Tag Archives: Master Data Governance

Three Valuable Ways To Use Your Metadata Manager MDM Tool

You probably know that you can and must validate the integrity of your Data Model using the Metadata Manager Tool workbench (MET), but you can also do much more. In this blog I focus on three additional things you can do with the MET.

Once you have used MET to obtain a valid Data Model, you can continue using MET to:

Migrate your project from development to test, and to production via change lists. (more…)

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Posted in Data Quality, Informatica University, Master Data Management | Tagged , , , , , , , | 1 Comment

Demarcating The Lines In Master Data Governance Turf Battles

Had a great meeting last week with the director of data management at a leading life sciences firm.  The company has a long-standing culture of business units running on a very autonomous basis.  But they are realizing that in order to have visibility into operations at a global enterprise level, and to run their business more effectively, they have to do things differently.

While a couple groups have implemented specific MDM initiatives, at a corporate level they are just embarking upon enterprise data management (EDM) and MDM.  We discussed a lot of things in the meeting, including the technical foundation for EDM, and how the various capabilities of the Informatica Platform mapped to their requirements.

But one of the most interesting topics was how they were handling who owned what master data.  In every company, different functions and different groups will argue about master data—what it is, how it is defined, and who “owns” it.  In a company with a history of decentralized operations, the notion of imposing master data at a corporate level is particularly challenging. (more…)

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Posted in Data Governance, Enterprise Data Management, Master Data Management | Tagged | 3 Comments

Technology as an Enabler for Master Data Governance – Five Things To Look For In An MDM Solution

No two companies approach master data governance the same way, because master data governance requires a unique approach within every organization, even among companies within the same industry. A customized approach is necessary because master data governance is closely tied to a company’s business processes, culture, and IT landscape. For the same reason, every company thinking of embarking on a master data governance initiative needs to go through a well-planned design phase in order to create a program that fits the distinct requirements of the organization.

Moreover, when it comes to execution, you need master data management (MDM) technology to bring that design vision to fruition. And not just any MDM technology, but one that’s flexible enough to support your custom design. If the MDM platform is rigid in its functionality—for instance, perhaps it supports only a fixed data model—you might have to compromise your data governance design to adapt to the limitations of the technology. So, what capabilities does your technology platform need in order to support your data governance design?

There are five broad requirements that any MDM platform should support out-of-the-box. To wit, your MDM platform needs to be able to:
1. Define your unique business data using a flexible data model.
2. Infer relationships across your data, such as between customer and products.
3. Support creation of master data directly on the MDM platform (“system of entry”).
4. Support centralized and federated MDM architecture.
5. Configure (rather than customize) data quality, data consolidation, and data governance rules.

Fundamental to these five requirements is the ability to capture rules and metadata. Since your policies cover your business processes, you want a rules-based system that can be easily used by a business person to capture the policies. In the absence of a rules-based system, you are going to have your programmers writing code, which is not an efficient way to implement data governance.

Similarly, you want all changes to your data captured as metadata. In the absence of metadata, you cannot prove the reliability or the origin of the data. When this happens, your users will not use the data because they can’t trust it, ultimately causing your data governance initiative to fail.

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