data quality tool capacities that goes beyond the classic batch cleansing approach.
In the same way, we also need SOA thinking when we have to make the master data repository doing useful stuff all over the scattered application landscape that most organizations live with today and probably will in the future.
MDM functionality deployed as SOA components have a lot to offer, as for example:
Reuse is one of the core principles of SOA. Having the same master data quality rules applied to every entry point of the same sort of master data will help with consistency.
Interoperability will make it possible to deploy master data quality prevention as close to the root as possible.
Composability makes it possible to combine functionality with different advantages – e.g. combining internal master data lookup with external reference data lookup.
As I like to be on the top of the hype curve I was preparing a post about how Santa digs into big data, including social data streams, to be better at finding out who is nice and who is naughty and what they really want for Christmas. But then I suddenly had a light bulb moment saying: Wait, why don’t you take your own medicine and look up who that Santa guy really is?
Starting in social media checking twitter accounts was shocking. All profiles are fake. FaceBook, Linkedin and other social networks all turned out having no real Santa Claus. Going to commercial third party directories and open government data had the same result. No real Santa Claus there. Some address directories had a postal code with a relation like the postcode “H0 H0 H0” in Canada and “SAN TA1” in the UK, but they seem to kind of fake too.
So, shifting from relying on the purpose of use to real world alignment I have concluded that Santa Claus doesn’t exist and therefore he can’t have a data store looking like a toy elephant or any other big data operations going on.
Also I won’t, based on the above instant data quality mash up, register Santa Claus (Inc.) as a prospective customer in my CRM system. Sorry.
One of my pet peeves in data quality for CRM and ERP systems is the often used way at looking at entities, not at least party entities, in a flat data model as told in the post A Place in Time.
Party master data, and related location master data, will eventually be modeled in very complex models and surely we see more and more examples of that. For example I remember that I long time ago worked with the ERP system that later became Microsoft Dynamics AX. Then I had issues with the simplistic and not role aware data model. While I’m currently working in a project using the AX 2012 Address Book it’s good to see that things have certainly developed.
This blog has quite a few posts on hierarchy management in Master Data Management (MDM) and even Hierarchical Data Matching. But I have to admit that even complex relational data models and hierarchical approaches in fact don’t align completely with the real world.
I remember at this year’s MDM Summit Europe that Aaron Zornes suggested that a graph database will be the best choice for reflecting the most basic reference dataset being The Country List. Oh yes, and in master data too you should think then, though I doubt that the relational database and hierarchy management will be out of fashion for a while.
So it could be good to know if you have seen or worked with graph databases in master data management beyond representing a static analysis result as a graph database.