Confusion: MDM vs Data Quality

Lawrence Dubov Posted by Lawrence Dubov • 1 December 2009 • Print This Post Print This Post
There's a lot of confusion between MDM and data quality

There's a lot of confusion between MDM and data quality

A data hub as an architectural concept is at the very core of most MDM implementations. At a high level the data hub’s function is to resolve the most important and distributed across the enterprise entities (master entities) and relationships.

Modern data hubs have grown into powerful, multi-faceted service oriented toolkits that match, link, merge, cleanse, validate, standardize, steward and transform data. Modern hubs accomplish this by leveraging advanced algorithms and master data modeling patterns, a variety of services, techniques and user-friendly interfaces.

With the advances of MDM we observe a growing confusion around commonalities and differences between MDM and data quality that manifest themselves in questions like:

  • Will an MDM initiative resolve our data quality issues? Is this the right approach to data quality?
  • We are not sure whether we need a data quality project first before we can implement MDM?
  • We are not sure where the boundaries are between MDM and data quality. How are these two related?
  • We are confused and don’t know which of these two projects we need. If we need both, then what are the dependencies and the recommended implementation sequence?
  • We already have data quality tools. Do we still need MDM?
  • Is data quality part of MDM, or is MDM part of data quality?

A list of this type of questions can be easily expanded. What is your experience? In essence, the questions tell us that there exists confusion around what MDM does in terms of data quality and what it does not.

No simple answer can resolve the confusion expressed in the above questions. Instead, a discussion on how MDM can help develop data quality along with the critical path methods, techniques and considerations is required. This is the focus of the present series of blogs.

What other questions have you encountered along these lines? Share your notes in the comments.

Read the next post in this series, MDM as a Technique to Prioritize Data Quality Problems.


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4 Responses »

  1. Dylan Jones
    17 December 2009 • 9:00 pm

    Great post Larry, as you say there is a great deal of confusion in this area.

    I think one of the most common questions I've read, certainly from our members, relates to ownership - is MDM a subset of DQ or vice versa?

    Sometimes this is simply an issue with maturity I feel, many organisations have a simplistic view of data quality as cleansing so they (mistakenly in my view) perceive data quality as a function within the wider MDM strategy.

    I don't think this has been made any easier by some vendors switching their message from being a generic DQ player to a DQ with MDM capability, in my eyes this isn't always the case and is often a knee-jerk reaction to be seen having the full spectrum of solutions.

    Look forward to the rest of the series, I think this is certainly a popular topic at present so I'm keen to read more on your take.

    Great content, found this very useful.

  2. Lawrence Dubov
    18 December 2009 • 9:35 am

    Dylan,

    Thank you for your comment and kind words about the blog.

    From my perspective these two disciplines overlap. MDM focuses on data the quality of master data and does it very efficiently through a number of new methodologies and technologies emerged over the last decade, data hubs being part of it.

    By attacking master data quality (MDQ) first many enterprises have found an efficient way to solve key enterprise data quality problems. That said MDM does not focus on other data quality issues that are not under the umbrella of master data. Thus, MDQ is the highest priority part of more generic data quality conundrum.

    That said, I am not saying that data MDM is just a part of data quality. A typical MDM implementation includes other areas such as master data visibility, security, eligibility, data stewardship and governance around master data, etc. These areas are beyond data quality.

    I hope this explains why I think that MDM and DQ overlap and one is not just a part of the other.

    You can contact me directly if you wish to discuss further

    Thanks,
    Larry
    ldubov@initiate.com

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