The Pros and Cons of Master Data (Management)

As a comment on my LinkedIn status about my previous post Jan van Til asks:

Wouldn’t the world be far better of without the concept of master data? What problems are solved by it? What problems are introduced by it? The balance? So … why do we keep toiling with master data?”

What problems are solved?

A common definition of data quality is that data are fit for the intended purpose of use. However, with master data we have multiple purposes of use of these core data entities. In an enterprise architecture with no focus on master data, the same real world construct will be described in many different databases in many different ways.

This causes a myriad of challenges. There are no one face to our customers, which makes us look stupid. We may offer the same product unintentionally with different prices and with different features, which is stupid. Our reporting, business intelligence, data science will be based on ambiguous data and therefore potentially cause stupid decisions.

If we have data quality issues with no centralized management of master data we will fund data cleansing and prevention initiatives many times for the same real world construct – and probably with varying outcome. In a centralized master data management set up, we can avoid reinventing the wheel as explained in the post The Database versus the Hub.

What problems are introduced?

Implementing Master Data Management (MDM) is not without tears. This involves the whole enterprise, and will increasingly caused by the rise of digitalization involve the business ecosystem as well. You will have to spend money and resources, which are not always easily justified.

MDM is a complex discipline involving many stakeholders. There is a high risk of running over budget and time and missing the goals.

As MDM is the remedy against data silos, you may end up with MDM as just another data silo within your enterprise. And still, your MDM hub may be a data silo in your business ecosystem.

The balance

Think big, start small. Make it agile. A good example of an agile approach to MDM is proposed by the MDM vendor Semarchy as described here on What is Evolutionary MDM™?

While you need a strong inner hub for master data in your enterprise, do not try to impose that hub on everything and everyone as for example examined in the post A Different End-to-End Solution for Product Information Management (PIM).

Multi-Side MDM

4 thoughts on “The Pros and Cons of Master Data (Management)

  1. Michael H (@mphnyc) 10th March 2017 / 22:16

    Thanks for the mention above, Henrik. Just back from the Gartner Data & Analytics Conference in the US, and it was interesting to see how MDM is still very much at the center. So many of the disciplines require easy access to high-quality trusted data. Our Agile approach grew out of an understanding that there was a better way to tackle mastering data across any domain than what was offered by existing mega-vendor solutions.

    Please you picked up on this and hope we can talk more about how we’re innovating with an Intelligent MDM solution going forward.

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