Year: 2017

The Internet of Things (IoT) promises to change everything by enabling “smart” environments (homes, cities, hospitals, schools, stores, etc.) and smart products (cars, trucks, airplanes, trains, wind turbines, lawnmowers, etc.). The article discusses the importance of moving beyond just collecting the data, to transitioning to leveraging this new wealth of IoT data to improve the decisions that these smart environments and products need to make: to help these environments and products to self-monitor, self-diagnose and eventually, self-direct.
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Chances are your company is swimming in data. From handwritten notes to computer-generated information, you have a ton of data that can be used to gain new insights, make important businesses decisions and sometimes even predict the future.
On the surface, this large amount of data is simply a benefit, but it comes with one major caveat: with so much information, it’s often challenging to find a pragmatic way to leverage data for business optimization.
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Quantum computing is now a commercial reality. Here’s the story of the companies that are currently using it in operations and how this will soon disrupt artificial intelligence and deep learning.
Like a magician distracting us with one hand while pulling a fast one with the other Quantum computing has crossed over from research to commercialization almost without us noticing.
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Blockchain or distributed ledger technology revolves around an encoded and distributed database serving as a ledger whereby records regarding transactions are stored. At the core blockchain is an innovative database approach with a data model whereby cryptography (encryption) is utilized in each transaction update and verifications become possible across the specific blockchain network, depending on its goal and stakeholders.
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Traditionally, data professionals and information managers distinguish three types of data: structured, unstructured and, you guessed it, semi-structured. Some also talk about “quasi-structured”, “multi-structured” and “hybrid data” but in this article we’ll keep it simple. Although there are debates about the structure surrounding unstructured data and much more, it’s important to look at what these three types of data essentially mean, where they come from, how they are used and, most of all, how they can generate value. As the title already indicates, unstructured data is our major focus here.
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