Kurt Cagle Tech Expert

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. 

 

Feast or Famine: What Happens When the Job Ends

As a consultant, I go through periods where the work is just not there. This is not a case of being unemployed, rather, it is rather the default mode of existence for independent contractors who have to simultaneously do the technical work and nonetheless sell their skills.

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Maybe It's Not Agile

I was, once upon a time, a huge proponent of agile. I thought the manifesto made a lot of sense, and it fit with my observation that a small group of skilled, dedicated programmers, working closely with the client, can almost always produce software more efficiently than a large consulting firm. Yet, over time, I've also seen Agile applied at companies with dozens or hundreds of programmers, and if anything the results were worse than that hokey old religion of Waterfall.

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Why GDPR Will Change The World

The General Data Protection Regulation, or GDPR, the European Union's most recent efforts to combat the rise of both "fake news" and identity theft, has come from obscurity to becoming one of the biggest issues that anyone involved in data management has faced in years. The ideas behind it are relatively simple - by unifying a set of requirements on data management, the EU hopes to staunch the abuse of data about people that's collected for one purpose then sold for something very different.

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Give Your Data a Bath: Ingesting SQL as RDF

In the era of Big Data, data quality - or how clean the data is - inevitably floats to the surface. Data scientists typically refer to data as dirty when it has a number of basic flaws:

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The Rule of One

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".

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