MDM: Subject-Area Data Integration

I frequently describe MDM as subject area data integration. The whole point of mastering and managing data is to simplify data sharing, since confusion only occurs when you have two or more instances of data and it doesn’t match. It’s important to realize that mastering data isn’t really necessary if you only have a single system that contains one copy of data. After all, how much confusion or misunderstanding can occur when there’s only one copy of data? The challenge in making data more usable and easy to understand exists because most companies have multiple application systems each with their own copy of data (and their own “version of truth”). MDM’s promise is to deliver a single view of subject area data. In our book, Customer Data Integration: Reaching a Single Version of the Truth (John Wiley & Sons, 2006), Jill Dyché and I defined MDM as:

“The set of disciplines and methods to ensure the currency, meaning, and quality of a company’s reference data that is shared across various systems and organizations.”

 As companies have grown, so to have the number of systems that require access to each other’s data.  This is why data integration has become one of the largest custom development activities undertaken within an IT organization.  It’s rare that all systems (and their developers) integrate data the same way.  While there may be rigor within an individual application or system, it’s highly unlikely that all systems manipulate an individual subject area in a consistent fashion.  This lack of integrity and consistency becomes visible when information on two different systems conflict.  MDM isn’t a silver bullet to address this problem.  It is a method to address data problems one subject area at a time. 

The reason for establishing a boundary around subject area is because the complexity, rules, and usage of data within most organization tend to differ by subject area. Examples of subject areas include customer, product, and supplier.  There can be literally dozens if not hundreds subject areas within any given company. 

 

Figure 1: Different Data Subject Areas

Do you need to master every subject area? Probably not.  MDM projects focus on subject areas that suffer the most from inaccuracies, mistakes, and misunderstandings, for instance, customers with inaccurate identification numbers, products missing descriptive information, or an employee with an inaccurate start date.   The idea behind master data management is to establish rules, guidelines, and rigor for subject areas data.

The rules associated with identifying a customer are typically well defined within a company.   The rules associated with adding a new product to the sales catalog are also well defined.  The thing to keep in mind is that the rules associated with product will have nothing to do with customers.  Additionally, most companies have rules that limit what customer data can be modified.  They also have rules that restrict how product information can be manipulated..  The idea behind MDM is to manage these rules and methods in a manner where all application systems manipulate reference data in a consistent way.

Implementing MDM isn’t just about building and deploying a server that contains the “master list” of reference data;  that’s the easy part. MDM’s real challenge is integrating the functionality into the multitude of application systems that exist within a company.   The idea is that when a new customer is added, all systems are aware of the change and have equal access to that data. 

For instance, one of the most universal challenges in business today is managing a customer’s marketing preferences.   When a customer asks to opt out of all marketing communications, it’s important that all systems are aware of this choice.  Problems typically occur when a particular data element can be modified from multiple different locations (e.g., a web page, an 800 number, or even the US Postal Service).  MDM provides the solution for ensuring that the master data is managed correctly and that all systems become aware of the change (and the new data) in a manner that supports the businesses needs.

Comments

[Sandeep Potnis]

Excellent article!
Having been through some integration nightmares on BI projects, I can see how MDM could help avoid the glut of one-off mapping tables.

The point that specifically caught my eye in the article is "confusion only occurs when you have two or more instances of data and it doesn’t match".

So the problem MDM solves is not limited integrating multiple systems but also one caused by single systems with multiple instances of data (e.g. multiple ids for same description)

[evan levy]

Sandeep -- yes.

The focus of MDM is to identify (and map/link) to the instances of an individual reference element -- within an individual system and across multiple systems. Since many operational applications that are "systems of creation" can't support data cleansing and changes (because there are embedded application rules/logic that can't support deduplication or data cleansing), it's fairly common that MDM systems are used to understand and resolve application-stored data inconsistencies. We also see this when a single system has multiple tables or images that reflect the "system of creation"

One of the benefits of MDM is to have a scalable and efficient manner of onboarding new systems quickly and efficiently. It wouldn't be practical (or of much value) if every system had to be perfect prior to onboarding to the MDM hub. An MDM hub is built to support and resolve these issues.

Thanks for the feedback.

E.