The Gartner 2015 Magic Quadrant for Master Data Management of Customer Data Solutions is out. One way of getting the report without being a Gartner customer is through this link on the Informatica site.
Successful providers of Mater Data Management (MDM) solutions will sooner or later need to offer ways of connecting MDM with big data.
In the Customer MDM quadrant Gartner, without mentioning if this relates to customer MDM only or multi-Domain MDM in general, mentions two ways of connecting MDM with big data:
- Capabilities to perform MDM functions directly against copies of big data sources such as social network data copied into a Hadoop environment. Gartner have found that there have been very few successful attempts (from a business value perspective) to implement this use case, mostly as a result of an inability to perform governance on the big datasets in question.
- Capabilities to link traditionally structured master data against those sources. Gartner have found that this use case is also sparse, but more common and more readily able to prove value. This use case is also gaining some traction with other types of unstructured data, such as content, audio and video.
My take is that these ways applies to the other MDM domains (supplier, product, location, asset …) as well – just as I think Gartner sooner or later will need to make only one MDM quadrant as pondered in the post called The second part of the Multi-Domain MDM Magic Quadrant is out.
Also I think the ability to perform governance on big datasets is key. In fact, in my eyes master data will tend to be more externally generated and maintained, just like big data usually is. This will change our ways of doing information governance as discussed in my previous post on this blog. This post was by the way inspired by the Gartner product MDM person. The post is called MDM and SCM: Inside and outside the corporate walls.
Liliendahl on Data Quality 
Despite the hype in the market, I continue to see the Big Data use case as exactly that: hype. Trying to apply tools that are built to master data directly to what is inherently transactional in nature is not going to be successful. The link case makes much more sense. The link use case is similar in nature to pulling opportunities from CRM or assets from a service system. But I think the MDM system should be relying on the Big Data source to properly identify the data, similar to finding the foreign key. The good news is that Big Data systems are built to respond quickly to exactly this type of query.
At Semarchy we’re firmly in the link category. We’ve written before about MDM being foundational for big data.
And I certainly agree with you that these possibilities apply to other MDM domains (and RDM domains!) Having well-managed location, supplier, asset, or customer data can only benefit your big data initiatives.
Hi Henrik,
Great blog as always. Thank you.
I am in favor of both the approaches you and Gartner mentioned here. I do believe that every use case around mastering + big data should revolve around the business value just like it is in good old, plane MDM initiatives. To that front, I’m a huge supporter of ‘technology adopting to the business need’ (Versus the other way). It’s only a matter of time before we see more and more companies adopt big data mastering & big data governance. I’m already seeing this trend.
There is a third way as well, which is about offloading some of the processing we do in traditional MDM into Hadoop to accommodate large data volumes in shorter time. Specifically in scenarios such as m&a, new source system on-boarding. I will write more on this in a separate blog. I would love to hear what you are seeing in the market.
Thanks
-Prash
@MDMGeek
Thanks Gino, Matthew and Prash for adding in. As I have seen it the MDM vendors tackles the raise of big data in very different ways. Some are doing it the R&D way by actually releasing services. Some are doing it the marketing way by talking about big data without really offering anything new. It is new frontiers and we still have to see what goes and what doesn’t go. But we won’t find out unless we try.
As usual, your insights are spot on. Completely agree that pushing the envelope is the way to go as the effort of doing so is almost always worth at least the knowledge gained. And learning how to exploit big data in the context of master data will be profitable for all. My point above was that master data is different than big data the same way that master data is different than transactional data. And big data has almost the same characteristics as transactional data. So master data techniques don’t usually apply. But making them work together will be an exciting learning process for all of us.
Out of interest, here’s a link to the article referred to earlier in the thread.
https://www.semarchy.com/semarchy-blog/mdm-is-foundational-for-big-data/
Thanks Gino and Richard for keeping the ball rolling. Indeed the triangle of master data, transaction data and big data will be a playground in the future.