Top 5 Misconceptions About Master Data Management

The success of even the most well-thought out plans hinge on an organization’s ability to execute. Its people, technology and processes must work in unison towards the same goal. Companies have used some form of Master Data Management (MDM) for about as long as there has been data to manage. The most effective companies know how to leverage their people, technology, and processes to implement MDM successfully.

Master Data Management is defined as a combination of technologies and processes that help manage data integrity, flow, and synchronization. To successfully implement MDM, a company must enforce standards at the enterprise level and make sure they are carried out by the people, technology, and processes of the organization.

MDM in itself is pretty straightforward, but because it is often interpreted in different ways, many misconceptions have been formed. The five misconceptions listed below are the most common.

1. The belief that MDM should be handled like a data quality program

This misconception is common because data quality is so vital to the success of any MDM. However, MDM itself should not be thought of as a data quality program – it should be understood that it contains a data quality program, but this is only one of its elements.

2. The belief that MDM is like a data warehouse

An MDM is often compared to a centralized hub of data, and this is where the misconception begins. There is only one similarity: both are meant to offer meaningful information in an organized manner.  But this is where the similarities end. MDM handles more of the operational data integration, where data warehouses are meant to support applications by drawing out trends from historic information.

3. Thinking that MDM is an infrastructure initiative

It may help if you think of MDM like a discipline rather than your typical IT initiative. You can only experience a successful MDM implementation when you have all executives on the same page. Rarely does it depend on where the tool is offered. In contrast to an infrastructure initiative, with MDM, technology is a last priority.

4. The idea that MDM should be treated as an application

MDM is a process, not an application, and should always be treated as such. If you were to treat MDM as an application, you would undoubtedly measure its success by the following criteria: ease of use, ease of navigation, and user-friendliness. However, the success of MDM lies beyond the scope of such qualifications. You would be better suited to measure your MDM by the value it is delivering to your business.

5. The assumption that data governance is optional

One should never make this mistake when running MDM. If the data is not properly controlled from the start, all decisions that follow cannot be trusted, therefore nullifying the entire MDM.

Keep in mind that the four most important attributes for an MDM are accuracy, timeliness, consistency, and completeness. If you run your MDM with those things in mind, you are on your way to data management success.