MDM Foundations: Tactics and strategy that span data quality, MDM and data governance


In my previous post, I talked about planning your MDM initiative and the importance of data quality and data governance to the effort. Today, we're going to drill in a bit into Master Data Management Foundations, a SAS offering unique in the marketplace that serves as a bridge between data quality, data governance and more strategic MDM projects.

What is MDM Foundations?

In a nutshell, MDM Foundations provides a batch-fed, single-domain client-based MDM hub. What might take weeks or months with an enterprise MDM solution would take hours or days with this application.

If you're familiar with the SAS Data Management suite (or previous DataFlux dfPower Studio versions), MDM Foundations is a natural extension. It is built into the SAS Data Management primary user interface with a simple install that takes only minutes. Imagine leveraging the same agile data quality skills to prototype, iterate and capture the definitions – then output a governance process to define your master data entity and source data load jobs.

SAS DataFlux delivers a phased approach to deriving business value.
SAS delivers a phased approach to deriving business value with success at every stage.

A phased approach is often useful for companies who have a long-standing data quality or data governance effort – or for those who want to a more deliberate approach to MDM.  We have several customers (including Mohegan Sun) taking this phased approach.

As the graphic above depicts, there are three stages to a phased MDM approach.

  • Data quality. The initial step for any MDM project, including one that employs MDM Foundations, is to address tactical data quality issues. These often include address verification, name standardization and de-duplication of customer records to reduce marketing expenses.
  • MDM Foundations. The next step is to migrate to a batch-fed, single domain client-based MDM solution. For example, with MDM Foundations you can deliver a single view of product across two ordering systems. It's important to remember that, as mentioned above, this component is embedded inside of your existing SAS Data Management platform.
  • MDM. In the final phase, export that entity definition into SAS MDM. Here, you can use the governance process output to reduce risk and speed time-to-value for your enterprise MDM deployment.

Some of the benefits of MDM Foundations include:

  1. Building MDM competencies within your organization.
  2. Removing deployment complexity.
  3. Tackling the challenges of entity definition, profiling and identification of sources – as well as the generation of matching and survivorship rules with a wizard-based job generation mechanism.
  4. Targeting MDM hubs as if they are any other data source.
  5. Auto-generation of hub load jobs.

In my next post, I will talk about how to apply data governance to a phased MDM approach, as well as the steps required for a final enterprise MDM deployment.


About Author

Matthew Magne

Principal Product Marketing Manager

@bigdatamagnet - Matthew is a TEDx speaker, musician, and Catan player. He is currently the Global Product Marketing Manager for SAS Data Management focusing on Big Data, Master Data Management, Data Quality, Data Integration and Data Governance. Previously, Matthew was an Information Management Solutions Architect at SAS, worked as a Certified Data Management Consulting IT Professional at IBM, and is a recovering software engineer and entrepreneur. Mr. Magne received his BS, cum laude, in Computer Engineering at Boston University, has done graduate work in Object Oriented Development and completed his MBA at UNCW.

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