My [MC]2 2014 Manufacturing and Scaling Big Data Presentation

Yesterday afternoon, I gave a 45 minute presentation on Manufacturing and Scaling Big Data at [MC]2 2014.  Here are just a couple of my slides where I was sharing some of the laws and numbers around Big Data as well as the summary.



100,000 Libraries of Congress 

•The amount of printed material at the Library of Congress is 10TB

–A petabyte is 1,000 TB

•An exabyte (EB)

–The prefix exa means one billion billion, or one quintillion

–1,152,921,504,606,846,976 bytes

–1,000 petabytes, or a million TBs or a billion gigabytes

–67 million iPhones of data

•It is rumored that NSA has 3 to 12 Exabytes at their new facility in Bluffdale, Utah

•New large data set tools, like hadoop, have replaced yesterday’s tools, and new tools will be created to deal with tomorrow’s even larger data sets


340,282,366,920,938,463,463,374,607,431,768,211,456  

•That’s 340 undecillion

•aka IPv6 addressing – 128 bit addresses

•If we took every single atom on planet Earth, we would be able to give each and every atom 100 IPv6 addresses

•IPv4 was too small at 4,294,967,296

•That’s 4 billion

•That’s 32 bits

•IPv6 is the foundation for manufacturing and scaling big data

  O'Dell's Law  

•Scaling is ALWAYS THE problem

•If you’re not afraid, you simply do NOT understand

  Groundwater's Law 

•/* You are not expected to understand this */

•Everything you know is wrong

•How do the little electrons know?

–Monster cables versus Home Depot wire

•Do the math

–Sun Net Manager’s Two Questions

•Can be summarized as, “stop, and think through this problem.”

Edstrom's MTConnect Law

•The value of any manufacturing network is the number of MTConnect enabled systems plus the number of software systems that are integrated with that MTConnect data squared

•[MTConnect + Integrated Software ]2

Below is the summary of my presentation:

• Manufacturing means accessing and creating the RIGHT data and the right metadata  

• Scaling means using the right metrics and algorithms to separate the signals from all the noise

• It’s not the size of your data, it’s what you do with it.