Kurt is the founder and CEO of Semantical, LLC, a consulting company focusing on enterprise data hubs, metadata management, semantics, and NoSQL systems. He has developed large scale information and data governance strategies for Fortune 500 companies in the health care/insurance sector, media and entertainment, publishing, financial services and logistics arenas, as well as for government agencies in the defense and insurance sector (including the Affordable Care Act). Kurt holds a Bachelor of Science in Physics from the University of Illinois at Urbana–Champaign.
I'm going to put my geek hat on for a bit. Over the course of the last couple of months I've been exploring semantic modeling from the standpoint of "context-free" design - where I've been looking for patterns that seem to hold true regardless of what the data topic itself. One pattern that I feel comfortable now identifying is "The Rule of One", or put another way "Ted Codd was right".
Machine Learning is supplanting both Big Data and Data Scientist as marketing buzzwords, and as is typical, as it gains in popularity it also loses whatever more precise meaning that it had before. People tend to talk about machine learning without necessarily knowing precisely is being described, and see it as its own whole, separate discipline rather than being simply one aspect of a complex graph of related technologies.
During the day, I spend a lot of time researching online. I may be looking for coding tips on articles I'm writing for my programming columns. I may see something on Facebook or LinkedIn that I'd like to reference for articles on futurist issues, or politics, or industry trends. I may come across a reference image or detailed information about a place for the novels that I write. Anyone who writes regularly likely also spends a significant amount of time finding and compiling such resources.
An interesting pattern emerges when you throw a potentially controversial idea out, as I did recently with the article below. I had written that I think that we need to strengthen our "local" networks - energy and information both - by utilizing more of a mesh approach, precisely because the existing (largely centralized) networks are too vulnerable to storms, earthquakes and cascading power failures.