When most people think about metadata, they often recall the embedded fragments of information that help define digital photographs or office documents. While these are useful for identification and classification purposes, they are not the most important use cases for metadata. Metadata’s most useful purpose is to create context for information. And once information has context, it becomes powerful.
What can you do with data with the added context that metadata provides? Michael Schrenk explains how metadata is created and used to gain competitive advantage and demonstrates how he used metadata to better understand if the success of a new online business was built on a bubble or if the business fundamentals were sound.
On the flip side, metadata can also be the source of privacy leaks and data security threats. Michael discusses how to protect yourself from researchers and hackers who uncover trade secrets by combining op sec (operations security) and creative metadata techniques. You’ll also learn how metadata was used to predict troop strength and how a law inadvertently prevented metadata from exposing an entire generation to identity fraud. Most importantly, you’ll discover how to look at data within context of the world where it was collected.
Michael Schrenk has developed software that collects and processes information for some of the biggest news agencies in Europe and leads a competitive intelligence consultancy in Las Vegas, where he consults on information security everywhere from Moscow to Silicon Valley, and most places in between. Mike is the author of Webbots, Spiders, and Screen Scrapers. He has lectured at journalism conferences in Belgium and the Netherlands and has created several weekend data workshops for the Centre for Investigative Journalism in London. Along the way, he’s been interviewed by BBC, the Christian Science Monitor, National Public Radio, and many others. Mike is also an eight-time speaker at the notorious DEF CON hacking conference. He may be best known for building software that over a period of a few months autonomously purchased over $13 million dollars worth of cars by adapting to real-time market conditions.
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