There is a lot of hype (as you know) related to Artificial Intelligence (AI), machine learning and specifically Deep learning (complex neural networks). You also know (if you have been keeping up with the news) that we are all users of such techniques in many every day tools. But recently the technology has gotten a little too close for comfort.
Some vendors in the data space, specifically focused on data quality, MDM and data management have started talking about how Deep learning will change the use of those tools significantly. At this point, I am not so sure. I think there is great promise but, as with many technologies, we need to be clear how we plan to use them,
For example, deep learning might help us discover where our master data is kept. Finding where our master data is, embedded copies all over the place inside and between business systems in a complex landscape of on-premises and cloud apps is a hard task. Deep learning might be able to “spot” where the most frequently referenced data reside (much as the famous cats were “discovered” in the YouTube experiments).
This same concept is what sits at the heart of tools (think of IBM’s Watson) that sifts through diagnosis or recipes and concertos as they break constituent elements down and “discover” (really, it’s a form of classification) each one. But does this change MDM?
We have had access to semantic discovery tools for years. But finding where our master data exists is not equal to MDM – it is just part of the overall set of tasks needed to sustain MDM. In fact, there are two other tasks (among many others) that are much different and we don’t need, and cannot use, deep learning. The first task is “what is your master data” and the second concerns the enforcement of the policies that sustain it.
