After completing more than 60 enterprise MDM projects, Gaine has built an extensive knowledge base.  In this MDM Academy series, we have complied a collection of bite-sized lessons learned which we hope you will find useful. The world of MDM is complex and we trust you will find value in our experience. We welcome your feedback and suggestions for future articles.

 

Explore by Topic


Architecture

MDM and SOA Integration

One of the roles of an MDM Hub is the Harmonization of changes to Master Data between applications. This is sometimes misconstrued as an alternative approach to synchronizing changes via a service oriented architecture (SOA). MDM has an important role in simplifying the implementation of SOA in the enterprise.

Measuring Trust

Deciding which systems are trusted to update a master record can be daunting. Tools and techniques have been developed to measure and rank systems in order to calculate “trust” but just how practical are these in the real world?


Core Processing

Durable and Non Durable Attributes

Durable attributes, things like social security number or date of birth, are not expected to change. Attributes that can be expected to change over time such as address, credit terms or marital status are called non durable attributes. Durable and non durable attributes require different management techniques within the MDM Hub.

When is Different Not Different?

An MDM solution must be able to tell the difference between how data is represented versus what it represents. This is just another of the complications that MDM architects must deal with when designing a solution.


Data Governance

Notification Attributes

Not all changes to a master record are equal. Some changes to master data are so impactful to downstream systems that they warrant additional attention. By tagging certain attributes as Notification Attributes we can ensure that these high impact changes are flagged for additional governance in the MDM process.

Managing Data Stewards

The extent to which rules can be used to resolve the conflicts and ambiguities arising from inconsistent and incomplete data is limited. At some point it becomes necessary to turn to data stewards to apply human reasoning and additional insight to the data. There are many factors that determine the amount of work required by data stewards, but in some cases the workload can be significant. In this article we discuss some of the important considerations when planning and managing a large data stewarding effort.

Fitness for Purpose

Data Quality should only be measured within the context of how it will be used. The concept of “Fitness for Purpose” is central to any data quality metrics within a data governance program.

Data Governance Plans: Many Companies Don't Have One

Companies may be diving deep into big data, but failure to implement data governance policies puts many at risk.


Project Management

Business Process Improvements

The analysis of master data and MDM processing logs can reveal interesting opportunities for business process improvements. We show how, as long as you collect the right data and know how to analyze it, you can drive significant business benefits that extend beyond just data quality improvements.

Changing a Match Rule

"How do we change a match rule?" Our answer - "With a great deal of care and planning!" In this article we will talk about why the data governance considerations relating to the changing of a match rule (or any MDM rule) are far more important than the mechanics of implementing the change.

Opt In Synchronization

Not all operational systems will choose to, or be able to, consume the changes made to master data in an MDM hub. The reasons for being out-of-synchronization may be technical, regulatory, political or economic but at some point it will be necessary to manage different views of the same master data.

Key Questions to Ask During Master Data Consolidations

Typical master data consolidation starts with combining the operational master records from all the data silos where they exist. The key aspect being, creation of master data indexes to support single view; knowing and asking right questions during this phase can save lot of time and rework.